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0 | ey | ey-execs-double-down-on-ai-explore-5-ai-adoption-strategies-for-success.pdf | Execs double down
on AI: explore 5 AI
adoption strategies
for success
AI Pulse Survey — July 2024
About the survey
The EY AI Pulse Survey, conducted in May 2024, captures the investment trends
and attitudes toward artificial intelligence (AI) among 500 senior US executives as
they prepare to scale AI technologies in 2024. The survey highlights a significant
projected increase in AI investments, emphasizing the technology’s growing
importance in corporate strategy. The findings suggest that successful AI
adoption requires a holistic approach, including strategic diversified AI investments,
responsible AI practices and workforce upskilling. The insights offered aim to
guide executives in navigating the complexities of AI integration, with strategic
recommendations for those seeking to lead in the AI-driven business landscape.
2 | Execs double down on AI: explore 5 AI adoption strategies for success
Artificial intelligence is redefining the However, despite the forecast investment boom,
competitive business landscape, with our findings also indicate that many leaders are
leaders actively investing to capitalize on ignoring the foundational functions AI needs in
its transformative promise. To investigate order to thrive. Successful AI adoption demands
top-tier investment trends and perceptions more than just technological integration; it’s
in AI technology adoption among corporate about adapting to a new paradigm whereby
leaders, as well as uncover the state of AI in AI reshapes every aspect of the enterprise.
the US, we commissioned a survey among From building a scalable data infrastructure
500 senior executives across a spectrum of to fostering a workforce fluent in emerging
industries. Survey findings indicate a projected technologies, the research emphasizes the
nearly twofold increase in AI investments, need for a holistic approach to AI adoption.
exceeding US$10 million or more in the next As we stand on the cusp of an AI-driven era,
year, among those who are already investing, the message is clear: Those who invest wisely
signaling AI’s shift to a central role in corporate in AI today will be the industry trailblazers
growth strategies. of tomorrow.
This sentiment follows a year in which AI This article illuminates the essential strategies
investments had already significantly increased executives should deploy to navigate the
in pace. Just three years ago, about half of complexities of AI adoption as their investments
senior leaders said their organization spent less increase, including five key takeaways:
than 5% of its total budget on AI investments.
In contrast, today, 88% of those same leaders Adopt diversified AI investment
1
spend 5% or more of their total budget on AI. strategies.
It’s a number that is set to grow even higher, as
half of senior leaders said they would dedicate 2 Prioritize return on investment
25% or more of their total budget toward AI (ROI)-driven AI deployment.
investments in the coming year.
Align your business and AI maturity
3
At the same time, those already doubling roadmap.
down on investments are seeing the impact.
While nearly all are investing in AI, our findings
Invest in responsible AI as a
4
indicate a divergence between companies competitive edge.
experimenting in small ways and those making
larger investments. Senior leaders whose Embrace talent development as a
5
organizations are investing in AI and whose value driver.
current budget for AI investments is 5% or more
of their total budget saw higher rates of positive
return across dimensions surveyed compared
with those who spend less than 5%.
3 | Execs double down on AI: explore 5 AI adoption strategies for success
Leaders should adopt diversified
AI investment strategies
In the search for operational excellency, Figure 1: Leaders should adopt diversified AI
investment strategies
businesses are turning to AI as a transformative
technology. Custom AI development stands
out for its ability to enhance an enterprise’s With 95% of senior leaders
saying their organization is
operations, delivering peak efficiency and
currently investing in AI.
intelligent workflow management tailored to the
intricate needs of the business. Simultaneously,
the allure of pre-built AI technologies lies in 95%
their ability to offer immediate implementation
and a more favorable cost structure. Discerning
businesses should undertake a comprehensive
analysis of their operational requirements,
56%
competitive landscape and long-term goals
to determine the optimal blend of in-house
56%
developed and off-the-shelf AI solutions.
By doing so, they position themselves to
The key investment focus
leverage the full spectrum of AI benefits, lies in balancing custom
The acquisition of AI development.
ensuring a strategic advantage in the rapidly
ready-made AI products.
evolving marketplace.
This strategy allows for tailored solutions where necessary
while also leveraging the speed and cost efficiency of pre-built
AI technologies.
4 | Execs double down on AI: explore 5 AI adoption strategies for success
Prioritize ROI-driven AI deployment
Forward-thinking enterprises are looking to Figure 2: Prioritize ROI-driven AI deployment
AI as a catalyst for business transformation.
Strategic deployment of AI is crucial for firms
aiming to strengthen their performance About a third (34%) of senior
leaders say their organization is
and realize cost efficiencies. By focusing
tracking the impact of AI initiatives
on AI solutions that improve operational fully and at scale.
workflows and enhance employee productivity,
organizations can convert traditional business
34%
models into intelligent, AI-powered operations.
This advancement goes beyond simple task
improvement — it calls for a radical redesign of
77%
business processes to be AI-centric. By doing
so, companies are not just automating; they are
innovating, ensuring that their investments in AI
74%
yield measurable financial returns and solidify
their standing for the future.
The survey shows that
among organizations
investing in AI, those
investments are delivering
Employee positive returns, especially
productivity (74%). in areas like operational
efficiencies (77%).
5 | Execs double down on AI: explore 5 AI adoption strategies for success
33
Align your business and AI
maturity roadmap
Capturing the full potential of AI requires more Figure 3: Align your business and AI maturity roadmap
than just technological investment; it demands a
strategic alignment that integrates AI initiatives About 1/3 (34%) of senior
executives report that their
with the core objectives of the business. A
organization is aligning 34%
robust and well-structured data infrastructure AI strategy with business
objectives fully and at scale.
is critical as it underpins intelligent operations
and aligns with the company’s strategic
A third (36%) of senior
pursuits. This alignment paves the way for
leaders report that their
enhanced decision-making capabilities and organization is investing
36% in data infrastructure (i.e.,
a fertile environment for innovation. By
quality, accessibility and
achieving strategic AI maturity, organizations governance of data) fully
and at scale,
can transition into “superfluid” entities,
characterized by their seamless decision-making
processes and a relentless drive for innovation. and 35% report that their
In this way, a strong data foundation not only organization is creating a 35%
roadmap for AI implementation
supports AI but also propels businesses toward
fully and at scale.
their goals with unprecedented efficiency
and insight.
A superfluid enterprise is a highly agile
and adaptable organization, leveraging
digital innovation to swiftly respond to
market shifts, optimize processes and drive
continuous growth, ensuring sustained
competitive advantage.
6 | Execs double down on AI: explore 5 AI adoption strategies for success
44
Champion responsible AI
as a competitive edge
The surge in executive interest toward Figure 4: Champion responsible AI as a competitive edge
responsible AI marks a pivotal shift in business
strategy, placing ethical considerations at
the forefront of AI adoption. To navigate About a third (34%) of senior
this new terrain, companies should invest in leaders say their organization
is building an AI governance
comprehensive AI governance frameworks and
framework fully and at scale.
strategies for mitigating bias, thereby ensuring
that their AI systems uphold fairness and
transparency. Firms that excel in responsible AI
not only distinguish themselves in a competitive 34%
marketplace but also fortify themselves against
future regulatory issues. In addition, ethical
AI practices are a linchpin in the creation of
32%
a “superfluid” enterprise, where stakeholder
trust is strengthened, compliance is effortlessly 53%
maintained and operational friction is reduced,
all of which propels innovation. Pioneers in this About as many (32%)
senior leaders say their
are setting a new industry standard, providing
organization is addressing
services that are both transparent and With 53% of senior leaders bias in AI models fully and
equitable and charting the course for the future whose organization is at scale.
investing in AI reporting
of AI-powered businesses. There is clear interest
increased organizational
in responsible AI, but
interest in responsible
leaders are not taking the
AI over the past year,
necessary steps to realize
businesses should prioritize
this interest.
ethical considerations in
their AI strategies.
7 | Execs double down on AI: explore 5 AI adoption strategies for success
55
Embrace talent development
as a value driver
The scarcity of AI skills in the job market is a Figure 5: Embrace talent development as a value driver
clarion call for businesses to invest in extensive
employee upskilling programs. By cultivating AI
There is clearly a gap and talent is
skills within their existing workforce, companies hard to find, but only 4 in 10 (40%)
senior leaders are encouraging
can not only expedite the adoption of AI Additionally, only 37% of
employees to embrace AI fully and
senior leaders say their
technologies but also secure a vital competitive at scale.
organization is training/
advantage. Developing an internal pipeline of AI upskilling employees on
AI fully and at scale.
talent is essential for fostering a workforce that
is not just proficient but superfluid — adaptable, 40%
innovative and fully equipped to leverage AI
37%
for maximum impact. Moreover, by placing a
premium on attracting and nurturing AI-savvy
employees, organizations can establish that 83%
their operations are driven by professionals that
can unlock the full spectrum of AI’s capabilities, 78%
positioning the business at the forefront of
technological advancement.
With 83% of senior
leaders prioritizing
attracting workers who The difficulty in finding
are knowledgeable of AI, employees with the
businesses must recognize AI skill set needed for
the importance of building their organization (78%)
an AI-competent workforce. underscores the need for
comprehensive upskilling
programs.
8 | Execs double down on AI: explore 5 AI adoption strategies for success
Conclusion
The burgeoning influence of AI on the business landscape is undeniable, with our survey of 500
senior executives revealing a significant uptick in AI investments. This is not merely a trend but a
strategic imperative; companies that do not actively engage with AI risk being left behind in a market
that increasingly rewards innovation and agility. As we have seen, the future belongs to those who
recognize AI’s potential to redefine every facet of their operations — from process improvement to
decision-making — and invest accordingly.
A diversified AI investment strategy is paramount. Companies must balance the allure of ready-made
AI technology solutions with the bespoke advantages of custom development to create a hybrid model
that aligns with their unique business needs. This approach enables organizations to harness AI’s
full potential while maintaining flexibility in a dynamic market. In addition, the focus must be on ROI-
driven AI deployment. Investments in AI should not be made for the sake of technology alone; they
must be tied to clear, measurable business outcomes. Organizations that prioritize AI applications
with direct impact on operational efficiency and productivity will not only see immediate benefits but
also set the stage for long-term financial success.
It’s important to note that championing responsible AI is not just an ethical mandate but a competitive
differentiator. As AI becomes more widespread, companies that lead with transparency, fairness and
governance will build trust and resilience, positioning themselves favorably in the eyes of consumers
and regulators alike.
And aligning business and AI maturity roadmaps is crucial. Organizations must verify that their data
infrastructure and AI initiatives are in lockstep with their strategic goals. This synergy will enable
them to make smarter decisions faster and foster an environment ripe for continuous innovation.
Finally, talent development is a critical value driver in the AI equation. The scarcity of AI competencies
necessitates a proactive approach to upskilling and attracting top talent. Companies that build a
robust internal pipeline of AI skills will not only accelerate technology integration but also secure a
lasting competitive edge.
9 | Execs double down on AI: explore 5 AI adoption strategies for success
Methodology
Ernst & Young LLP commissioned a third party to conduct the 2024 EY AI Pulse Survey. The
online survey was conducted among n=500 US-employed decision-makers (SVP+) in the health;
life sciences, energy, technology, media and telecommunications (TMT); government and public
sector; consumer products and retail; advanced manufacturing and mobility (AMM); financial
services; private equity; and real estate, hospitality and construction (RHC) industries (i.e., n=50
per industry). The survey was fielded between April 29 and May 6, 2024. The margin of error
for the total sample is +/- 4 percentage points.
Ernst & Young LLP contacts
Dan Diasio
EY Global Artificial Intelligence Consulting Leader
[email protected]
Traci Gusher
EY Americas Data and Analytics Leader
[email protected]
Samta Kapoor
EY Americas Energy AI and Trusted AI Leader
[email protected]
10 | Execs double down on AI: explore 5 AI adoption strategies for success
EY | Building a better working world
EY exists to build a better working world, helping
to create long-term value for clients, people and
society and build trust in the capital markets.
Enabled by data and technology, diverse EY
teams in over 150 countries provide trust
through assurance and help clients grow, transform
and operate.
Working across assurance, consulting, law,
strategy, tax and transactions, EY teams ask better
questions to find new answers for the complex
issues facing our world today.
EY refers to the global organization, and may refer to one or more, of the
member firms of Ernst & Young Global Limited, each of which is a separate
legal entity. Ernst & Young Global Limited, a UK company limited by guarantee,
does not provide services to clients. Information about how EY collects and
uses personal data and a description of the rights individuals have under data
protection legislation are available via ey.com/privacy. EY member firms do not
practice law where prohibited by local laws. For more information about our
organization, please visit ey.com.
Ernst & Young LLP is a client-serving member firm of
Ernst & Young Global Limited operating in the US.
© 2024 Ernst & Young LLP.
All Rights Reserved.
2406-4556497
ED None
This material has been prepared for general informational purposes only and is not intended to be relied
upon as accounting, tax, legal or other professional advice. Please refer to your advisors for specific
advice.
ey.com |
1 | ey | ey-ukc-short-report-ai-and-productivity.pdf | How can AI
augment your
people to realise
their full potential?
Contents
Chapter One: Setting the scene 1
What’s the value of AI innovation? 1
What tasks will AI augment? 2
Chapter two: How can AI enhance productivity 3
for UK business?
What magnitude of productivity savings could AI bring 3
to the UK economy?
Benefits of AI adoption for the workforce 4
What are the potential risks? 4
How can organisations retain and protect female talent? 4
Chapter three: AI and the UK regions 5
Where does this leave the regions? 6
Chapter four: Balancing rapid adoption with 8
ethical innovation
Cultural and operational risks 8
How can you create the right environment for AI innovation? 8
Chapter five: Create the right conditions 9
for enhanced productivity
Contacts
Catriona Campbell
Client Technology & Innovation Officer, Ernst & Young LLP
https://www.ey.com/en_uk/people/catriona-campbell
Harvey Lewis
Partner, Client Technology & Innovation, Ernst & Young LLP
https://www.ey.com/en_uk/people/harvey-lewis
Sofia Ihsan
EY Global Responsible AI, Consulting Leader, Ernst & Young LLP
https://www.ey.com/en_uk/people/sofia-ihsan
Chapter one
Chapter one:
Setting the scene
Policymakers worldwide recognise that artificial How is AI impacting
intelligence (AI) has the potential to drive enormous gains productivity?
in productivity and growth, with forecasts suggesting a
Implemented effectively,
contribution of between $13 trillion and $15.7 trillion to
AI could effectively add
the global economy by 2030.1 9.8 million
workers to the
In findings published by EY and Liberty Global in the report, Wired for AI,2 from a UK workforce
labour market perspective, 50% of jobs in the US, EU, UK and Switzerland could
be complemented by AI because the latest technology can help people become Create additional
more efficient in at least half of their tasks. productivity
equivalent to
As the UK economy takes its first tentative steps towards a more buoyant $7 trillion
economic outlook in 2024, AI has the potential to accelerate economic recovery,
in wages
thanks to the vast productivity and efficiency gains on offer to reinvigorate GDP,3
arising from AI’s ability to enhance work output and quality.
46%
Indeed, assuming the maximum potential efficiency gains for all workers, the of UK
total additional ‘productive capacity’ that could be unlocked within the combined jobs could be
complemented by AI
economies of the US, UK and Europe by AI is equivalent to 124 million workers:
around 62 million in Europe, 51 million in the US, 9.8 million in the UK and
1.4 million in Switzerland. The total value of this additional productive capacity
equates to approximately $7 trillion in yearly wages.
What’s the value of AI innovation?
Around 400 million people are employed across the US, EU, UK and Switzerland.
Of that figure, EY and Liberty Global analysis4 suggests that 50% of these jobs
could be complemented by AI because the technology can help people become
more efficient in at least half of their tasks. This means there are benefits on
offer for the majority of businesses in all sectors and markets. Put into the
context of workforce output, this acceleration of productivity is equal to adding
124 million more workers into the economies of the US, EU, UK and Switzerland.
Our results are consistent with estimates published by the IMF, who suggest that
60% of jobs in advanced economies could be impacted by AI, 40% in emerging
markets and 26% in low-income countries.5 The research suggests that AI could
have the greatest impact in Luxembourg, where nearly 56% of jobs could be
complemented; in the UK, that figure currently stands at 46%. With the impact of
AI set to target such a significant proportion of the UK workforce, understanding
its true benefit is crucial.
1
Chapter one
Percentage of jobs that can be complimented by AI
Figure 1. Percentage of jobs that can be complemented by AI and GenAI in the US, UK, Switzerland and
individual countries in Europe, showing the contribution from highly network dependent jobs
Source: EY and Liberty Global21
What tasks will AI augment?
As a general-purpose technology, AI’s principal impacts are likely to be felt
in improved efficiency and new business models across industries, providing
opportunities for business transformation and job creation. In the US, EY estimates
that generative AI (GenAI) is set to provide a substantial lift to productivity, likely
delivering a boost worth $650 billion over the next decade and lifting real GDP by
nearly 2.5% by 2033.6 Moreover, Goldman Sachs indicates that further progress
in the field of GenAI could add an extra $7 trillion to global output over the next
decade, as innovative tools like ChatGPT become increasingly woven into the fabric
of business and society.7
With the World Economic Forum predicting that 44% of roles will be disrupted
in the next five years, there is, of course, a fear that AI will displace workers in
sectors which are unable to adapt quickly.8 Yet, this is not the only possible future.
As economists Erik Brynjolfsson and Gabriel Unger suggest, “There is a scenario
in which AI leads to a higher-productivity-growth future. AI might be applied
to a substantial share of the tasks done by most workers and massively boost
productivity in those tasks.” 9
This report will explore where AI will have the biggest impact, how business leaders
can prepare their workforce for the new reality of AI augmented work, and the
regulatory and ethical watch outs — particularly when it comes to supporting women
in the workplace — that businesses should be wary of to ensure AI innovation does
not eclipse the needs of the workforce.
Responsible quantum computing for everyone | 22
Chapter two
Chapter two:
How can AI enhance
productivity for UK business?
In 2024, business leaders and policymakers alike will need
to address urgent issues in the labour market, namely the
number of people in work and the skill levels across the
workforce. As we enter a new epoch of technology innovation
in our workplaces, businesses will need more widespread tech
skill than ever before.
With AI presenting opportunities for net gains in employment figures, business
leaders and policy makers would be wise to focus on developing AI skills amongst
the existing workforce to prevent employee loss and provide opportunities for those
out of work to access AI skills courses. This can expedite their return to work, future
proof their skills and prevent unnecessary delays to AI innovation.
Addressing inactivity by encouraging people back into work through the creation of
AI-related roles could help contribute to closing some of the disparities in regional
growth performance. In a study conducted by the Institute for the Future of Work,
although 47% of respondents said AI and automation had eliminated positions within
their company, almost 67% reported the technology had created new positions.10
What magnitude of productivity savings could AI bring to the UK economy?
An accurate assessment of AI’s potential effect on productivity is difficult to
establish because it is a broad and rapidly evolving field. Considerable uncertainty
also remains about how it will be adopted by people and integrated into established
business processes. However, there are ways to assess its impact on the workforce
by considering how it can help people to carry out tasks more efficiently.
For organisations looking to retain top talent in a sluggish talent market, AI
innovation can be instrumental in improving workforce conditions, particularly in a
marketplace where budgets for learning and development are contracting; findings
from the recent EY CEO Outlook survey11 indicate 96% of UK leaders are considering
restructures or hiring freezes, a reduced focus on learning and development and
a move from permanent to contract workers. By prioritising opportunities for AI
innovation in areas where the technology can help to reduce the workload, free up
workers to enjoy more skillful work and embrace a better work-life balance; AI could
be the vehicle needed to redress potential workforce issues before firmer tactics,
such as redundancy, are adopted.
3
Chapter two
What are the potential risks?
Despite the evident benefits of integrating AI into the everyday lives of the
workforce, it’s essential that organisations remain aware of the potential damage AI
can have on diversity and inclusion, particularly in how it impacts women or lower
skilled workers.
In findings published by McKinsey Global12, the industries expected to shrink
as a direct result of AI automation include food services, customer service and
sales, and office support- all industries in which women are disproportionately
overrepresented. In the UK, women account for 53% of workers in food service13,
and over 60% of total workers across the service and administrative sectors.
Seniority also adds to the burden placed on women; there’s well established evidence
that women hold more lower paying jobs than men: currently only 41% of managerial
roles are held by women, this figure decreases to 38%14 when looking at senior
business leading positions. And with AI poised to drive operational efficiencies that
reduce administrative and repetitive workstreams, those in more junior positions-
who are predominantly women — also stand to be more affected.
How can organisations retain and protect female talent?
Considering AI’s potential to unduly damage the careers of women, it’s essential that
workplaces invest in comprehensive training and development programmes to upskill
workers and provide opportunities for growth into roles that are augmented by AI
rather than subsumed by it. By fostering a culture of continuous learning, companies
can ensure that all individuals remain at the forefront of AI advancements. As a
result, businesses can reduce potential layoffs and the exacerbation of skill gaps that
prevent employees from advancing, therefore avoiding an abyss of talent with those
with AI skills on one side, and those without stranded on the other.
Despite the gloomy outlook in some sectors, there will also real opportunities for
women thanks to AI innovation. McKinsey Global15 reports that in spite of real
challenges to workplace equality, AI innovation will generate a demand for work and
workers, which will only increase as economies grow, facilitated by AI: by 2030, the
same research indicates there will be a 17% increase in women’s jobs gained in the
UK as a result of AI. Men will also experience the same uplift.
Benefits of AI adoption for the workforce:
• Achieve an improved work- • Improve delivery times for
life balance, which may work, without an increase in
reduce attrition and work- stress or demand for overtime.
related stress. • Introduce lower skilled
• Perform other meaningful individuals to the workforce,
work, which increases output thanks to AI tools being able
quality or enhances value. to take the strain of more
• Spend more time with their complicated tasks.
clients, which increases client • Reducing burden of repetitive
satisfaction and may lead to or administrative tasks.
growth in future sales.
• Foster innovation because
creative thinking requires time.
• Increase work quality since
they have more time for
4
each task.
Chapter three
Chapter three:
AI and the UK regions
Despite AI’s potential to generate huge opportunities for the
UK — it has already delivered £3.7bn in gross revenue and
created 54,000 jobs — a staggering 75% of all AI activity is
taking place in just three regions: London, the South East
and the East of England, according to the Department for
Science, Innovation & Technology, leaving other UK regions
vulnerable to slower economic recovery and less opportunity
for productivity enhancement.16 Whilst ‘the golden triangle’ is
generating value for the UK economy, activity across the rest
of the UK is sluggish, particularly in the North, Midlands and
South West.
In the recently published EY Regional Economic Forecast17, it was found that London
and the southern regions of the UK are expected to lead the economic recovery,
thanks to a still strong labour market, a recovery in consumer spending, and robust
growth expected in information and communication, professional services, and a
recovering retail sector.
London’s success continues to be driven by the distinctiveness of its economy,
which is characterised by knowledge-based sectors such as professional services,
information and communication, and the concentration of high-skilled workers in
these sectors. AI is undoubtedly supporting that surge in economic recovery.
The UK Sectors Most Impacted by AI
Finance, IT and professional services will be most impacted by AI
Finance & insurance Information & Professional, Property Public Education
communication scientific & administration
technical & defence
Source: Department for Education, Unit for Future Skills, the impact of AI on UK jobs and training report
5
Chapter three
In research undertaken by the Department for Education18, London was the city
identified as most likely to the experience the earliest impacts and benefits of AI
innovation due to its high concentration of roles in these sectors. When considered
alongside the fact that 75% of AI focussed organisations are based in London, and
the density of professions that will be most quickly and intensely impacted by AI, it’s
unsurprising that London is currently at the forefront of AI innovation and is able to
realise its benefits before other UK regions.
Where does this leave the regions?
Whilst AI has the potential to exacerbate existing regional inequalities due to
disparities in AI preparedness, harnessing the technology could galvanise economic
growth across the regions, and help upskill workers cross-sector. But it will
take investment.
A report published by the Institute for the Future for Work19, expounds the
importance of developing workforce skills rather than focussing on AI alone,
emphasising that training and upskilling will have the biggest impact on regions
currently displaying the lowest levels of AI preparedness. The report says:
“Investments in training, complemented by the sharing of information about new
technologies, consultation on technology adoption, and an orientation towards
empowerment and autonomy, are expected to influence whether new technologies
have a positive or negative impact on work and workers. First, a highly skilled
workforce will be more likely to understand the need for the new technology, its
technical aspects, and its benefits, and feel less threatened by it (as noted by the
OECD in 2023), this will facilitate approaches to AI adoption in which labour is
complemented by technology.”
By upskilling workers, and providing opportunities for their personal development,
the fast-moving organisations can successfully use these technologies, and by
preparing the workforce first, and at scale, regional leaders can better enhance the
overall preparedness of their regions.
Nurturing high-value sectors can boost resilience in tough times and
accelerate growth in better years, but doing so requires regions to
build their own tailored growth plans that consider which industries
are set to flourish and how to cultivate them locally. High-value
sectors will require a high-value workforce, so building in-demand
skillsets and competencies with latest technology should help a
region attract investment while bolstering the local economy. For
example, Generative AI has the potential to enhance regional and
UK productivity rates, but will require a shift in skills to ensure the
workforce can collaborate with and complement the technology.
Rohan Malik, EY UK&I Managing Partner for Government & Infrastructure, EY UK regional economic forecast.20
66
Chapter three
Despite the south dominating AI growth, EY research projects that, between 2024-
2027, Manchester will experience the greatest Gross Value Add (GVA) growth at
2.2% compared to any other city in the UK, including London (0.6%)21. Whilst the
northern city undoubtedly lags behind the ‘golden triangle’ when it comes to AI,
economic value in Manchester is accelerating faster than any other, as investment
pours in. With initiatives such as the newly established AI Foundry helping SMEs
in the area make headway with AI innovation, and investment in AI research at
Manchester University increasing — this February the university received £12 million
in funding for AI research22 — it’s only a matter of time until this vibrant city closes
the gap on its southern counterparts.
Whilst there’s obvious work to be done to help the North embrace
all that AI has to offer, Manchester is undoubtedly benefiting from
a boost in GVA, that other cities across the country can’t rival.
With more spending power to invest in emerging technologies, it’s
essential that cities in the north embrace AI to help continue this
positive outlook, and to close the gap on the ‘golden triangle’.
Stephen Church, EY UK&I North Markets Leader & Manchester Office Managing Partner
77
Chapter four
Chapter four:
Balancing rapid adoption
with ethical innovation
Cultural and operational risks
The rapid evolution of AI technologies poses its own set of challenges. As AI technologies
advance, keeping up with the latest developments and understanding which innovations
are most applicable to individual businesses and sectors becomes increasingly complex.
This rapid progression can lead to a misalignment between AI capabilities and business
needs, potentially resulting in investments in technologies that are either outdated shortly
after implementation or do not deliver the expected value.
The fundamental challenge of business adoption lies not just in the successful
implementation of AI pilots and projects but also in cultivating an environment where
innovation is nurtured, and the workforce is ready and prepared to adapt alongside
these advancements.
Companies often struggle with rigid organisational and cultural structures that can
significantly impede the speed and success of AI implementations. Such structures
typically foster siloed departments and a resistance to change, making it challenging to
embrace the collaborative and agile methodologies required for effective AI integration.
Centralised approaches, while offering streamlined decision-making, may lack the
flexibility and localised insights necessary for innovative AI solutions. Conversely,
federated structures can encourage autonomy and innovation at the departmental level
but may suffer from a lack of cohesion and unified risk management or strategic direction.
To support both workplaces and the workforce towards embracing AI innovation in their
day-to-day work, creating the right environment for innovation will be crucial.
How can you create the right environment for AI innovation?
There is a critical need for clear and robust guidelines on the ethical use
of AI in the workplace, both in how the workforce interact with AI and how
1 Provide clients’ and consumer data is treated. Policymakers must formulate and
guardrails for enforce regulations like the EU’s AI Act, while businesses should establish
AI’s use comprehensive governance frameworks. This will ensure that AI is used
responsibly, with a focus on data privacy, fairness and transparency.
Companies should prioritise organisational agility to adapt swiftly to the
Nurture
2 changing AI landscape and to nurture curiosity that cuts across teams and
curiosity and
functions. Emphasising flexible, collaborative work environments and a culture
increase agility of continuous innovation will be key. This approach will enable businesses to
respond effectively to new AI advancements and market demands.
Keep As AI transforms the UK workforce, targeted investment in skill development
3 upskilling at and workforce training is imperative. Businesses should focus on equipping
the heart of AI their employees with the necessary skills to navigate and leverage AI
transformation technologies. Policymakers can support this initiative by providing incentives and
frameworks for continual learning and skill enhancement in the AI field.
8
Chapter five
Chapter five:
Create the right conditions
for enhanced productivity
As AI transforms the UK workplace, targeted investment in skills development and
workforce training is imperative to realise the 46% productivity growth potential on
offer for the UK.23
To rise to the challenge, companies should focus on equipping their employees with
the necessary skills to navigate and leverage AI technologies. Businesses can do this
by creating the necessary guardrails to create business environments in which AI can
be used safely whilst still promoting greater creativity and efficiency.
Policymakers can support business leaders by providing incentives and frameworks
for continual learning and skill enhancement in the AI field, that not only incentivise
individual businesses but help accelerate both productivity and output across the
whole of the United Kingdom.
The productivity boosts that are enabled will be significant for both the UK’s
economy and skills market as more workers upskill in preparation for a future
of work enabled by AI. But capitalising on AI’s full potential demands targeted
investment across the whole of the UK, cultivating new skills and strategic
organisational realignment. In essence, the UK is primed for a new digital
transformation, but leaders must prioritise developing AI skills to realise the promise
of a more productive future that benefits everyone.
Questions for those charged with leading AI innovation:
• What opportunities am I providing the workforce to upskill in AI technology?
• Do I have the right guardrails in place to guide AI innovation?
• Where is the potential in my organisation to use AI for greater impact?
• What does success look like for my organisation?
Acknowledgements
This report was written by Dr Harvey Lewis, Partner at EY and Catherine Jones from the Ernst
& Young LLP, Brand, Marketing and Communications team with support from EY ITEM club.
Some of the content in this report has been taken from the Wired for AI report, written in
conjunction with Liberty Global, published in February 2024.
The proprietary EY sourced analysis in this report was undertaken by Dr Harvey Lewis
and Timea Ivacson, Manager, from the Ernst & Young LLP, Data and AI team. Additional
contributions were gratefully received from Gareth Shier, Director in the Ernst & Young LLP
Econometrics and Modelling team, Sofia Ihsan, Director in the Ernst & Young LLP, Technology
Risk team, and Dr Ansgar Koene, Director and Global Leader for AI Ethics and Regulation.
The report has been designed by the Ernst & Young LLP, Creative Services Group.
9
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work-transitions-in-the-age-of-automation, accessed 23 April
10
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2 | ey | ey-european-ai-barometer-2024.pdf | From challenges
to opportunities:
How EY and AI work
hand in hand
EY European AI Barometer
June 2024
From challenges to opportunities: How EY and AI work hand in hand
Contents
3
Introduction
1 4
The AI revolution
2 6
Adoption
3 8
Benefits
4 10
Impact on workforce
5 14
Capability building
6 18
Future of AI technology
7 19
Use cases
22
Study design
23
Contacts
2
From challenges to opportunities: How EY and AI work hand in hand
Introduction
In a world shaped by relentless technological progress, companies that fail
to evolve could disappear from the map. Artificial intelligence (AI) is sweeping
through the business landscape with fierce intensity – reshaping industries
and economies at an unprecedented pace. As AI advances to the forefront
of technological innovation, some brace themselves for the inevitable
challenges a step change of this magnitude will bring, while others fervently
seek to unlock the immense opportunities it promises.
Irrespective of AI technology’s many facets and manifestations, one thing
is certain: it will fundamentally redefine the way we work, the way we live and the
way we interact. Businesses need to give careful consideration to some bedrock
questions: should they embrace AI without reservation or proceed with caution?
What pitfalls and paybacks can they expect? How will AI impact the world of work?
And what regulatory frameworks do companies need to observe and how?
Nobody can lay claim to having all the answers in this rapidly evolving new
reality. But since the inception of the AI revolution, our EY teams have stepped
up to the challenge and have been helping clients chart their course for successful
transformation, pinpointing where they can best invest their resources
to extract value from AI for their respective businesses – and creating a better
working world for all stakeholders in the process.
This report seeks to share our experience with, and shed light on, the
multifaceted impact of AI in its many manifestations, examining how managers
and non-executive employees view the challenges and opportunities ahead.
We deep-dive into different sectors to understand the latest approaches to
harnessing the power of AI, with a particular focus on Europe, while maintaining
a global perspective. Our overarching goal is to unlock AI’s potential to create
positive impact in our economies and our communities, advocating for
a responsible, people-centered approach that prioritizes value
creation for everybody.
3
From challenges to opportunities: How EY and AI work hand in hand
1.
The AI revolution
Unlike past technological revolutions that largely involved adequately equipped by their employers to tackle the AI
the automation of manual labor, AI marks a paradigm shift transformation process with suitable training? Are they
in its focus on assisting and automating complex cognitive seeing a meaningful evolution of their job profiles and tasks?
functions, with unavoidable consequences for knowledge
workers. Entire industries and all manner of professions Around the world, regulators too are shifting their attention
are on the cusp of profound change. toward AI and its implications for the economy and for
society more broadly. Government commissions and tasks
AI has the potential to enhance workers’ efficiency and forces are investigating the likely impact on all sectors, from
unearth productivity gains throughout the economy. Our healthcare and financial services through to transportation,
AI survey of multidisciplinary professionals across levels, as they seek to address a host of concerns: citizens’ privacy,
sectors – and across Europe – already provides insights bias in algorithms, job displacement – the list goes on.
of the depth and scale of the AI-driven productivity In May 2024, for instance, the EU introduced the Artificial
boost in major economies, and an indication of its potential Intelligence Act (AIA), which aims to regulate the
contribution to the global economy. That said, unlocking development and use of AI systems within its borders,
productivity gains that AI promises will likely take time, to protect the safety, security and fundamental rights
effort, and wise strategy. of its people. To navigate the global regulatory complexity
that is rapidly emerging, companies will need to designate
One use case we are already seeing gaining traction is the compliance responsibilities for AI deployment and
deployment of AI as a powerful knowledge tool. By distilling use across their entire organization, not just
key insights from vast volumes of data, AI is already helping technical departments.
businesses and their teams make more accurate and in turn
better decisions. Forward-thinking executives are wasting Notwithstanding all the advancements promised, one thing
no time and have set to work exploring how AI can empower can never change: humans need to remain at the heart of AI
informed decision-making, while breaking down silos and development. Moving forward, the emphasis must be on AI
allowing more voices to be heared than ever before. But how empowering employees across all industries to work smarter,
exactly is Europe faring in the AI revolution? Do employees better and more efficiently. It’s not just a question of bringing
here feel that they are a part of the change and are to market better products and solutions: AI holds the
potential to craft a more sustainable global economy – for
people and for the environment.
AI
4
From challenges to opportunities: How EY and AI work hand in hand
Getting AI right
With a human-centered approach to AI, we help hone
technology to maximize talent, driving efficiency and
Most companies recognize the need to accelerate their AI productivity gains across business functions. EY teams
initiatives to gain a competitive edge. Yet concerns persist of leading multi-disciplinary professionals spanning risk,
as regards the pace of AI adoption and the maturity of strategy, technology, and transformation work hand in hand
solutions. Some caution against overinvesting in tools or use with clients to assist them in an implementation process that
cases likely to become obsolete all too soon. Others question is aligned with their purpose, culture, values, and key
whether the timeline touted by AI visionaries is realistic to stakeholders so that AI drives positive human impact.
unfold a truly transformative impact.
In the following, we gauge the current state of AI in European
There is no shortage of questions, and problems still remain businesses across a range of industries and determine the
unresolved. There is no one-size-fits-all model when it comes level and challenges of adoption, the perceived and captured
to AI. Drawing on the rich experience we have already gained benefits, the impact on the workforce, and approaches
working shoulder-to-shoulder with the clients across a broad to capability building, and cast a glance ahead at the future
spectrum of industries and a huge variety of use cases, we of this rapidly evolving technology.
are convinced that it is possible to create meaningful value
by taking a broad approach to AI and by augmenting people
potential to drive extraordinary outcomes.
Key takeaways
Adoption
Many organizations are still struggling with the operationalization of AI.
Barriers need to be removed; a clear tone from the top is needed.
Benefits
Cost benefits are already evident. The frame needs to be expanded to include
other benefits, including creating more meaningful and attractive work profiles
and hence improved employer branding.
Impact on workforce
AI is certain to have a huge and imminent impact on the workforce across all
sectors and professions. Upskilling is key.
Capability building
Organizations need to accelerate their investment in AI capabilities and make
sure they place their bets on the right technologies in a field that is undergoing
fast-paced innovation and whose future is difficult to predict. Appropriate
training programs are key.
Future of AI technology
AI technology is advancing along many different avenues. Tomorrow’s winners
are already making bold moves today.
5
From challenges to opportunities: How EY and AI work hand in hand
2.
Adoption
Beyond the specifics of where AI can be used, what a lot of decision-
makers want to know is the broader success factors for getting the
most long-term value from implementation. First and foremost, they
want to determine the technological foundations required for AI, with
a strong focus on data and the cloud, and their attention is on making
investments that deliver value. Stakeholders also note the importance
of workforce buy-in and adoption to ensure success at scale, with the
primary focus typically on building employees’ confidence to use AI
to improve their day-to-day efficiency.
The adoption of AI in European businesses continues to Security, accuracy, and explainability are viewed as crucial
evolve, albeit with its fair share of challenges. While the factors on the road to success when implementing AI – more
potential benefits are undeniable, organizations are still so with respect to business viability than ethics. At the top
grappling with operationalization. Barriers to adoption of the agenda are security and privacy, discussed from a
include internal policies, external regulation, and the technical as well as a responsibility angle. Secure internal
complexity of the new technologies. GPT models as well as the need to comply with regulation
(current and future) around data and AI are of high
Although some knowledge workers have been keen to importance to stakeholders and decision-makers.
experiment with AI, others are still reticent. In a recent
survey, EY teams asked members of the workforce from
Hurdles to adoption
all over Europe to share their experience with AI.
Almost three-quarters of all respondents (73%) already have
hands-on experience with AI technologies. Most of them use Organizations face a number of hurdles in the
AI in their private lives (38%), rather than at work (12%). operationalization of AI. For one thing, companies
The remaining respondents (23%) have AI experience themselves often impose restrictive policies on AI use by
in both spheres. employees. In some countries, only a relatively low share
of employees in the EY survey report being permitted by
From a regional perspective, the share of AI early-adopters their employer to use AI applications, most notably in
is highest in Spain (84%), followed by Switzerland (82%) Germany (42%) and Austria (46%). That contrasts markedly
and Italy (77%). On the other end of the scale, early with the situation in Switzerland (73%) and Spain (63%),
adopters are less common in the Netherlands (66%) and where most employees are permitted by their employers
Germany (67%). Men (75%) have experience using AI to use AI applications in their work. The share is likewise
applications more often than women (70%). Differences are relatively high in Portugal (58%), Belgium (57%), Italy (56%),
also evident between ranks, with more than 84% of managers and France (55%), all of which exceed the European average
saying they use or have used AI applications, compared with of 52%.
just 67% of respondents among non-executive employees.
In the following we look at some of the most common Complexity is another issue organizations are grappling
barriers to AI adoption in a work setting. with. Six out of ten respondents (67%) point to the
complexity of the implementation process for AI systems
in their organization. Adding another layer of complexity
6
From challenges to opportunities: How EY and AI work hand in hand
subject to increasing regulatory scrutiny, and Europe “systemic risks”. For instance, low-risk AI such as chatbots
is no exception (see EU AI Act). used in customer service will be subject to few requirements
beyond notifying users that they are interacting with AI.
Another important factor is the tone from the top. In AI intended for high-risk application areas that may impact
Switzerland, most respondents (56%) give their employer health, safety, or fundamental rights of people will have to
a good report card when it comes to the extent to which comply with stricter controls,while some applications areas,
management has a positive attitude as regards making such as subliminal manipulation of vulnerable groups,
progress with AI applications. However, only 5% describe are outright prohibited.
their employer as very open when it comes to implementation.
Switzerland ranks lowest in the category, together To comply with the AI Act, companies will need to clearly
with Germany. assign within their organizations responsibilities for
overseeing AI deployment and compliance. The mandated
The level of adoption of AI varies across Europe, with some responsibility extends beyond technical departments to
countries and sectors embracing it more readily than others. encompass the entire corporate fabric. Non-compliance
However, regardless of geographical location or industry, a exposes companies to severe risks, including heavy penalties
clear tone from the top is essential for successful integration. with maximum fines that even surpass the maximum fines
Leaders need to champion AI initiatives, fostering a culture under the EU’s General Data Protection Regulation (GDPR).
that encourages experimentation and innovation while
Compliance strategy and adaptation
addressing justified concerns about job, displacement,
ethical and legal considerations.
A strategic approach to implementation of AI Act compliance
Regulatory framework
begins with companies identifying gaps in their current
practices and outlining a meticulous plan customized to the
The European Union’s AI Act unifies how AI is regulated specific manifestation of AI deployment in their organizations.
across the single market of the 27 EU member states. It also The approach involves an as-is assessment encompassing
has important extraterritorial implications, as it covers all AI current procedures, employee training levels, and a technical
systems impacting people in the EU, regardless of where understanding of AI solutions, including an exhaustive
these systems are developed or deployed from. The AI Act inventory of the AI solutions deployed in the organization,
aims to standardize the use of AI across all its member what they are used for by whom.
states. Ratified by the European Parliament on 13 March
2024 and approved by the Council of the European Member Given the phased transition period, with enforcement of the
States on 14 May 2024, the act is expected to enter into AI Act prohibitions taking effect within 6 months, obligations
force in June 2024. It introduces a new regulatory for General Purpose AI mode developers starting after 1 year
framework for AI technology focused on the protection of and most of the obligations for high-risk AI applications
safety, security and fundamental rights of people in the EU. coming into force after 2 years, companies must hasten
to adjust their operations and implement the required
Risk and compliance framework
changes in a phased, monitored process. Not only is initial
compliance by the end of the implementation deadline
The AI Act adopts a risk-based approach to compliance critical, it also needs to be accompanied by a sustained
obligations, categorizing AI systems by application areas commitment to adapt to ongoing legislative amendments
and target groups into distinct risk levels. In this tiered and to provide staff with appropriate training at regular
compliance framework most requirements fall upon the intervals. In this way, companies can align with the EU’s
developers and deployers of AI systems classified as goal of ensuring safe AI use without stifling innovation.
“high-risk”, and on general-purpose AI models (including
foundation models and generative AI) deemed to pose
7
From challenges to opportunities: How EY and AI work hand in hand
3.
Benefits
How companies can add value by embedding AI into their products
is a major topic for managers in all sectors. Delivering convenient and
enjoyable experiences, using GenAI to improve chatbots, including
virtual try-on, or current checkout-free stores are prominent examples
that help make AI success stories and progress tangible and visible. In
addition, strategies are often directly linked to revenue generation.
Despite the challenges, the benefits of AI adoption are By function, the use cases in which AI has been
already evident, most notably as measured by cost savings. operationalized vary widely, from streamlining supply chain
However, the narrative surrounding AI benefits needs to be operations to optimizing marketing strategies and enhancing
expanded beyond just financial gains. While cost improvement customer experiences. At present, organizations are seeing
remains a primary driver, AI also enables organizations to the greatest benefits in IT (35%), followed closely by
improve decision-making processes, unlock new revenue marketing (30%) and cybersecurity (27%). Interestingly,
streams, and raise their employer brand value. legal and compliance departments see little scope for
AI implementation at present (see figure 2). That said,
First and foremost, what executives invariably want to know with little more than initial inroads made so far in AI
is their return on AI investment. Across Europe, almost half implementation and operationalization, most eyes are
of managers (45%) say that AI use has allowed them to save still fixed on future iterations of the technology.
costs, increase profits – or both (see figure 1). Measured
by these two criteria, AI deployment to date has been most Aside from cost and efficiency improvements, embracing
successful in Switzerland, where 81% of managers have AI allows businesses to automate repetitive tasks, freeing
had a positive experience with the technology. The share up employees to focus on more strategic and creative
of satisfied managers is also above average in Spain (60%) endeavors. Indeed, most respondents expect artificial
and Italy (58%). On the other hand, respondents intelligence to take over parts of their work (65%), with
in the Netherlands, Austria, and Germany (all 34%) some anticipating that they’ll be handing over some of their
are less impressed. workload to AI in the very near future (14%). If they get
it right, organizations have a tremendous opportunity to
leverage AI to enhance job descriptions. A shift toward
AI
more intellectually stimulating work profiles would not only
improve employee satisfaction but also enhance employer
branding, attracting top talent in a fiercely competitive
labor market. That said, all stakeholders need to address
legitimate concerns about job displacement, an issue we
investigate in the following section.
8
From challenges to opportunities: How EY and AI work hand in hand
Figure 1
?
Has AI already led to cost savings or increased profits within your company?
Switzerland 22% 14% 45% 12% 7%
Spain 20% 21% 19% 26% 14%
Italy 14% 26% 18% 26% 17%
Belgium 17% 17% 13% 38% 15%
France 12% 18% 16% 36% 18%
Portugal 14% 10% 15% 41% 20%
Germany 11% 11% 12% 33% 33%
Austria 12% 12% 10% 32% 34%
Netherlands 8% 12% 14% 38% 28%
Europe West 13% 16% 16% 32% 23%
Yes, we did save costs Yes, we increased revenues Yes, both It is too early to say that No, neither
Figure 2
?
In which area do you think AI can already help improve your business? (up to three answers)
IT 34,5%
Marketing 30,1%
Cyber security 26,6%
Emplyomee support 21,9%
Sales 21,7%
Human ressources 18,7%
Operations 18,7%
Legal/compliance 8,4%
Other 5,1%
I do not think AI can help my business 13,4%
9
From challenges to opportunities: How EY and AI work hand in hand
4.
Impact on workforce
Most of the leading minds in business AI say that employees will
be empowered by the new technology to work smarter and more
effectively. Speed and time savings are emphasized a lot. You often hear
talk of augmenting and freeing up employees – typically in conjunction
with reassurances that AI will not replace them and highlighting how
it will allow them to spend more time on value-added, creative, and
collaborative tasks. While employee efficiency gets the greatest
attention, improvement in other areas is also noted.
As AI technologies continue to advance, they are having Figure 4 shows the general consensus among respondents
an ever-increasing impact on the workforce. Job losses across the nine analyzed European countries, with more than
due to automation are a legitimate concern, particularly one in two (53%) stating that AI applications will influence
in industries with routine, repetitive tasks. However, the their work – or are already doing so. In Italy and Switzerland
broader impact extends beyond displacement, with AI (59% each), the figure is almost six out of ten. The
reshaping job profiles and necessitating new skill sets. proportion is also above average in the Netherlands (57%),
and Austria and Germany (56%). On the other hand, it is
When asked whether the use of AI will lead to job losses, below average in France (47%), Belgium (48%), as well as
respondents’ views vary greatly across European countries. in Spain and Portugal (both 49%).
Overall, slightly more than two out of three respondents
(68%) say that they expect fewer employees will be needed As discussed in the previous section, most respondents
as AI systems become more established and the number and expect artificial intelligence to take over elements of their
scope of use cases increases (see figure 3). The proportion work and redefine their job profiles. Analyzed by country,
is particularly high in Portugal (80%), Spain (78%), Italy more than three out of four respondents in Switzerland
(76%), and Belgium (74%). In contrast, there is somewhat (76%) assume that artificial intelligence will take over some
less concern about job losses as a consequence of AI of their tasks. This if followed by Spain and Portugal (72%
in Switzerland (57%), Germany (59%), and the each), Italy (70%), and Belgium (68%), where employees are
Netherlands (64%). likewise sure that – sooner or later – some of their tasks will
be taken over by applications from the field of AI.
One in three respondents in Italy (34%) expects that the The average among all respondents is 65%. In Germany
new technology will replace human labor on a large scale. (57%) and Austria (59%), the figure is below average.
The figure is similarly high in Portugal (31%). In contrast,
the proportion is significantly lower among respondents in Viewed by rank, managers (72%) are more likely to assume
Germany (14%), Switzerland (16%), and Austria (17%). that they will hand over tasks to AI-powered programs and
machines in the future than non-management employees
(61%). From a sector perspective, oil and gas (91%),
technology, media and telecommunications (81%),
financial services (81%), and insurance (81%) stand out.
10
From challenges to opportunities: How EY and AI work hand in hand
Figure 3
?
Do you think the use of Al will lead to companies needing fewer staff?
Portugal 25% 55% 18% 2%
Spain 18% 60% 20% 2%
Italy 20% 56% 21% 3%
Belgium 22% 52% 19% 7%
France 21% 48% 26% 5%
Austria 20% 45% 26% 9%
Netherlands 15% 49% 28% 8%
Germany 16% 43% 31% 10%
Switzerland 13% 44% 39% 5%
Europe West 19% 49% 26% 6%
Yes, definitely Yes, rather No, not so much No, definitely
Figure 4
?
Do you think your job is affected by the developments around artificial intelligence?
Italy 14% 45% 27% 14%
Switzerland 9% 50% 28% 13%
Netherlands 10% 47% 31% 12%
Austria 12% 44% 31% 13%
Germany 12% 44% 29% 15%
Portugal 11% 38% 30% 21%
Spain 10% 39% 33% 18%
Belgium 10% 38% 33% 19%
France 11% 36% 26% 27%
Europe West 11% 42% 29% 18%
Yes, very strongly Yes, partially No, hardly No, not at all
11
From challenges to opportunities: How EY and AI work hand in hand
On average in Europe, almost one in five respondents (19%) Upskilling and reskilling initiatives are of paramount
say that AI is already influencing their work – in Italy, it is importance to mitigate any negative consequences of
almost one in four (24%), while in Belgium, it is just over AI on employment. Organizations must invest in training
one in ten respondents (12%). programs to equip their workforce with the necessary
competencies to thrive in an AI-driven economy. Additionally,
A sizable 38% of all respondents expect to see a noticeable fostering a culture of lifelong learning is essential to ensure
increase in the influence of AI applications on their jobs that employees remain adaptable and resilient in the face
within the next three years. Here, respondents in of technological disruptions. According to our survey results,
Switzerland (54%) clearly stand out. not enough respondents are satisfied with the level of
training on AI they get at work. Switzerland leads the way,
That said, an interesting dichotomy is evident in that a not where 36% say their employer is providing enough training.
insignificant number of respondents think it unlikely that Employers in other countries need to do a lot better, most
artificial intelligence will take over parts of their work (35%). notably in Portugal, where only 14% of employees are
And of those who do anticipate having to hand over some satisfied with the current level of AI training they are
of their workload to AI, the vast majority don’t see that receiving. Most employees want live training and workshops
happening anytime soon (see figure 5). It appears that a (43%), followed by online courses (38%). In the following
substantial section of the workforce still believes that AI is section, we take a closer look at the investment priorities
not an imminent concern or it’s something that happens to of organizations in AI capabilities, including training.
somebody else. Either way, organizations clearly need to do
more to sensitize sections of the workforce about the scale
and scope of the AI revolution, an area in which training has
critical role to play.
AI
12
From challenges to opportunities: How EY and AI work hand in hand
Figure 5
?
How likely is it in your opinion, that parts of your tasks on the job will be done by programs and applications from the
field of artificial intelligence?
Switzerland 12% 64% 19% 6%
Spain 16% 56% 22% 5%
Portugal 21% 51% 22% 6%
Italy 16% 54% 23% 7%
Belgium 12% 56% 22% 10%
Netherlands 9% 56% 24% 11%
France 16% 47% 27% 10%
Austria 13% 46% 31% 10%
Germany 10% 47% 31% 13%
Europe West 14% 51% 26% 9%
Likely and very soon Likely but it will take some time Unlikely That is not going to happen
Figure 6
?
How likely is it in your opinion, that parts of your tasks on the job will be done by programs and applications from the
field of artificial intelligence?
Europe West Female Male
62,9% 65,7%
9%
14%
Likely and very soon
26% Likely but it will take some time
Management Non-management
Unlikely
That is not going to happen
51%
72,3% 60,7%
13
From challenges to opportunities: How EY and AI work hand in hand
5.
Capability building
With the rapid development of AI in mind, many decision-makers
across various sectors emphasize the need to accelerate AI initiatives
to gain a competitive edge, and are increasing investment accordingly.
Discussion of partnerships to accelerate innovation is common, while
a few are pursuing equity investments in AI specialists. However,
several companies also express concerns regarding the pace and
maturity of AI development, including both those investing and others
taking a more cautious approach. Some warn against overinvesting in
tools or use cases that could soon become obsolete.
To fully leverage the potential of AI, organizations must Analyzed by sector, employees in private equity (71%),
prioritize capability building. Assessing AI readiness is crucial financial services (66%), the energy sector (62%), and
to identify gaps and allocate resources effectively. Holistic advanced manufacturing and mobility (62%) are confident
capability building involves not only investing in cutting-edge of their employers’ ability to pursue the AI
technologies but also cultivating a data-driven culture and transformation journey.
nurturing talent with expertise in AI in all its manifestations,
from machine learning to large language models. Most employees in Switzerland (57%) expect AI to be a top
investment priority in the coming year, followed by Spain
Taking a look at the current situation, employees in (54%). Prospects for AI investment are bleaker in Germany,
Switzerland (58%) are most confident about the where only 25% of respondents expect AI to be prioritized
possibilities for AI implementation in their company. and Austria with a mere 22%.
In Italy, too, a majority (52%) confirm that their employer
has the knowledge and the will to tackle the AI Respondents see new software (35%) and employee
transformation. Employees in Germany (34%) and Portugal qualification (33%) as top investment priorities for their
(35%), on the other hand, are more skeptical about their organization when it comes to AI. Interestingly, forecasting
company’s ability to implement and leverage AI capabilities rank lowest in the list of investment priorities.
(see figure 7). That might seem surprising given the possibilities already
demonstrated by AI-driven high-precision forecasting in
many sectors.
14
From challenges to opportunities: How EY and AI work hand in hand
Figure 7
?
Do you feel that your company has sufficient knowledge to implement and use AI effectively and start the
transformation process that comes with it? Percentage of respondents who answered “yes”.
Switzerland 58,0%
Italy 51,7%
France 45,2%
Spain 41,4%
Belgium 40,4%
Netherlands 39,4%
Austria 37,3%
Portugal 35,2%
Germany 34,0%
Europe West 41,7%
15
From challenges to opportunities: How EY and AI work hand in hand
Taking a closer look at the people factor, managers in In many instances, employees are taking the initiative and
Switzerland (72%) are most confident that their people have availing themselves of self-learning opportunities, be it
adequate training to work effectively with AI or are ready for privately, professionally, or a combination of the two.
the transformation process ahead. This compares with 56% Self-education in the field of AI is most widespread in
in Belgium, 54% in Italy, and 51% in Spain. At the other end Switzerland (60%), Italy (54%), and Spain (54%). Employees
of the scale are Austria and Germany both with 34%. in Germany are least likely to be engaged in self-education
activities (37%), indicating a clear need to sensitize the
Broken down by sector, managers in advanced workforce there as to the importance of AI skills for the
manufacturing (69%) are most confident that their people future of work and their career prospects.
have adequate training to work effectively with AI or are
ready for the transformation process ahead. This compares AI can be a powerful tool in the hands of skilled and
with 65% in financial services, 65% in agriculture, and 63% well-trained employees, promising massive productivity
in private equity. Lagging well behind at only 19% is the gains. Companies need to adopt an active role in training
public sector practice. and upskilling their people. Among other initiatives,
strategic partnerships with academic institutions and
Training programs tailored to the specific needs of each technology providers can also facilitate knowledge
region, sector, and function are essential for ensuring the exchange and accelerate innovation. By investing
successful integration of AI into business operations. in AI capabilities today, organizations can position
Employees are beginning to recognize the imperative themselves as leaders in an increasingly
of honing their AI acumen for their careers, with 44% of competitive landscape.
respondents stating that they are educating themselves
in the field of AI. Revealing a concerning gender bias,
our survey indicates that male employees (49%) are
more likely to be brushing up on their AI skills than their
female colleagues (40%).
AI
16
From challenges to opportunities: How EY and AI work hand in hand
Figure 8
?
Which specific field will be a top investment priority over the next year for your company when it comes to AI?
(up to five answers)
New software 34,5%
Employee qualification 33,0%
Cyber security 26,2%
Optimzing/automating current processes 25,7%
(Data)Analytics 23,3%
New hardware 23,2%
Logistics 20,1%
Manufacturing 17,5%
Analyzing customer/client data 16,6%
Customer contact/services 14,2%
Analyzing in general 12,7%
Accounting 12,7%
Copy writing 12,4%
Analyzing processes within the company 11,4%
Human ressources 10,2%
Controlling 8,7%
Procurement 8,6%
Knowledge management 8,6%
Forecasting 6,4%
Other fields 1,2%
Figure 9
?
Are you educating yours |
3 | ey | ey-gl-adobe-genai-marketing-guide-06-2024.pdf | GUIDE
Leading generative
AI deployment for
marketing.
Overcoming three hurdles in
generative AI adoption.
1
Contents
Executive Summary 3
Thought leaders in generative AI 4
Dial up transparency as you improve the relevance of customer experiences. 4
Be transparent while building your first-party data. 5
Match generative AI to customer expectations. 5
Make customer benefits central to decisions. 6
Amplify creativity without replacing human judgement. 6
Transform skeptical and novice employees to empowered generative AI pros. 6
Prioritize upskilling at all levels. 7
Begin with content creation. 8
Use short-term comparison metrics. 8
Appoint generative AI pioneers. 9
Drive generative AI innovation with confident governance. 9
Map and mitigate novel generative AI risks. 10
Establish a single point of control. 11
Organize your goals into the right sequence. 11
A checklist to start now. 12
Conclusion 13
Methodology 14
Sources 14
About Adobe 15
About EY 15
2
Executive Summary
Generative AI is defining the next generation of marketing We spoke with leading executives around the world across
today. Delivering hyper-personalized, multi-channel customer marketing, creative, CX, data, legal, risk, and compliance.
experiences at a fraction of the time and cost. Helping you glean
insights from your data in an instant. Detecting and responding to We uncovered three primary challenges to generative AI
conversion opportunities in real time. Experimenting to enhance adoption: managing customer privacy and experience
customer experience and deliver results at pace. expectations, transforming employees concerned about their jobs
into champions and innovators, and establishing governance that
This year, 98% of CEOs will invest in their company’s generative enables generative AI innovation to flourish.
AI capability. But 66% remain uncertain of the optimal adoption
path for their organization.1 To assist, Adobe collaborated with Through our interviews with early adopters, we found
the EY organization to undertake a series of structured, these consistent challenges and uncovered resolutions to
qualitative interviews to learn from generative AI first movers. overcome them.
Customers
80
Dial up transparency as you improve the relevance of customer experiences.
%
With generative AI, customers expect improved personalization from brands—but their trust
in organizations to use their data responsibly is limited. Generative AI can help you please your
of customers prioritize
customers with relevant and timely experiences. But to stand out in a crowded field, your focus
knowing when they are
on their needs must be tangible at every touchpoint.
talking to a human being
Resolution: Design every step in your generative AI journey for transparency and accountability or a bot.2
to customers to deliver meaningful experiences they trust.
Employees
Transform skeptical and novice employees to empowered, generative AI pros.
81
% Early adopters are making their first returns on investment in generative AI by automating
lower-value, repetitive tasks, for example, in content production. However, this is also where
employees will be most anxious about role reductions. To make progress, organizations should
Employees expect AI to
reassure and incentivize employees to master the tools, to experiment, and to contribute toward
free them to focus on
the future of their function.
higher-value tasks.3
Resolution: Prove the value of generative AI to employees, demonstrating job enrichment, time
savings, new opportunities, and career advancement.
Organization
#2 Priority
Drive generative AI innovation with confident governance.
Innovation in generative AI can drive efficiency and deliver new opportunities for revenue, but the
Data security and AI
pace at which you realize these gains is dependent on governance. Your external vendors and partners
should offer not just innovative tools, but also responsibly developed ones. You’ll also need your internal governance frameworks
stakeholders to flag the right opportunities, share data, and collaborate on new governance processes to are second only to
work at pace with your vendors.
employee skills in execs’
2024 priorities for AI.4
Resolution: Level up your leadership oversight and governance processes and focus on commercially
safe solutions that help you manage risk while taking advantage of the opportunities generative AI offers.
3
Thought leaders in generative AI.
Adobe and EY specialists are privileged to work with a wide range of organizations around the world,
facilitating their deployment of generative AI especially in the domain of customer experience (CX).
The world’s leading brands and agencies are partnering with Adobe to drive greater efficiency
in their organizations, applying our natively integrated generative AI in Creative Cloud and
Experience Cloud today to empower their teams to boost productivity and deliver personalization
at scale. We believe 2024 will be a watershed moment in developing customer experience.”
Eric Hall
SVP and Chief Marketing Officer,
Digital Experience, Adobe
Far from taking away creative work, we see generative AI supercharging it, creating exponential
value, and putting a new palette of CX capabilities at the fingertips of your whole team, which
further builds confidence. Customer expectations will change in 2024 through exposure to hyper-
personalized experiences. We are inspired by this generational opportunity, and the extraordinary
uses our clients are already making of it, keeping people at the center.”
Laurence Buchanan
Global Customer and Growth Leader, EY
From this experience and discussion with industry leaders we’ve distilled insights to support marketing
and CX leaders as they evaluate, implement, and harness the power of generative AI. This guide
concludes with a checklist to help you assess and refine your immediate priorities this year.
90
%
of $5 billion+ revenue companies remain at proof-of-concept or isolated capabilities in generative AI
Source: May 2023: EY Innovation Realized pulse survey, C-suite executives from majority $5bn+ global companies
4
Dial up transparency as you improve the relevance
of customer experiences.
Adobe’s State of Digital Customer Experience research revealed that 56% of consumers believe that generative AI
will make digital experiences more personalized, 54% believe content will be more relevant to their preferences,
and 53% expect to see an increase in product and service innovation.6
However, generative AI also poses new questions about privacy, transparency, and control. Consumers are
wary—79% are concerned or very concerned about how companies are using their personal data.7 So, CX leaders
must bring the voice of the customer to every part of the business that’s experimenting with this technology.
Marketing leaders must validate that every touchpoint that makes up the brand experience remains meaningful
and authentic. The organization will need their leadership to keep the focus on differentiating the brand and
building trust, regardless of function or touchpoint. On this solid foundation, you can push forward to deliver the
personalization that customers value.
To help resolve the tension between privacy and relevance, take these
actions that we see first movers doing:
Be transparent while building your
first-party data.
Organizations are adapting to a cookieless future by
expanding and enriching their own first-party customer Customer acquisition costs through digital
data, with the right permissions and consents. This also marketing are still pretty high right now. One
means working with your data partners to determine change that motivates is to really think about
how to collaborate to enrich data without relying on your first-party data—what data do we want to
third-party cookies. own and how do we get that data with the right
consent and permissions to be able to use it?”
As you shift to a clear first-party data strategy you must
also set clear expectations as you gather data. One CDO
Laurence Buchanan
at a global consumer packaged goods (CPG) business
Global Customer and Growth Leader, EY
explained to us that offering customers clarity in the
moment about how the company will hold and use
data is essential to winning trust and the consents they
need to engage customers with their augmented reality
experiences.
5
Match generative AI to customer As a creative team, we decided from day one
expectations. that we need to make sure that we’re upfront
about when we’re using AI versus not. Part of
Organizations need guidelines for disclosure around the
why we work with Adobe is because of their
use of generative AI. Regulators and governments do
not always keep up with the rapid pace of change, so stance on ethics. The idea that they’re making
marketing leaders need to champion the best interests sure that they’re pulling everything from their
of their customers. own stock images which are sourced ethically,
that was a big deal for us. They are also
An example of this is to take extra care when leveraging working on creating watermarking, so viewers
generative AI to represent human appearance or voice— know when it was generated by AI, that we can
customers may be upset by mistaking a generative AI then adapt in our marketing material.”
experience for an actual human being. A global CPG
organization commits not to use generative AI for any
Bridget Esposito
front-of-packaging images of people—employing it instead
Vice President, Head of Creative, Brand,
for close-ups where customers expect to see illustrations or
Prudential
infographics. A US top-5 insurer has set similar guidelines
that permit the use of generative AI for creating product
images, but never for images of humans.
Make customer benefits central to decisions.
It’s vital to look beyond immediate business value to target
potential benefits for customers. How are you considering
We have some special opportunities
customer preferences? Could the generative AI you’re
and possibilities to create quite stunning
deploying today make a customer experience more
digital experiences for our customers, or
empathetic, more accessible, or more timely?
to have a much more immersive shopping
experience. That is a super big moon shot
As you weigh your priority initiatives, factor in satisfaction
ratings or other customer experience indicators—to start but you can imagine a tool that would
where the real customer value is and to monitor the generate how a product would look in your
impact on your customer experiences. space. These are real possibilities.”
One multinational retail organization measures the Stefan Esping
impact of their generative AI chatbot not only through Data & Machine Learning Domain Manager,
customer satisfaction ratings, but also by the percentage Ingka
of conversations contained within the chatbot rather than
being transferred to an employee, and any increases in
sales following a chatbot interaction.
Amplify creativity without replacing human judgement.
Have a clear strategy to validate that any generative AI content is true to your brand, creatively enriching, and never generic.
As you free up creative teams from lower-value tasks such as image variation, the team can refocus on larger, more impactful
creative work for your brand.
Additionally, it is critical to apply generative AI via a custom model that can be trained on your own brand content, tone, style,
images, and standards so the outputs retain your brand’s unique traits.
6
First movers tell us they retain a human in the loop to keep consistency in brand messaging, imagery, and tone of voice. This
helps you to guarantee meaningful customer experiences every time.
A technology executive for a CPG company explained that generative AI is already deployed in drafting a significant
proportion of the product copy that’s displayed on retail websites in the US and the UK to engage consumers with their
products’ features and benefits. None of it goes live without human approval. In a global fashion brand, generative AI
develops prompts and visualizations for product designers, drawing on trends harvested from customer sentiment analysis.
Transform skeptical and novice employees to
empowered generative AI pros.
Demonstrate generative AI as a creative, career-building opportunity.
Generative AI creates real opportunity for professional development and career enhancement, but it’s only
natural that some employees may feel anxious around generative AI initiatives within their organizations. This
tension may be the most important blocker you face. You need your teams to have appetite for exploring
generative AI, before they can begin to capture the new opportunities it brings.
98 12
% to %
Reduction in proportion of employees concerned about generative AI after participating in pilot.
Source: January 2024: EY generative AI tool deployment – internal study
The senior executives surveyed in Adobe Digital Trends 2024 cited “advanced AI skills training for key staff” and
“basic AI understanding for all employees” as their top two priorities for preparing their employees to work
effectively with generative AI.8 Assigning pilot projects and letting insight grow organically is an essential first
step. Marketing leaders need to make these opportunities visible and relevant to each team member, to build the
“what’s in it for me” of the technology.
As one leader explained, on average, employees can expect their roles to become more strategic.
Our interviewees recommend content creation and content workflows as tasks that allow you to move swiftly to
demonstrate benefits for employees and customers. In an analysis of a generative AI deployment undertaken by
EY LLP, a 12-week pilot in a specific use case dramatically improved employees’ ability to grasp the opportunity
beyond the risks 9:
■ Understanding the potential of generative AI grew from 37% to 84%
■ Concerns about using generative AI fell from 99% to 12%
■ Confidence in personal ability to work with generative AI improved from 28% to 77%
7
To turn uncertain novices into empowered professionals, marketing leaders need to:
A digital executive in a CPG organization spoke about
Prioritize upskilling at all levels.
building a Center of Excellence. They provided education for
Success depends on the readiness of your teams to leverage
every one of their 10,000 employees to create a common
generative AI tools and processes. To enable them to become
baseline of understanding across every employee, and
fluent in generative AI, define and deliver training programs
published white papers about generative AI on the company
for all employees, from executives to practitioners.
portal. One Global Chief Marketing Officer in professional
services holds “promptathons,” a series of prompting
One marketing leader at a global professional services
sessions to upskill her team in the “art of the prompt.”
firm recommends creating “learning labs” with access to
generative AI tools, giving employees guidance and hands-
Adobe has established an AI Center of Excellence that
on experience with tools to reduce uncertainty. Another
oversees strategic alignment, compliance, and governance of
organization has an internal portal where employees can
AI initiatives. To ensure employees are equipped to apply AI
request a license for pre-approved generative AI tools.
in their roles, Adobe has comprehensive training programs,
resources, and podcasts to upskill the workforce and
personalize career development.
At Shiseido, we prioritize the continuous learning and development of our employees. Through our internal
Digital Academy, we provide accessible programs and certifications in data and AI advancements.
This education is crucial for our team’s success.”
Angelica Munson
Global Chief Digital Officer,
Shiseido
Begin with content creation.
We consistently hear that optimizing content creation or
content workflows is a powerful first move. It’s where your
employees are spending a lot of time and does not require
a lot of data connectivity to get started. Starting points for
generative AI usage in content workflows include: If you think about the previous world, you
■ Creative concepting and ideation would have a great concept, a great idea.
That would take time to really bring to life
■ Copy drafting and iterations
to share with your ‘buyer’. Now generative
■ Image drafting and refinement
AI can bring that idea very quickly to some
■ Production of content variations for testing across: sort of visualization. That speed is a clear
■ different channels benefit.”
■ different markets
■ different personas Duncan Avis
Americas Customer & Growth Leader, EY
Generative AI helps teams overcome the content
scalability challenge, boosting the quality, quantity,
velocity, findability, and reusability of the content you need
to drive personalization across multiple channels.
8
Use short-term comparison metrics. Choose metrics that build confidence:
■ Workplace satisfaction
Several first-movers report success using comparison
metrics to motivate employees and engage budget-
■ Time saved
holders with the before-and-after progress they’re making.
For example, 82% of employees taking part in a global
professional services generative AI pilot reported faster ■ Volume of content created
task completion.10
■ People required
■ Cost per asset
■ Speed to launch
Appoint generative AI pioneers.
In some areas, activating the organization to adopt
generative AI will be similar to change programs you
may have led in the past. The network of early adopters
in your org needs to be experienced in their professional
disciplines and able to mentor others.
To identify your generative AI pioneers across the
organization, start with employees who:
What has worked well for us is taking
■ Have a direct interest in AI capabilities, from the
perspectives of the business, marketing, tech, and risk an employee ‘influencer’ approach by
identifying people who are hungry to
■ Have the skills and appetite to communicate the benefits change, hungry to learn, and building out
and to positively influence employee culture the process with those employees. This
will then be cascaded throughout the
■ Hold aspirational, mid-level roles with a degree of organization more broadly.”
decision-making, managing more junior levels in the
organization
Chris Chesebro
Chief Digital Officer, Wella
■ Are commercially aware and risk-informed, capable of
assessing innovations from both perspectives
Collaborate with them to research and propose a set
of generative AI design principles and equip them to
experiment. Their example and enthusiasm can inspire the
team to move faster and move past any uncertainties.
One CPG organization has chosen 30 employees from
middle management to take part in the first generative
AI pilot in a sandbox environment. They were tasked to
identify risks and share learnings.
9
Drive generative AI innovation with
confident governance.
Develop generative AI controls and partners that can help you navigate risk
and opportunity.
To deliver business outcomes such as cost savings or content acceleration with generative AI, companies must
choose solutions that are built for business use cases. The right generative AI tools will need to meet some
unique criteria and have the right controls in place:
■ The base model must give you transparency into the data provenance and be designed for commercial safety.
■ You must be able to apply custom models that are trained on your own data to keep outputs relevant for
your brand and your business.
■ Your data must be secure and private, not shared with other businesses or used to train a publicly
available model.
■ Your partners should prioritize ethical, responsible AI development to protect your brand.
In addition to careful selection of the generative AI solutions that fit your business, companies must optimize
governance of those tools within their organization. Your existing internal controls framework will need to evolve.
AI governance is not just about setting rules, it’s about striking the right balance. It’s about
fostering creativity and innovation while ensuring accountability, responsibility, and transparency.
At the heart of AI governance is the commitment to respect our customers and align with our
values. It’s about turning AI potential into real-world applications, responsibly and ethically.”
Cynthia Stoddard
CIO of Adobe
10
To unleash the full potential of innovating in generative AI,
leaders have learned how to:
Map and mitigate novel generative AI risks.
Use an evaluation framework for generative AI tools that
screen for solutions with responsibility engineered into
their tools, including:
■ Clear intellectual property rights accounted for and
indemnification provided to minimize lawsuit risk
I don’t want to use an AI that’s been trained on
■ Robust security and privacy of your data non-licensed materials. We expect our bigger
■ Fairness and bias controls built-in agencies to self-certify for responsible practices
and we will write it into their contracts.”
■ Transparency in how models are built
Select vendors and partners who are passionate about IT Engineering Director,
preserving intellectual property and content credentials Global packaged good organization
and are helping to guide global regulation. Check if they
participate in industry standard-setting, for example
in the Content Authenticity Initiative, the NIST AI Risk
Management Framework or the EU AI Elections Accord. By
thinking ahead of regulations, these vendors will help to
future-proof your developing generative AI capability.
Establish a single point of control.
As marketing organizations move generative AI from
pilot to production, they need “air traffic control”—a
team comprising marketing, compliance, and technology
It’s crucial for marketing to be positioned
heads—to coordinate and direct generative AI development
at the heart of an AI control tower strategy,
across the organization. They will:
serving as a central hub that coordinates with
■ Define and communicate a governance framework for
legal, cybersecurity, privacy, and technology
generative AI
stakeholders to harness and action data
■ Assess risk for new generative AI vendors and proposals insights effectively. This centralized approach
helps establish that the CMO and marketing
■ Prioritize for customer and commercial relevance
teams are integral to the collaborative network,
■ Direct capital investment in generative AI
facilitating a unified direction and decision-
making across the various departments, to
This control function should be a distinct practice of an
overall delivery-focused generative AI Center of Excellence, move at the speed of business while mitigating
whose scope it is to govern: risk.”
■ The business model—generative AI opportunities
Tom Edwards,
for product portfolios, value proposition, growth
Managing Director,
opportunities
Applied & Generative AI Lead, EY
■ The operating model—generative AI potential to reduce
cost, accelerate, evolve the organization
■ Risk management—identifying and mitigating novel
11
risks, such as data privacy, bias, IP, and so on
11
Organize your goals into the right sequence.
A key role for leaders governing generative AI in their organization is to recognize the different ways it affects customers
and employees and to sequence your projects to suit.
Aim to prove concepts and cultivate skill and insight within the team before taking on more complex use cases.
The typical order, from simple to complex, will be:
Integrate vendor tools into your content creation
workflows to add creative uplift, scale, and
accelerate content production.
In parallel to your content creation
opportunities, kick off work to audit, connect,
clean, and structure your data. This will help
you prepare for more data-heavy generative
Develop customized content using generation AI use cases like personalization.
models trained on proprietary content, brand
guidelines, and historic campaigns.
Personalize marketing campaigns—build tailored messaging, content, and journeys
across channels for each customer.
Harness unstructured data by using generative AI to query, gather, and democratize insights from broad
datasets. This also helps you strengthen your personalized marketing campaigns noted above.
12
A checklist to start now.
To get started now and deliver on the full potential of generative AI in marketing and CX, organizations
should focus on the following key areas identified from our research with marketing leaders and subject
matter experts:
1. Customer trust
■ Do we have a list of customer pain points?
■ Is customer experience fully visible and factored into the way we assess generative AI priorities?
■ Have we defined specific customer-centric principles for uses of generative AI?
■ Does our existing research gather data on customer attitudes to generative AI?
■ Have we reviewed current brand guidelines to fit with generative AI applications?
2. Employee empowerment
■ Do we have a cross-functional list of employee pain points?
■ Do we have the right communications plan and training resources in place?
■ Do we have experiments up and running—and are we capturing what we learn?
■ Have we created space for open-ended innovation during generative AI discovery and experimentation?
■ Does the team have a mandate to discover its own metrics as projects progress?
■ Are we investing in generative AI training for all levels?
■ Have we created simple, accessible ways for employees to access and familiarize themselves with generative AI tools?
■ Are we refining roles and responsibilities to keep a human in the loop?
■ Have we defined scope and nominated advocates for a network of generative AI champions?
3. Organizational opportunity
■ Do we have an evaluation process in place to screen tools for risk mitigation?
■ Do we understand where generative AI is being assessed or implemented across the organization?
■ Is there a team in place applying a common framework or governance to align and maximize benefits?
■ Have we defined an efficient process to evaluate and implement generative AI technology in partnership with our
technology and legal peers?
■ Are we clear how our vendors and strategic partners’ generative AI initiatives map to our needs? Have we made full use
of their advice and resources?
■ Have we considered our customers and employees in the sequencing of our generative AI initiatives?
■ Is our mid- to long-term data transformation plan defined?
13
Conclusion
Leading the marketing function in the era of
generative AI.
Generative AI is here to stay as a transformative force across every part of the organization. But it has special
relevance for marketing and CX. In some capacity, 83% of creative professionals are already using generative AI
tools in their work. Among Gen-Zs, it’s above 90%.11
As a marketing or CX leader, applying generative AI means designing a plan for the marketing function that helps
drive profitable demand, inspires your employees, and enriches the customer experiences you deliver. It’s critical
to keep these three challenges in mind at every step: for your organization, your employees, and your customers.
Methodology
Structured interviews were conducted with participants in 30-, 45-, or 60-minute sessions with external
organizations (n=11) and subject matter experts (n=10).
Sample consisted of participants from across marketing, CX, digital, data, legal, and creative.
Focus of the discussion looked to explore relevant use cases, partnerships, and lived experiences from individuals
in the support of, exploration, and deployment of generative AI within a commercial context to gather lived
experiences and practical advice from participants.
Sources
1 EY CEO Imperatives quarterly update, January 2024
2 Adobe Digital Trends 2024, March 2024
3 EY - US, How organizations can stop skyrocketing AI use from fueling anxiety, October 2023
4 Adobe Digital Trends 2024, March 2024
5 EY Innovation Realized pulse survey, C-suite executives from majority $5bn+ global companies, May 2023
6 Adobe, The State of Digital Customer Experience Report 2023, October 2023
7 Adobe Trust Report - Customer trust is earned or broken with every experience, March 2022
8 Adobe Digital Trends 2024, March 2024
9 EY generative AI tool deployment – internal study, January 2024
10 EY generative AI tool deployment – internal study, January 2024
11 Adobe Blog - Creative pros are leveraging Generative AI to do more and better work, February 2024 14
ABOUT ADOBE
Adobe Experience Cloud is the most comprehensive suite of customer experience management
tools on the market. With solutions for data, content delivery, commerce, personalization, and
more, this marketing stack is created with the world’s first platform designed specifically to
create engaging customer experiences. Each product has built-in artificial intelligence and works
seamlessly with other Adobe products. And they integrate with your existing technology and
future innovations, so you can consistently deliver the right experience every time.
ABOUT EY
EY exists to build a better working world, helping create long-term value for clients, people and
society and build trust in the capital markets. Enabled by data and technology, diverse EY teams
in over 150 countries provide trust through assurance and help clients grow, transform and
operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask
better questions to find new answers for the complex issues facing our world today.
EY refers to the global organization, and may refer to one or more, of the member firms of Ernst
& Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a
UK company limited by guarantee, does not provide services to clients. Information about how
EY collects and uses personal data and a description of the rights individuals have under data
protection legislation are available via ey.com/privacy. EY member firms do not practice law
where prohibited by local laws. For more information about our organization, please visit ey.com.
© 2024 EYGM Limited. All Rights Reserved.
EYG no. 005649-24Gbl
This material has been prepared for general informational purposes only and is not intended to be relied upon as accounting, tax, legal or other professional advice.
Please refer to your advisors for specific advice. The views of third parties set out in this publication are not necessarily the views of the global EY organization or its
member firms. Moreover, they should be seen in the context of the time they were made.
15 |
4 | ey | ey-idc-maketscape-worldwide-ai-services-2023-vendor-assessment.pdf | IDC MarketScape
IDC MarketScape: Worldwide Artificial Intelligence Services
2023 Vendor Assessment
Jennifer Hamel
THIS IDC MARKETSCAPE EXCERPT FEATURES EY
IDC MARKETSCAPE FIGURE
FIGURE 1
IDC MarketScape Worldwide Artificial Intelligence Services Vendor Assessment
Source: IDC, 2023
Please see the Appendix for detailed methodology, market definition, and scoring criteria.
May 2023, IDC #US49647023e
IN THIS EXCERPT
The content for this excerpt was taken directly from IDC MarketScape: Worldwide Artificial Intelligence
Services 2023 Vendor Assessment (Doc # US49647023). All or parts of the following sections are
included in this excerpt: IDC Opinion, IDC MarketScape Vendor Inclusion Criteria, Essential Guidance,
Vendor Summary Profile, Appendix and Learn More. Also included is Figure 1, 2 and 3.
IDC OPINION
This IDC study represents a vendor assessment of the 2023 artificial intelligence (AI) services market
through the IDC MarketScape model. IDC last assessed this market in 2021. In the past two years, we
have revised our evaluation criteria and buyer perception survey instrument to refine our assessment
methodology and reflect market evolution. Thriving vendors in today's AI services market can both
clearly articulate their strategies for enabling clients' adoption of AI solutions and readily demonstrate
their current capabilities and proof points through existing client engagements.
Organizations increasingly look to AI solutions to drive revenue and profit growth as well as improve
outcomes in areas such as customer satisfaction, operational efficiency, sustainability, process speed
and accuracy, and speed to market for new products and services. However, many challenges persist,
including employees' lack of data literacy and technology training, technical complexity, lack of
resources to support end users and maintain AI systems, and issues related to security, privacy, and
governance. Professional services firms remain a critical source of expertise, skills, and tools to
incorporate AI into digital business strategies, build production-grade solutions, and realize ROI.
In this assessment, IDC evaluated AI services vendors across scoring criteria and collected feedback
from customers on their perception of the key characteristics and the capabilities of these vendors.
Key findings include:
▪ The most critical vendor attribute for successful AI services engagements, according to IDC's
Artificial Intelligence Services Buyer Perception Survey, remains "ability to achieve business
outcomes." The perceived priority of this attribute over all others was unchanged from the
2021 study.
▪ When buyers were asked about the primary business objective driving their engagement of
their artificial intelligence services vendor, at a worldwide level, the most frequent responses
were "improve operational efficiency," "build capability for tomorrow's business," and "drive
higher revenue growth, gain market share." Nearly 30% of the buyers we surveyed said they
achieved 30% or greater improvement in measurable KPIs from their AI services engagement.
▪ The top-rated vendor attribute, in aggregate, was the ability to "integrate vendor project team
with internal team." This aligns with IDC's evaluation of client adoption strategies around
workshops and stakeholder alignment and AI program enablement as top areas of strength on
average across AI services vendors.
IDC MARKETSCAPE VENDOR INCLUSION CRITERIA
This research includes analysis of AI services providers with global scale and broad portfolios
spanning IDC's research coverage. This assessment is designed to evaluate the characteristics of
each firm — as opposed to its size or the breadth of its services. In determining the group of vendors for
analysis in this IDC MarketScape, IDC considered the following set of inclusion criteria:
©2023 IDC #US49647023e 2
▪ Worldwide AI services revenue of at least $100 million over the last calendar year, with
revenue generated in each major geographic region (i.e., Americas, EMEA, and Asia/Pacific)
▪ Offerings across the life cycle of AI business and IT services (e.g., project-based, managed,
support, and training)
▪ AI services offerings and solutions addressing a range of industry verticals and business
functions
▪ Go-to-market alliances with a range of AI software providers
ADVICE FOR TECHNOLOGY BUYERS
▪ Maturity assessment. Challenges exist at every stage of the AI adoption journey that often
require expert advice to navigate. Look for services firms to assess your organization's AI
maturity, readiness, talent, and data needs and assist you with creating or refining AI
strategies and operating models to achieve specific business objectives and prepare you for
the next stage of adoption. Even organizations with previously established AI programs may
find your strategies and governance frameworks need adjustment to consider new implications
(ethical, regulatory, or otherwise) of generative AI capabilities and to incorporate appropriate
guardrails for developing and using the technology.
▪ Use case development. In today's economic climate, there is a heightened need to connect AI
solution innovation to real business outcomes. Seek a services partner that can provide
frameworks, methodologies, and tools to help you source innovation ideas from within your
business, discover and prioritize use cases, define KPIs for measuring business value, create
a strong innovation foundation across your organization, and produce deployable and scalable
AI solutions. As several of the customer reference interviews IDC conducted for this study
indicated, vendors' industry and functional domain knowledge gained from experience working
with many different customers helps accelerate the process of identifying and developing
impactful AI use cases.
▪ Skills. AI talent gaps are neither new nor abating for organizations anytime soon. IDC research
suggests that organizations will not solve their AI talent issues by merely hiring more data
scientists. Seek a services partner that can provide expertise not only in core AI model
development and your chosen AI platform but also in scaling and operationalizing AI models
(whether custom-developed algorithms or repurposed "off the shelf" solutions) and in
empowering your business end users to leverage AI-driven insights in their roles. Also,
consider guidance and support from services partners beyond staff augmentation to help you
build AI skills in your organization. Ask for best practices, recruiting resources, access to on-
demand AI talent pools, and pod-based or build-operate-transfer models that enable your
employees to learn AI skills while working with expert teams.
▪ Innovation and delivery accelerators. The fundamental value that AI services vendors offer is
helping customers achieve ROI from AI more quickly than they would on their own. Consider
the proprietary assets that vendors may propose as part of their AI services offerings, which
can include pretrained industry- or function-specific models, reusable component repositories,
curated and annotated training data sets, developer tools and microservices, and even full-
fledged products and platforms. These assets can fill gaps in commercial software products,
address specific business domain or technical challenges (such as integrating legacy
enterprise systems with new AI capabilities), or industrialize AI solution development and
management. Also consider the ecosystem of partners that AI services vendors collaborate
with to provide access to innovation that benefits your organization.
©2023 IDC #US49647023e 3
▪ Stakeholder alignment. According to IDC's Artificial Intelligence Services Buyer Perception
Survey, the most common project sponsors for AI services engagements were CIOs/CTOs,
information technology (IT) directors and managers, chief analytics/data officers, and line-of-
business (LOB) heads. Choose a vendor that can work across IT, LOB, and data teams to
ensure solutions address key stakeholder priorities. Buyers also rated "knowledge
transfer/training for our internal team" as one of the top 10 most critical attributes for AI
services engagement success. Seek out vendors that not only speak with budget holders but
also communicate effectively with end users, who will be interacting with and supporting AI
solutions, through workshops and change management programs.
▪ Data and AI governance. Strong foundations for data quality and privacy, responsible AI, and
MLOps are critical for enterprise-grade AI solutions that are both functional for business needs
and compliant with regulatory and risk management requirements. Seek services providers
that offer thought leadership and frameworks for data privacy, responsible AI, and MLOps and
proactively help you consider these issues as early as possible in the design process, as well
as through the deployment and monitoring of solutions, to mitigate potential risks.
▪ Vendor selection. Use this IDC MarketScape in contract negotiations and as a tool to not only
short list vendors for AI services bids but also evaluate vendors' proposals and oral
presentations. Make sure you understand where these players are truly differentiated and take
advantage of their expertise, technical, industry base, or otherwise.
VENDOR SUMMARY PROFILES
This section briefly explains IDC's key observations resulting in a vendor's position in the IDC
MarketScape. While every vendor is evaluated against each of the criteria outlined in the Appendix,
the description here provides a summary of each vendor's strengths and challenges.
EY
According to IDC analysis and buyer perception, EY is positioned in the Leaders category in this 2023
IDC MarketScape for worldwide artificial intelligence services.
EY places data and AI at the core of its Transformation Realized approach, which aims to enable
clients to envision their future business models and then design transformations that develop technical
capability at scale and manage organizational change. The firm's offerings cover both direct expansion
of large-scale AI programs and infusion of AI into transformational programs driven by C-suite buyer
agendas. Increasingly, EY integrates resources from its AI practice with strategy consultants from EY-
Parthenon to engage with boards and to shape AI strategies and has recently launched a generative
AI strategy and road map offering. The firm also continues to invest in proprietary technology
capabilities on the EY Fabric Intelligence ecosystem to provide responsible AI solutions (ShEYzam
including fairness as a service, NLP as a service) and reusable assets (e.g., EY Lighthouse). EY has
also created a collection of prebuilt AI solutions made available to clients through a marketplace called
EY ASpace. EY also leverages strategic partnerships with AI technology providers such as Microsoft,
SAP, IBM, Databricks, and Snowflake to codevelop solutions in quickly evolving areas such as
fairness, sustainability, and generative AI.
Strengths
According to customers, EY's strengths are the company's ability to deliver across the life cycle of AI
services, provide solutions using their preferred AI technology providers, integrate EY's project team
with their internal team, deliver AI-enabled automation services, and resolve problems or issues
©2023 IDC #US49647023e 4
related to customer service. IDC considers EY's strategies around offerings, platform-based delivery,
client adoption, sales enablement, alliances, growth, innovation and R&D, technology skills, and
employee retention as key strengths. EY also showcased strengths in achieving business outcomes
for clients with AI services.
Challenges
IDC believes EY's go-to-market strategy, though strong overall, could be improved further by more
collaboration with specialist AI software providers and data annotation services or crowdsourcing
providers on go-to-market initiatives for AI services. EY could also benefit from continued investment
in new asset-based AI services.
APPENDIX
Reading an IDC MarketScape Graph
For the purposes of this analysis, IDC divided potential key measures for success into two primary
categories: capabilities and strategies.
Positioning on the y-axis reflects the vendor's current capabilities and menu of services and how well
aligned the vendor is to customer needs. The capabilities category focuses on the capabilities of the
company and product today, here and now. Under this category, IDC analysts will look at how well a
vendor is building/delivering capabilities that enable it to execute its chosen strategy in the market.
Positioning on the x-axis, or strategies axis, indicates how well the vendor's future strategy aligns with
what customers will require in three to five years. The strategies category focuses on high-level
decisions and underlying assumptions about offerings, customer segments, and business and go-to-
market plans for the next three to five years.
The size of the individual vendor markers in the IDC MarketScape represents the market share of each
individual vendor within the specific market segment being assessed.
IDC MarketScape Methodology
IDC MarketScape criteria selection, weightings, and vendor scores represent well-researched IDC
judgment about the market and specific vendors. IDC analysts tailor the range of standard
characteristics by which vendors are measured through structured discussions, surveys, and
interviews with market leaders, participants, and end users. Market weightings are based on user
interviews, buyer surveys, and the input of IDC experts in each market. IDC analysts base individual
vendor scores, and ultimately vendor positions on the IDC MarketScape, on detailed surveys and
interviews with the vendors, publicly available information, and end-user experiences in an effort to
provide an accurate and consistent assessment of each vendor's characteristics, behavior, and
capability.
Market Definition
IDC defines AI as systems that learn, reason, and self-correct. These systems hypothesize and
formulate possible answers based on available evidence, can be trained through the ingestion of vast
amounts of content, and automatically adapt and learn from their mistakes and failures.
Recommendations, predictions, and advice based on this AI provide users with answers and
assistance in a wide range of applications and use cases.
©2023 IDC #US49647023e 5
AI services are utilized to assess, plan, design, implement, and operate the following:
▪ AI platforms facilitate the development of artificial intelligence models and applications,
including intelligent assistants that may mimic human cognitive abilities.
▪ AI applications include process and industry applications that automatically learn, discover,
and make recommendations or predictions.
Detailed definitions of the software tools and platforms that are relevant for AI services engagements
are available in IDC's Worldwide Software Taxonomy, 2023 (IDC #US50513623, April 2023). The
underlying data services are a critical component to AI systems, serving as the basis upon which initial
analysis and learning are conducted. Data services are highly specific to the function and process of
the AI system and may come from a wide range of sources, both unstructured and structured. These
data services include the processes needed to ingest, organize, cleanse, and utilize the data within the
AI-enabled applications.
AI services providers engage with clients to build AI capabilities through business services and IT
services (see Figure 2). For a detailed definition of the services markets illustrated in Figure 2, see
IDC's Worldwide Services Taxonomy, 2022 (IDC #US47769222, July 2022).
FIGURE 2
Artificial Intelligence Services
Source: IDC, 2023
Customer Perceptions of AI Services Vendors
A significant and unique component of this evaluation is the inclusion of the perceptions of AI services
buyers of both the key characteristics and the capabilities of the vendors evaluated. The buyers
participating in IDC's Artificial Intelligence Services Buyer Perception Survey have partnered with at
©2023 IDC #US49647023e 6
least one of the participating vendors directly on an AI services engagement within their company. The
survey findings highlight key areas where buyers expect AI services providers to showcase a range of
capabilities. The buyers consider these capabilities a must-have for AI services to be able to fulfill the
requirements of many business and IT issues that challenge the buyers.
Figure 3 illustrates the order of factors important for a successful AI services engagement for the AI
services customers surveyed in 2023. Survey findings suggest that the ability to achieve desired
business outcomes by the consulting and delivery teams working on an AI services engagement is the
most critical factor for the successful completion of the engagement. Customers also indicated a
vendor's ability to create quality data sets and pipelines for AI model training, provide quality skills in
and knowledge of AI, provide technical insights and competency, and provide security and governance
of AI algorithms, APIs, and training data to be among the most critical attributes for an engagement's
success.
©2023 IDC #US49647023e 7
FIGURE 3
Top 10 Factors for Successful Artificial Intelligence Services Engagements, 2023
Q. In order for an AI services engagement to be successful, please indicate the importance of
each of the following characteristics.
n = 116
Note: Mean scores are based on a scale of 1–5, where 1 is highly detrimental to success and 5 is essential to success.
Source: IDC's Artificial Intelligence Services Buyer Perception Survey, 2023
©2023 IDC #US49647023e 8
LEARN MORE
Related Research
▪ Artificial Intelligence Services Findings from Enterprise Intelligence Services Survey, 2022
(IDC #US49230423, January 2023)
▪ IDC FutureScape: Worldwide Artificial Intelligence and Automation 2023 Predictions (IDC
#US49748122, October 2022)
▪ Market Analysis Perspective: Worldwide Analytics and Intelligence Automation Services, 2022
(IDC #US48206022, September 2022)
▪ Worldwide Artificial Intelligence Services Forecast, 2022–2026 (IDC #US48206222, August
2022)
▪ Worldwide and U.S. Artificial Intelligence Services Market Shares, 2021: Adapting to Evolving
Client Needs (IDC #US48206622, August 2022)
▪ IDC's Worldwide Services Taxonomy, 2022 (IDC #US47769222, July 2022)
▪ IDC MarketScape: Worldwide Artificial Intelligence Services 2021 Vendor Assessment (IDC
#US46741921, May 2021)
Synopsis
This IDC study represents a vendor assessment of the artificial intelligence (AI) services market
through the IDC MarketScape model. This assessment discusses both quantitative and qualitative
characteristics that explain success in the AI services market. This IDC MarketScape covers a variety
of vendors participating in the AI services space. The evaluation is based on a comprehensive and
rigorous framework that assesses vendors relative to the criteria and to one another and highlights the
factors expected to be the most influential for success in the market in both the short term and the long
term.
"With rising public awareness of AI capabilities, spurred most recently by the ability to interact with
free, web-based generative AI tools, organizations are feeling pressure to move faster to incorporate
AI into digital business strategies or risk being left behind by competitors," says Jennifer Hamel,
research director, Analytics and Intelligent Automation Services at IDC. "Successful AI services
providers continue to evolve their portfolios to meet ever-evolving client needs while remaining trusted
advisors to cut through hype and hysteria, set reasonable expectations for what AI can and should do
for their businesses, and develop road maps for adopting and managing AI solutions at scale."
©2023 IDC #US49647023e 9
About IDC
International Data Corporation (IDC) is the premier global provider of market intelligence, advisory
services, and events for the information technology, telecommunications and consumer technology
markets. IDC helps IT professionals, business executives, and the investment community make fact-
based decisions on technology purchases and business strategy. More than 1,100 IDC analysts
provide global, regional, and local expertise on technology and industry opportunities and trends in
over 110 countries worldwide. For 50 years, IDC has provided strategic insights to help our clients
achieve their key business objectives. IDC is a subsidiary of IDG, the world's leading technology
media, research, and events company.
Global Headquarters
140 Kendrick Street
Building B
Needham, MA 02494
USA
508.872.8200
Twitter: @IDC
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www.idc.com
Copyright and Trademark Notice
This IDC research document was published as part of an IDC continuous intelligence service, providing written
research, analyst interactions, telebriefings, and conferences. Visit www.idc.com to learn more about IDC
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Copyright 2023 IDC. Reproduction is forbidden unless authorized. All rights reserved. |
5 | ey | ey-the-aidea-of-india-2025-how-much-productivity-can-genai-unlock-in-india.pdf | How much
productivity
can GenAI
unlock in India?
The AIdea of India: 2025
The AIdea of India: 2025 1
2 The AIdea of India: 2025
stnetnoC
Foreword
Chapter 1
Generative AI:
Shaping tomorrow
Executive
summary
Chapter 2
Pivoting to
AI-first digital
transformation
40
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Chapter 3
Chapter 6
Transforming work
Policy agenda
with GenAI
for India
Chapter 4
Industries in
transformation Annexures
Chapter 5
Government
Services
The AIdea of India: 2025 3
84
:on
egaP
85
:on
egaP
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611
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421
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egaP
Foreword
Over the past few years, innovation possible to use open-source models for as
in Generative AI (GenAI) has low as a few thousand rupees a month
progressed at an extraordinary in India.
pace, reaffirming its transformative
Yet, amidst all this innovation, enterprise
potential across a number of domains.
adoption rates of GenAI remain very low.
The possibilities are vast and hold the
Our survey shows that 36% of Indian
promise of profound changes on the
enterprises have allocated budgets and
horizon for millions of Indian citizens.
begun investing in GenAI, while another
Technical breakthroughs have been jaw 24% are testing its potential. Technology
dropping. We have quickly moved from sector clients are leading the way, with
auto-complete chatbots to reasoning Life Sciences and Financial Services
machines capable of spinning out following suit. Despite this, the business
credible, human like ‘Chains of Thought’ value remains limited, with just
(CoT) to find solutions to complex 15% having GenAI workloads in
problems. Today, multi-modal large production and only 8% able to fully
language models (LLMs) can enable measure and allocate AI costs.
seamless processing of text, audio,
This is not surprising – it takes time for
image, and video. Emerging trends like
innovation to be packaged and made
Agentic AI are enabling autonomous
ready for enterprise adoption. Enterprises
entities capable of taking actions. The
need clarity on ROI and guarantees
evolution of new hardware platforms
around issues like hallucination, data
and new AI accelerators has ensured
privacy and algorithmic bias as they craft
the computational power to support
their digital transformation roadmaps.
increasingly sophisticated models,
Over the next few years, we expect an
having even a trillion parameters and
explosion of enterprise adoption as these
groundbreaking efficiency.
issues are addressed and AI and GenAI
Along the way, the cost of intelligence models make their way into the
has fallen, driven by the open-source enterprise mainstream.
movement and the trend to use purpose
Just as during the earlier era of mobile
specific small language models (SLMs).
disruption, fintech and healthtech
This is making AI accessible to smaller
enterprise adoption will lead to the birth
businesses and very soon it may become
4 The AIdea of India: 2025
of AI-first companies with new business potential as the use case and data capital
models and revamped economics. These of the world. The focus will need to be on
firms will compete with digital interfaces enhancing data accessibility and compute
powered by chat, voice and regional infrastructure, fostering AI research
language models. Algorithms and new and innovation through initiatives like
datasets will help drive population-scale localized LLMs, and addressing challenges
operations. AI-driven apps will transform in responsible governance, intellectual
knowledge work. property rights, and data protection.
On the other hand, we need to address
The coming wave of change has
the coming potential job dislocation in
significant implications for India.
the workforce by implementing
In industries like financial services,
aggressive skilling programs and
healthcare and retail, we expect AI
apprentice schemes.
to reshape basic processes including
customer acquisition, operations and This report is an in-depth exploration
service. Industries including IT/ITeS and of GenAI’s current state in Indian
BPO will undergo more dramatic changes. enterprises, key trends shaping its
Next-generation industries like biotech, future, and implications for Indian
advanced manufacturing and renewables enterprises and policymakers.
will have the potential to leapfrog to
I hope you find this report valuable
AI-first business models.
- happy reading!
Our analysis reveals that, at a macro
level, the AI platform shift will impact
38 million employees, potentially
driving a 2.61% boost in productivity
by 2030 in the organized sector.
Enterprises will need to reorient Rajiv Memani
themselves rapidly to deal with this
Chairman and CEO,
coming impending tides of change.
EY India
There will also be significant pressure
on India’s policy agenda. On one hand,
there is the imperative to realize India’s
The AIdea of India: 2025 5
6 The AIdea of India: 2025
Executive
summary
Th e entire earth will be be paired with practical applications that solve
converted into a huge real-world problems, empower users, and bridge gaps in
brain, as it were, capable digital access and infrastructure.
of response in every one of its
parts.” This was Nikola Tesla, in
1904, predicting the impact of Innovation in GenAI continues at
the radio on the world.
a scorching pace
Every generation believes
it stands on the brink of
Innovation in GenAI surged in 2024, marking a
transformation, fueled by the
transformative year for the technology.
technologies of its time. Today,
as we contemplate the AI era, There was rapid progress in Multimodal AI, integrating
it feels like one of those pivotal text, images, audio and video into unified models that
moments. On one hand, there significantly enhance real-world usability. This was
is exponential innovation — particularly evident in the incorporation of these models
AI’s promise is vast, with the into AI-powered phones and emerging form factors like
potential to revolutionize smart glasses, enabling seamless and intuitive interactions
industries, redefine work, across diverse applications.
and unlock unprecedented
The open-source movement gathered steam. Leading
creativity and productivity.
open-source large language models (LLMs) such as
Breakthroughs in GenAI have
Meta’s Llama 3 and Mistral Large set new benchmarks for
been astounding, and the
performance while addressing critical concerns about data
possibilities appear limitless.
privacy and security. Simultaneously, there was a growing
Yet, there is the critical challenge realization that smaller, domain-specific models could
of making this transformation often outperform their larger counterparts in
relevant and accessible to targeted tasks.
consumers and enterprises. For
Year 2024 also saw breakthroughs in reasoning. Models
AI to truly deliver on its promise,
such as OpenAI’s GPT-4o31, and Google’s AlphaProof2
cutting-edge innovation needs to
1. https://openai.com/index/deliberative-alignment/
2. https://www.ebi.ac.uk/training/online/courses/alphafold/inputs-and-outputs/a-high-level-overview
The AIdea of India: 2025 7
GenAI in India: The current state of play
EY India’s C-suite GenAI survey
We conducted an in-depth GenAI survey covering more than 125 C-suite executives across India. They
represent diverse sectors, including Financial Services, Retail, Life sciences, Media and Entertainment,
Technology, Automotive, Industrials and Energy.
GenAI journey GenAI strategy: Direction and alignment
Integration with existing software means enterprises’ More than half of the enterprises have a GenAI strategy
exposure to GenAI is high. However, only a few have the but only some have a fully integrated strategy with clear
technology in production. execution plans
Fully integrated strategy
12% 22% with clear execution plans
36%
11% Strategy aligned with
business goals, but
34% 15% 18% execution plans are lacking
8% Strategy exists, but not
30% 9% aligned with business
goals
Basic understanding, no
POCs completed Productionalization in progress formal strategy
POCs in progress AI adopted
No POCs done No clear impact 39% No clear strategy
Architecture: GenAI platform and Implementation: Buy versus build
integration approach approach
Architecture integration is limited and enterprises are Approximately one in four have defined approach but
looking at ways to increase application application is uneven
11% Fully integrated and 16% 10% Well-defined and
19% optimized platform c ao pn ps rois at ce hntly applied
Integrated architecture in
4% place; facing utilization Defined approach, but
challenges not consistently applied
Platform selected; Preliminary approach, not
integration just started fully defined or executed
Platforms identified, but 21% Aware of options, but no
no integrated architecture clear decision framework
43% 23% N ino te p gl ra at tfo ior nm a s pe pl re oc ate cd h / 21% 32% N vso . c bo un ils di d ye er tation of buy
defined
Data: Platform readiness for GenAI Talent: Resource availability for GenAI
adoption adoption
Enterprises in India are at different stages of data AI expertise is a key need for most enterprises as they
readiness, with only a few at a mature level undergo GenAI transformation
3% 16% Fully ready and mature
3% 16% Extensive expertise and
Mostly ready, minor gaps resources for effective
deployment
Partially ready, requires Talent exists but
enhancements insufficient to support all
initiatives
Needs significant
23% improvements Have some skills but need
significant investment
22%
Not ready Aware of skills but lack
them and have no
19% 39% acquisition strategy
Have not thought about
35%
24% specific GenAI skill
requirement
8 The AIdea of India: 2025
GenAI in India: The current state of play achieved remarkable progress in solving GenAI in India: Shaping tomorrow
complex problems across disciplines like science,
mathematics and programming, consistently
India will chart a unique path as this technology
surpassing previous benchmarks. These advanced
evolves. We see five key trends that will
capabilities started to get packaged into agentic AI
significantly influence India’s AI evolution.
systems which aim to independently plan, reason,
and execute tasks by dynamically leveraging tools
and resources. Though still in its infancy, this
agent-driven paradigm promises to fundamentally 01 Chat, voice, regional languages
reshape our understanding of work and the way we augment digital interfaces
design software systems.
Hardware innovations continued to underpin these
02 Agents enable the transformation
advancements in GenAI. NVIDIA maintained its
of knowledge work
leadership with the Blackwell platform, enabling
trillion-parameter models while competitors drove
significant breakthroughs in AI accelerators.
03 LLMs are not all you need:
Toward compound AI systems
Moving from demos and labs to
enterprise grade capabilities
04
The falling cost of AI
Yet, despite these breakthroughs there is also
increasing doubt about the pace and magnitude
of the impact of GenAI. Goldman Sachs, for 05 The evolution of an
instance, has highlighted the imbalance between Indic AI ecosystem
the massive investments being funneled into AI and
the uncertain returns. In a June 2024 report titled
“Gen AI: Too Much Spend, Too Little Benefit?”,
the firm projected that tech giants and other
‘good enough’ for scaling across many use cases.
companies are set to invest nearly US$1 trillion
Our survey of Indian enterprises suggests that
in AI-related expenditures over the coming years,
customer service, operations and sales & marketing
spanning data centers, specialized hardware, and
functions are already leading the way in adoption.
infrastructure upgrades. Despite these staggering
Over the next few years, as these teething issues
sums, the tangible benefits remain elusive.
are addressed, AI and GenAI models make their
Our survey of Indian enterprises suggests that 36% way into the enterprise mainstream across all
of enterprises have budgeted and started investing functions and departments.
in GenAI while another 24% are experimenting with
it. Technology sector clients have been leading
the way with Life Sciences and Financial Services AI augmented interfaces will transform
following suit. At the same time business value
consumer apps
delivered is relatively low with only 15% of Indian
enterprises report having GenAI workloads in AI-powered chat, voice and regional language
production, and just 8% being able to fully measure tools are already making an impact and this trend
and allocate AI costs. will accelerate as digital models diffuse across
the Indian consumer, enterprise and government
This is not surprising. Packaging innovation into
landscape. GenAI native interfaces will also serve
products and services that enterprises can use
as front doors to onboard less digitally savvy users
is a time-consuming process. Enterprises need
into the digital economy. Solutions like NPCI’s Hello!
clarity on ROI and guarantees around issues like
UPI and IRCTC’s AskDisha chatbot demonstrate
hallucination, data privacy and algorithmic bias as
this shift, enhancing inclusivity for underserved
they craft their digital transformation roadmaps.
populations in semi-urban and rural areas.
Rapid advancements to date have already made AI
The AIdea of India: 2025 9
Agents will transform knowledge work A rich Indic AI ecosystem will evolve to
cater to unique Indian needs
The rapid integration of AI Agents into sectors
like information technology, finance, customer There has already been a mushrooming of Indic
service and healthcare will reshape traditional LLMs that leverage open-source models fine-tuned
ways of working, presenting both opportunities with Indian language datasets. A key initiative in
and challenges for Indian professionals. Our this space is Bhashini, a government-led AI project
analysis (more on this in ‘Transforming work with aimed at creating an open-source Indic language
GenAI’) indicates potentially large productivity dataset to expand internet and digital service
improvements that will begin to manifest accessibility in Indian languages. Going forward,
themselves and companies will begin to gear up to AI will increasingly become part of the India stack
help employees manage the coming transition to and available as digital public infrastructure to build
new ways of working. next generation platforms.
A burgeoning GenAI start-up ecosystem and local
Enterprises will start to move to an
AI infrastructure will help drive adoption in
AI-embedded tech stack Indian enterprises.
Enterprises will learn to treat LLMs as but one part
of an evolving AI enabled tech stack. AI adoption
Pivoting to AI-first digital
will accelerate as enterprises integrate LLM
capabilities with classical AI techniques, new transformation
modes of automation and the emerging modern
data stack.
Similar to the transformative impact of the digital
revolution, the accelerating shift toward AI-
AI costs will continue to fall
driven platforms is poised to reshape every factor
influencing a company’s EBITDA. Across Indian
The cost of using AI models has already
enterprises, AI-first approaches are steadily taking
plummeted, making them increasingly accessible to
root, embedding themselves throughout the value
enterprises. OpenAI’s GPT API costs, for example,
chain to enhance operational efficiency and unlock
have dropped nearly 80% in two years, while
new avenues of value creation.
open-source releases like Meta’s Llama are
unlocking new capabilities. This cost is expected At a foundational level, AI automates workflows,
to fall to around INR120 per hour* or lower as detects patterns, and delivers real-time predictions,
India specific LLMs offerings become viable. creating a closed-loop system for continuous
(*Assuming that the cost is US$4 per million tokens learning. This will help companies optimize value
and the application uses 100 tokens per second chains, enhance revenue streams through improved
continuously, the enterprise would spend channels and pricing, and transform delivery with
US$1.44 per hour.) new interfaces.
10 The AIdea of India: 2025
The agenda for enterprises
Reimagine the Rethink the tech Move to AI-ready Getting your Confronting the
business and stack data people ready for AI changing frontier of
operating model risk
A new AI-powered tech stack is emerging, and workforce adaptability. Change management
combining foundational models with specialized bridges the gap between innovation and
tools. Enterprises are increasingly adopting execution, enabling organizations to thrive
SLMs for domain-specific tasks due to their cost in an AI-driven world.
efficiency, precision, and ability to run on edge
AI-first strategies introduce risks related to bias,
devices. Enterprise software providers such as
cybersecurity and explainability. Organizations
SAP, Salesforce and Oracle are embedding AI
are mitigating these by adopting automated
into their platforms, accelerating adoption with
compliance systems, real-time anomaly detection,
ready-to-deploy AI tools. Meanwhile, traditional
and explainable AI models. Regulatory frameworks
Robotic Process Automation (RPA) is evolving
like India’s Digital Personal Data Protection
into intelligent automation by integrating GenAI,
Act (DPDP Act 2023) further emphasize the
enabling systems to adapt dynamically to changes
importance of responsible AI practices, especially in
without manual intervention.
sensitive sectors like healthcare and finance.
A solid data foundation is pivotal to enterprise
AI success. Enterprises are implementing robust
governance frameworks, addressing challenges Transforming work with GenAI
related to data quality, diversity and sensitivity.
Modern data stacks, including cloud platforms
and scalable data lakes, enable real-time ingestion In India GenAI has the potential to drive
and processing, essential for AI implementation. productivity gains, impacting millions of workers
Companies that nurture proprietary datasets and redefining the future of work.
are gaining competitive advantages by achieving
EY conducted a study of over 10,000 tasks in
superior model performance.
critical industries that contribute to the Indian
Preparing people for AI is crucial to unlocking its economy. To assess GenAI’s impact on productivity,
full potential, ensuring both technological adoption tasks were analyzed based on exposure (potential
The AIdea of India: 2025 11
impact of GenAI), complementarity (human due to its higher labor share in gross output, while
oversight needed) and intensity (frequency of tasks manufacturing and construction will see smaller
analyzed in granular time units). A ‘Productivity impacts. However, even in these sectors, AI can
Uplift’ Indicator was created, to quantify drive efficiencies through better capital deployment
this potential impact in terms of Automation and resource utilization, ultimately lowering labour
(elimination of the task), Augmentation expenses and improving overall cost efficiency.
(doing the same task better using GenAI) and
Realizing this potential requires reimagining
Amplification (enhancing the nature of the task
processes, redefining workflows and reskilling
and making it richer).
the workforce. The successful adoption of GenAI
This allowed us to analyze productivity gains at requires clear strategies, piloting use cases, and
job role, functional and organizational levels. scaling solutions, alongside reimagining processes,
Our analysis reveals that 24% of tasks can be redefining KPIs, and targeted reskilling. Large-scale
fully automated, while time spent on another upskilling initiatives, supported by public-private
42% can be significantly reduced, freeing up partnerships and AI-focused training programs, are
8-10 hours per week for corporate workers. This crucial to bridging the skill gap. With investments
translates to a productivity boost of 2.61% by in skills, data and infrastructure, GenAI can drive
2030 in the organized sector affecting 38 million economic productivity and ensure a future-ready
Indian employees and an additional 2.82% in the workforce for India.
unorganized sector. The largest productivity gains
from GenAI are expected in the services sector
Productivity gains across key sectors
This graph illustrates the labor cost as a percentage of gross output on the x-axis and the percentage
productivity improvement through AI on the y-axis. The size of the bubble represents the potential labor
efficiencies created by AI for the industry.
EY India jobs study: Transforming work with GenAI
12 The AIdea of India: 2025
laitnetop
ytivitcudorP
50%
45%
IT | 19%
40%
Retail | 5%
Banking | 9%
35%
Pharma | 2%
Insurance | 8%
30%
Telecom | 5%
Automobile | 2%
25% Metals and Mining | 4%
Healthcare | 13%
20%
Media and Entertainment | 5%
15%
10%
0% 10% 20% 30% 40% 50% 60%
Labor cost by gross output
secnahne
IA
secnahne
IA
yltnacfiingis
ytivitcudorp
yllanigram
ytivitcudorp
Labor plays Labor plays a
a smaller role larger role
A policy agenda for India To ensure Responsible AI, the government has
prioritized transparency, fairness and safety
through consultations and oversight. Plans include
India’s AI policy landscape reflects a balanced
forming a National Committee on Responsible
approach to fostering innovation while ensuring
and Trustworthy AI, addressing bias, privacy and
responsible deployment. The IndiaAI Mission stands
accountability. The DPDP Act requires
at the forefront, with a financial commitment
businesses to adopt privacy-preserving AI tools,
of over INR10,000 crore to develop India’s AI
anonymization protocols, and compliant
ecosystem across seven pillars, including access
workflows, aligning AI development with evolving
to high-quality datasets, expanded compute
data protection standards.
infrastructure, and responsible AI governance. Key
initiatives include establishing the India Dataset India’s strategic AI policies, anchored in inclusivity,
Platform for organized, sector-specific data access, data sovereignty and accountability, aim to position
deploying 10,000 GPUs to scale AI research, the country as a global AI leader while mitigating
and promoting AI solutions in critical sectors like risks, promoting innovation, and ensuring ethical AI
healthcare and agriculture through R&D incentives adoption across public and private sectors.
and innovation challenges.
The AIdea of India: 2025 13
C h a p t e r 1
Generative AI:
Shaping tomorrow
14 The AIdea of India: 2025
Generative AI:
Shaping tomorrow
The AIdea of India: 2025 15
Chapter 1
Generative AI:
Shaping tomorrow
The promise still holds
Multimodal AI advancements,
agent-driven systems, and hardware Over the past few years, innovation in GenAI has
progressed at an extraordinary pace, reaffirming
advancements like NVIDIA’s Blackwell
its transformative potential across a number of
are reshaping global applications,
domains. The possibilities are vast and hold the
moving GenAI from labs to promise of profound changes on the horizon. In
the domain of healthcare, AI could accelerate
enterprise-grade solutions
breakthroughs in biology, enabling the rapid
development of cures for diseases like cancer and
The rise of open-source LLMs and the Alzheimer’s while extending human lifespans. In
neuroscience, it offers hope for understanding
success of smaller, domain-specific
and treating mental illnesses such as depression
models are addressing privacy,
and schizophrenia, while also enhancing human
efficiency, and targeted use-case needs cognition and emotional well-being. Economically,
AI promises to potentially uplift billions out of
poverty by optimizing resource distribution and
The rapidly falling costs of AI
revolutionizing industries like agriculture and
solutions, like 80% drop in the price clean energy. In governance, AI might strengthen
of OpenAI’s APIs over two years, governance by enhancing public services and
reducing corruption. Finally, in education and work,
are making advanced capabilities
AI can democratize knowledge access and redefine
increasingly accessible to enterprises meaningful human contributions, ensuring an
inclusive future where technology enriches, rather
than replaces, human purpose.
India is leveraging GenAI for
regional language accessibility,
But GenAI is not without its
digital inclusivity, and transformative
consumer apps skeptics
As Indian enterprises adopt Yet, as with all transformative technologies, GenAI
has its share of doubters. While its promise is vast,
AI-embedded tech stacks;
concerns about the pace and magnitude of its
start-ups and SaaS companies will impact linger. Goldman Sachs, for instance, has
lead the charge, driving innovation highlighted the imbalance between the massive
investments being funneled into AI and the
and integration across industries
uncertain returns. In a June 2024 report titled
in the coming years “Gen AI: Too Much Spend, Too Little Benefit?”,
the firm projected that tech giants and other
companies are set to invest nearly US$1 trillion
16 The AIdea of India: 2025
in AI-related expenditures over the coming years, Emerging
spanning data centers, specialized hardware, and
trends
infrastructure upgrades. Despite these staggering
sums, the tangible benefits remain elusive. Adding such
to the tempered outlook, MIT economist and Nobel
as Agentic AI and synthetic data
laureate Daron Acemoglu provides a cautious
generation expanded AI’s capabilities
evaluation of AI’s economic impact. His research
suggests that contrary to ambitious forecasts of by enabling autonomous, multi-step
transformative productivity gains, AI may yield
tasks and addressing data scarcity
GDP growth of a more modest 0.93% to 1.16% over
the next decade, with the possibility of reaching
1.56% under optimal conditions. These critiques
underscore the need to balance enthusiasm with
realism, tempering grand visions with practical reasoning and accuracy. Landmark achievements,
assessments of AI’s current capabilities and including Nobel-recognized contributions to
its path forward. protein structure prediction (AlphaFold2) and
industry-specific LLMs for domains like healthcare
and finance, highlighted the technology’s potential.
The year of exponential Global investment in GenAI surged, driven by
tech giants like Google, OpenAI and Microsoft.
breakthroughs
Record-breaking funding rounds and open-source
contributions from Meta and others intensified
Year 2024 proved to be one of phenomenal competition, while advancements in hardware,
advancement in the field of GenAI culminating such as Nvidia’s Blackwell platform, provided the
with the announcement of OpenAI’s o3 class of computational power to support increasingly
models, which promise to offer a quantum leap in sophisticated models. Emerging trends, such
foundational LLM capabilities and reasoning. Earlier as Agentic AI and synthetic data generation,
in the year, the transition to multi-modality allowed expanded AI’s capabilities, enabling autonomous,
seamless handling of diverse data formats, while multi-step tasks and addressing data scarcity.
advancements like expanded context windows and SLMs offered cost-effective solutions for smaller
retrieval-augmented generation (RAG) improved enterprises. Despite concerns like overfitting
and model collapse, GenAI’s strides in reasoning,
multimodality and adaptability cemented its
position as a key driver of innovation and
productivity across sectors.
Every once in a while,
We are still early in the game
a new technology, an
Despite challenges, even today’s innovations in
GenAI offer immense enterprise value. The focus
old problem, and a
is not just on GenAI but also on integrating AI,
data, and automation to build tailored solutions.
big idea turn into an Rapid advancements have made AI ‘good enough’
for scaling across many use cases. Techniques like
innovation RAG and CoT address issues like hallucination,
while guardrails secure data privacy and safety. The
cost of AI has also dropped significantly, promising
Dean Kamen returns on existing investments.
Engineer and entrepreneur
The AIdea of India: 2025 17
Human-like adaptability of AI Agents
AI Agents operate, within an enterprise context, to achieve specific goals. They can be instructed in natural
language and act autonomously on behalf of users. Users specify objectives in terms of ‘what’ or task
goals, leaving the AI agent to figure out ‘how’ this is to be accomplished using available tools. An agentic
architecture represents a fundamentally new approach to building computer systems. If successful, it
signifies a leap forward as the focus is on outcomes rather than processes.
A key innovation is that much of the control logic in an AI Agent is driven by LLMs. This approach introduces
dynamic, non-deterministic behavior – similar to human decision-making – with its associated benefits and
challenges. Decision making, with AI agents, is no longer limited to rigid programming. Agents can adapt to
contexts and improve outcomes dynamically.
Applications of AI agents across contexts
Personal assistants Reasoning Agents
Advanced personal assistants, such as Apple’s OpenAI’s O1 (and now O3) models exemplify
AI-driven assistant, showcase how AI can AI’s growing reasoning capabilities. Using
handle complex, context-dependent queries. chain-of-thought methodology, O1 formulates
For instance, when asked about a family step-by-step plans to solve problems, improving
member’s flight arrival and dinner plans, the both accuracy and transparency. Users, too,
assistant seamlessly integrates information can trace the model’s logic, identify errors and
from emails, messages, maps, calendars and make corrections. Notably, O1 has achieved over
third-party apps. These systems build a semantic 80% accuracy in solving complex mathematical
model of the user, which enables navigation problems, marking a substantial advancement
across applications to respond accurately. As over previous models. Reasoning agents
AI becomes more embedded in devices and highlight the potential for AI to bring clarity and
productivity tools, personal assistants are poised reliability to intricate problem-solving tasks.
to adeptly manage digital lives, streamlining user
interactions and enhancing productivity.
Functional Agents Agents in the real world
Salesforce’s Agentforce platform brings Anthropic’s research on AI Agents emphasizes
agentic architectures into the enterprise realm. their ability to interact dynamically with the
These autonomous AI Agents personalize world to accomplish tasks and learn from
customer interactions, streamline support and those interactions. This vision extends beyond
orchestrate actions across multiple channels. the digital realm to where agents can control
This innovation shifts traditional business physical tools, robots or laboratory equipment,
models toward outcome-based pricing – where or even design equipment for specific tasks.
costs are tied to completed tasks rather than However, such dynamic systems bring
per-user licenses. Such a model aligns software challenges in ensuring safety, reliability, and
costs more closely with business outcomes, predictability – an essential focus
offering enterprises a flexible and value-driven for developers.
approach.
18 The AIdea of India: 2025
However, adoption remains low. Our survey of 02Open source LLMs
Indian enterprises suggests that 36% of enterprises
have budgeted and started investing in GenAI
The emergence of open source LLMs (OS LLMs)
while another 24% are experimenting with it.
from organizations like Meta and Mistral intensified
Technology sector clients have been leading the
competition, prompting closed-source providers
way with Life Sciences and Financial Services
such as Anthropic and OpenAI to enhance
following suit. At the same time business value
their offerings to justify premium pricing. For
delivered is relatively low with only 15% of Indian
instance, DeepSeek v3 has been able to surpass
enterprises report having GenAI workloads in
OpenAI’s GPT-4o in performance across several
production, and just 8% being able to fully measure
industry benchmarks.
and allocate AI costs. The survey highlights the
need for packaged solutions to bridge the gap and The shift also benefited hardware providers like
accelerate adoption. As innovations mature, they NVIDIA. Demand for GPUs expanded to include
will drive a new wave of digital transformation, organizations deploying OS LLMs privately, leading
unlocking extraordinary business benefits. At them to invest in NVIDIA hardware to run models
the same time, global trends positively influence like Meta’s Llama 3.1 405B internally,
GenAI developments in India through collaboration, rather than relying on API-based access to
investment and research. closed-source models. This diversification of |
6 | ey | catalyzing-economic-growth-through-ai-investment.pdf | Catalyzing economic
growth through capital
investment in GenAI
Catalyzing economic growth through capital investment in GenAI
1
Economic impact of AI: This EY-Parthenon macroeconomic article series
provides insights on the economic potential of GenAI and actionable
considerations. Discover more
In this installment, we delve into the realm of capital investment in generative AI
(GenAI). As GenAI has emerged as one of the key components of economic impact,
business leaders today find themselves at a crossroads. The October 2023 EY CEO
survey indicates a striking dilemma: while a significant 62% of business leaders
acknowledge the urgency of acting on GenAI to prevent competitors from gaining
Gregory Daco a strategic edge, an almost equal percentage (61%) express reservations due to the
EY-Parthenon Chief Economist uncertainties surrounding the formulation and execution of an AI strategy.
New York, NY
The survey further reveals an “adoption paradox.” It highlights that two-thirds of
organizations that have successfully launched at least one AI initiative anticipate
that AI will revolutionize their entire business and operational models within a mere
two-year span. In contrast, organizations with more extensive AI experience, defined
as those having completed five or more AI-related initiatives, project a more cautious
timeline of three to five years for AI to wield similar transformative effects.
This disparity in expectations underscores the presence of ‘“unknown unknowns” in
AI adoption, particularly in determining the nature and extent of capital investment
required for laying a robust AI foundation.
Catalyzing economic growth through capital investment in GenAI
2
In assessing the potential economic impact of GenAI from a capital investment
perspective, we examined the near-term boost to growth from increased investment
in research and development, infrastructure, software creation and company
adoption. Drawing parallels with the IT revolution in the period of 1980-2000, our
two main findings are:
• Significant boost to demand: Assuming trend growth around 8.5% in
investment categories where GenAI will be most significantly captured, we
estimate that capital investment in GenAI will contribute about 0.1 percentage
points (ppt) to US GDP growth annually over the next five years. Our baseline,
however, is that business investment will likely be 25% faster, leading to an
incremental boost to short-term growth of 0.1 percentage points of GDP per
year, worth over $150bn after five years. A more optimistic scenario could see
50% faster business investment growth, leading to an incremental boost to short-
term growth of 0.2ppt of GDP per year, worth a cumulative $325bn by 2028.
• Long-term boost from supply: In our baseline where business investment
is 25% faster than the current trend growth, the potential growth rate of the
economy would rise by 0.1ppt per year in the 2028-2033 period, lifting real GDP
by nearly 1% over the baseline by 2033, or the equivalent of a $250bn boost
over a decade. Assuming capital investment in AI technology grows 50% faster
than the 2017-2022 trend pace over the next five years, the annual capital
contribution to long-term GDP growth in the 2028-2033 period would rise by
0.2ppt. This stronger tech-driven trajectory would lift real GDP by more than 2%
over the baseline by 2033, or the equivalent of a $500bn boost over a decade.
Percent Billions (USD, 2017)
US Real GDP boost from 1.4 350
GenAI investment
1.2 300
1.0 250
0.8 200
0.6 150
0.4 100
0.2 50
0.0 0
Baseline — Optimistic — Baseline — Optimistic —
Boost Boost Cumulative Cumulative
per year per year� boost by 2028 boost by 2028
Source: Bureau of Economic Analysis; EY-Parthenon
Additional chart notes: This chart shows the GDP boost from GenAI investment on an average annual basis between
2023 and 2028 as well as the cumulative boost over the same time frame; both include baseline and optimistic
scenarios. Baseline assumes business investment in categories where GenAI will be most significantly captured
is 25% faster than trend growth; optimistic assumes business investment in categories where GenAI will be most
significantly captured is 50% faster than trend growth. Data from this chart is discussed in the article
Catalyzing economic growth through capital investment in GenAI
3
Looking across major economies, the contributions from greater GenAI investment
could also be significant. While the US market is likely to remain the leader in GenAI
technologies investment, China and Europe will be following closely behind. We
estimate that the lift to global GDP could total between $300bn and $600bn over the
next five years. The boost to global potential GDP could amount to between $500bn
and $1tn over the next decade.
In this installment of our “Economic impact of AI” series, we will focus on the
business investment and capital accumulation dimension and leave the productivity
dimension of accelerating processes, optimizing operations and unlocking new
capabilities to the next article in our series.
We will discuss investment in GenAI and associated capital accumulation by taking a
deeper look at the following:
• Back to basics: demand and supply
• The demand perspective: near-term contribution of capital investment in GenAI
to GDP
• The supply perspective: a strong capital foundation to promote more sustainable
growth
1. Back to basics: demand and supply
Capital investment in GenAI can spur stronger capital
accumulation and productivity, boosting the global
economy’s growth rate.
In an era where technological innovation is the cornerstone of economic prowess,
GenAI has the potential to reshape the contours of businesses and the broader
economy. This installment delves into the burgeoning role of increased capital
investment in AI, underscoring its potential to be a significant driver of near-term
economic growth.
It’s important to consider that GenAI investment is not just a technological upgrade
but a strategic economic lever to redefine business models, markets, industries and
the very fabric of the global economy. By dissecting the dynamics of AI investment,
we aim to unveil how it can propel economic activity, observed through the dual
prisms of demand and supply.
From the demand perspective, investment in GenAI is seen as a new frontier for
capital allocation, influencing various sectors from health care to finance, and
energizing them with innovative capabilities. The investment fuels the industries it
permeates, leading to an uptick in overall economic activity and consumer demand.
On the supply side, investment in AI will be a catalyst for stronger capital
accumulation as well as productivity growth, lifting the global economy’s potential
growth rate.
Catalyzing economic growth through capital investment in GenAI
4
As we noted in the first installment of our series, prior general-purpose
technologies have had a significant impact on economic activity, but that impact
has generally lagged.
Some of the main reasons for that lag are implementation and diffusion delays,
learning and adjustment periods due to the time it takes to effectively use new
technologies and delays in the development of complementary innovations or
infrastructure for the technology to be fully effective.
To establish GenAI as a cornerstone of modern industry, substantial capital
investment may be required.
• Research and development (R&D): Building and refining AI models necessitate
a significant influx of resources. The data-intensive nature of GenAI calls
for investment in gathering, storing and processing data, as well as in the
computational power needed to train sophisticated models.
• Infrastructure providers: Investment in the physical and digital infrastructure
necessary to support AI technologies forms another cornerstone of this economic
transformation. This encompasses everything from data centers to advanced
networking capabilities and even cybersecurity. The adequacy of this infrastructure
directly impacts the efficiency and effectiveness of AI solutions.
• Software creation: The investment in AI applications across various business
sectors is perhaps the most visible aspect of AI’s economic influence. From
finance to manufacturing, AI applications are revolutionizing traditional business
processes, enhancing customer experiences and opening new revenue streams.
These investments are not merely about automating routine tasks but are
also about leveraging AI to uncover insights, predict trends and create more
personalized and efficient services.
• Corporate adoption: It’s essential for businesses to invest in integrating GenAI into
their operations. This includes not only the technology itself but also the training
of personnel and restructuring of processes to fully capitalize on AI’s potential. The
widespread adoption of AI by businesses could have a notable economic impact
as it leads to increased operational efficiencies, reduced costs and enhanced
competitive capabilities. Moreover, as AI becomes more ingrained in business
operations, it will likely drive the demand for skilled workers and AI-related
services, and, consequently, it will probably stimulate job creation and economic
activity in related sectors.
Catalyzing economic growth through capital investment in GenAI
5
2. The demand perspective: near-term contribution of
capital investment in GenAI to GDP
Rising capital investment in GenAI is positioned to
increase quickly and prompt GDP growth.
In assessing the potential economic impact of GenAI from a demand perspective, it is
instructive to draw parallels with the investment dynamics of previous technological
revolutions. In the early 1990s, business investment in information processing
equipment and software totaled about 3% of GDP, or $155 billion.
As businesses invested in the physical and human infrastructure necessary to
support, implement and reshape business processes in the computer age, that
share of investment rapidly grew to 4.5% of GDP, or $400 billion by the early 2000s.
Percentage points
US business investment in
9.0
Historical Forecast
GenAI as a share of GDP
(percent)
8.0
7.0
6.0
5.0
4.0
3.0
2.0
8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
200 200 201 201 201 201 201 201 201 201 201 201 202 202 202 202 202 202 202 202 202
Optimistic — real Optimistic — nominal
Baseline — real Baseline — nominal
Trend — real Trend — nominal
Source: Bureau of Economic Analysis, EY-Parthenon; author’s calculation
Additional chart notes: Trend refers to trend growth around 8.5% per annum in investment categories where GenAI
will be most significantly captured. Optimistic assumes business investment is 50% faster than trend growth.
Catalyzing economic growth through capital investment in GenAI
6
We are likely on the cusp of a similar trend with GenAI, where burgeoning investment
in AI technology is poised to increase rapidly and boost GDP growth. Specifically, we
isolated the following investment categories likely to capture AI technology:
• Software
• R&D in semiconductor and other electric components manufacturing, other
computer and electronic product manufacturing, scientific services and
software publishers
• Computers and peripheral equipment
• Communication equipment
Scenario analysis
The categories where new investment in GenAI will be most significantly captured
totaled about $750 billion in 2017, or about 3.8% of real US GDP. By 2022,
investment had grown to just over $1.1 trillion, or about 5.2% of GDP.
• Trend growth: Assuming trend growth in line with economic momentum from
2017 to 2022, investment would be expected to grow around 8.8% per year from
2023 to 2028 and represent 7.6% of real GDP by 2028, or $1.9 trillion. While this
would mean that investment in AI technology would contribute about 0.4ppt to
GDP growth per year, it would not represent an increase in the growth contribution
relative to recent past.
• Baseline expectations: If, instead, we assume that nominal capital investment in
AI technology grows 25% faster than the 2017-2022 trend pace over the next five
years, then investment represents 8.1% of real GDP by 2028, or $2 trillion. This
would translate into an incremental contribution of GenAI technology investment
to GDP growth of 0.1ppt per year (for a total contribution of 0.5ppt) and, by 2028,
a boost to real GDP worth $150bn, or 0.6%.
• Reason for optimism: Still, there may be reason to be even more confident
about the outlook. Assuming capital investment in AI technology grows 50%
faster than the 2017-2022 trend pace over the next five years — which is akin
to the acceleration in business investment in information processing equipment
and software in the late 1990s — then investment would grow about 11% annually
from 2023 to 2028 and represent 8.7% of GDP by 2028, or $2.1 trillion. This
would constitute an incremental short-term contribution to GDP growth of 0.2ppt
per year (for a total contribution of 0.6ppt) and, by 2028, a boost to real GDP
worth $325bn, or 1.3%.
The potential uplift to global GDP from increased GenAI investment could also
be substantial. With the US expected to continue leading in GenAI technology
investment, closely followed by Europe, Japan and China, global GDP could see
an augmentation of between $300 billion (in our baseline scenario) and $600
billion (in the optimistic case) over the next five years. This significant boost would
reflect the accelerated adoption and integration of GenAI technologies across major
economies, underlining the transformative impact of AI.
Catalyzing economic growth through capital investment in GenAI
7
3. The supply perspective: a strong capital foundation
to promote more sustainable growth
Past tech disruptions and our scenario analysis
provide a case for optimism about GenAI’s ability to
drive long-term growth.
At the heart of AI’s transformative potential on the supply side of the economy is
its capacity to drive greater capital accumulation and stronger productivity growth.
Capital investment in AI is not just an expenditure; it’s a strategic allocation of
resources that acts as the foundation for developing and deploying AI solutions and
seeds future productivity enhancements.
While we will delineate the long-term growth implication from GenAI-driven
productivity growth in a subsequent article, we believe it is essential to dissect the
impact of greater capital accumulation first.
Capital accumulation in AI involves investing in various components such as AI
models (through building and refining), physical and digital infrastructure, software,
AI applications, and AI integration and adoption. Just like physical capital, these
investments in AI technologies act as the foundation that allows for stronger
economic potential.
Capturing longer-term impact from greater capital
investment in AI technology
The surge in business investment in information processing equipment and software
through the 1990s did not just lead to a direct boost to GDP growth, but it also led to
increased capital accumulation that then supported stronger long-term GDP growth.
To put things in perspective, the US economy’s potential GDP growth rate was
estimated to be around 2.5% from 1990 to 1995, but subsequently it accelerated
to 3.8% in the 1995-2000 period. Taking all drivers of growth into consideration,
the capital contribution to potential GDP growth nearly doubled from 0.7ppt in the
early 1990s to 1.3ppt in the 1995-2000 period. At the same time, the contribution
of productivity also rose from 1.1ppt to 1.7ppt from 1995 to 2000 and remained
elevated around 1.5ppt from 2000 to 2005.
Catalyzing economic growth through capital investment in GenAI
8
This confirms our findings from our first installment, which indicated a five- to 10-
year delay between the development of new technologies and their more sustainable
impact on productivity and growth potential.
Percentage points
US Average annual 4.0
contribution to real
3.5
potential GDP growth
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1990–1995 1995–2000 2000–2005
Labor and productivity Capital Potential GDP
Source: Bureau of Economic Analysis, EY-Parthenon; author’s calculation
Additional chart notes: Trend refers to trend growth around 8.5% per annum in investment categories where GenAI
will be most significantly captured. Baseline assumes business investment is 25% faster than trend growth
Optimistic assumes business investment is 50% faster than trend growth
Scenario analysis
Using the same three scenarios, which analyzed the potential short-term economic
impact of greater capital investment in AI technologies, we can infer the likely boost
to potential GDP growth in the five years from 2028 to 2033.
• Trend growth: Assuming business investment in AI technology continues to grow
in line with its moderate 2017-2022 trend, the annual capital contribution to
long-term GDP growth in the 2028-2033 period would likely be around 0.5ppt.
• Baseline expectations: If, instead, we assume that capital investment in AI
technology grows 25% faster than the 2017-2022 trend pace over the next five
years, the capital contribution to long-term GDP growth in the 2028-2033 period
would rise from 0.5ppt annually to 0.6ppt — thereby lifting the potential growth
rate of the economy by 0.1ppt. This may appear to be a small difference, but by
lifting the economy’s potential growth rate, this stronger tech-driven trajectory
would lift GDP by nearly 1% over the baseline by 2033, or the equivalent of a
$230bn boost over a decade ($360bn in nominal terms).
• Reason for optimism: As we noted earlier, there is reason to be more confident
still about the potential capital accumulation contribution to long-term growth.
Assuming capital investment in AI technology grows 50% faster than the 2017-
2022 trend pace over the next five years — which is akin to the acceleration in
business investment in information processing equipment and software in the
late 1990s — the capital contribution to long-term GDP growth in the 2028-2033
period would rise from 0.5ppt annually to 0.7ppt, thereby lifting the potential
growth rate of the economy by 0.2ppt. This stronger tech-driven trajectory would
lift real GDP by nearly 2% over the baseline by 2033, or the equivalent of a $475bn
boost over a decade.
Catalyzing economic growth through capital investment in GenAI
9
In the long run, the potential upside to global GDP from greater capital investment
could be quite significant. How significant? Factoring stronger investment in Europe,
Japan and China and slower investment across emerging markets, we estimate a
boost to potential GDP growth worth between 0.5% and 1% by 2033, representing
between $500bn and $1tn.
Percentage points
US average annual capital 0.8
contribution to real potential 0.7
GDP growth
0.6
0.5
0.4
0.3
0.2
0.1
0.0
2020–2025 2025–2030 2030–2033
Trend Baseline Optimistic
Source: Bureau of Economic Analysis, EY-Parthenon
Additional chart notes: Trend refers to trend growth around 8.5% per annum in investment categories where GenAI
will be most significantly captured. Optimistic assumes business investment is 50% faster than trend growth.
Catalyzing economic growth through capital investment in GenAI
10
Breakdown of AI capital investment across sectors
When thinking about the sector-specific benefits from the GenAI revolution, we
often omit the investments that may be required to shift how industries operate.
By fostering innovation, enhancing productivity and creating new markets and
opportunities, the capital investments described above may be instrumental in
driving potential GDP growth.
Retail sector: AI’s role in retail is multifaceted, ranging from personalized shopping
experiences to inventory management. Capital investments in AI enable retailers to
better understand consumer behavior, optimize supply chains and enhance customer
service, leading to increased sales and market expansion. This sectoral growth is a
key contributor to overall economic development because it could boost retail sector
productivity while also stimulating consumer spending, a major component of GDP.
Health care sector: Investment in AI within health care is revolutionizing patient
care and medical research. AI-driven tools are being used to enhance diagnostic
precision, streamline patient treatment plans and personalize health care services.
This not only improves health outcomes but also helps optimize resource utilization,
reducing costs and contributing to economic growth. Additionally, AI in health care
is spearheading innovations in drug discovery and disease prediction, opening new
markets and avenues for growth.
Automotive industry: The automotive sector’s investment in AI is pivotal in
advancing the development of autonomous vehicles. This not only transforms the
concept of transportation but also stimulates investment in adjacent industries
like logistics and urban planning. The ripple effects of such advancements could
contribute significantly to GDP growth by fostering new business models, enhancing
supply chain efficiencies and creating demand in related sectors such as sensor
manufacturing and AI-driven navigation systems.
Manufacturing industry: In manufacturing, AI investment focuses on automation,
predictive maintenance and supply chain enhancement. This not only increases
production efficiency but also improves product quality, reduction of waste and
operational costs. The resultant increase in competitiveness and productivity of
the manufacturing sector could significantly contribute to GDP growth, while also
fostering an ecosystem of innovation and technological advancement.
Financial services: AI investments in financial services are reshaping banking,
insurance and investment sectors through enhanced risk assessment, fraud detection
and personalized financial planning services. This could increase the efficiency and
resilience of financial systems, supporting economic stability and growth.
Energy sector: Investment in AI within the energy sector is pivotal in transforming
how we generate, distribute and consume energy. AI technologies are being
integrated to help optimize energy production, enhance grid management and
facilitate the shift to renewable sources. Additionally, AI applications in predictive
maintenance of infrastructure may further boost economic efficiency. The
innovations driven by AI in the energy sector are crucial in supporting the transition
to a low-carbon economy, promoting sustainable economic development.
Catalyzing economic growth through capital investment in GenAI
11
Five recommendations for business leaders
By focusing on the following areas, stakeholders can better navigate the
complexities of AI capital investments and harness their full potential to drive
meaningful business transformation.
Strategic alignment with business goals
• Insight: It’s essential for AI investments to be closely aligned with the overarching
business goals and objectives. This alignment helps ensure that AI initiatives directly
contribute to the company’s strategic priorities, whether it’s improving customer
experience, optimizing operational efficiency or driving innovation.
• Recommendation: Conduct a thorough analysis to understand how AI can address
specific business challenges or opportunities. Establish clear KPIs to measure the
impact of AI initiatives on business outcomes.
Leveraging data as a strategic asset
• Insight: High-quality, relevant data is the fuel that powers AI systems. The ability of
a business to collect, process and analyze data effectively is a critical determinant
of AI success.
• Recommendation: Prioritize the establishment of a robust data infrastructure and
governance model. This may help ensure data quality, accessibility and scalability
to support AI initiatives.
Acquiring the right talent and partnering
• Insight: Successful AI implementation may require a combination of the right talent,
including data scientists, AI engineers and domain experts.
• Recommendation: Invest in building internal AI capabilities and work with
organizations that can bring the necessary professional skills and knowledge.
Continuous training and development programs are crucial to keep the team up
to date with the latest AI advancements.
Catalyzing economic growth through capital investment in GenAI
12
Fostering a culture of innovation and adaptability
Key contact
• Insight: The fast-evolving nature of AI technology makes it essential for businesses
Gregory Daco to be agile and adaptable.
EY-Parthenon Chief Economist
• Recommendation: Encourage a culture of innovation where experimentation with
New York, NY
AI is supported. This involves fostering an environment where learning from failures
is seen as a stepping stone to innovation, and where employees are encouraged to
think creatively about applying AI to solve business problems.
Understanding and managing risks
• Insight: AI projects come with their own set of risks, including data privacy
concerns, ethical considerations and potential biases in AI models.
• Recommendation: Develop a robust risk management framework that addresses
these challenges. This includes investing in data security, helping ensure compliance
with relevant regulations and implementing ethical AI practices. But for large-
scale transformation to happen, businesses may need to make significant upfront
investment in physical, digital and human capital to acquire and implement new
technologies and reshape business processes.
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7 | ey | ey-how-to-ask-corporate-vendors-the-right-questions-when-it-comes-to-rai.pdf | "How to ask corporate\nvendors the right\nquestions when it comes\nto responsible AI\nAs artificial (...TRUNCATED) |
8 | pwc | ai_adopion_study.pdf | "AI Adoption in the\nBusiness World:\nCurrent Trends and\nFuture Predictions\n1\nApril 2023\nAgenda\(...TRUNCATED) |
9 | pwc | nextgen-survey-2024.pdf | "PwC’s Global NextGen Survey 2024\nVietnam report\nNextGen Vietnam\nSucceeding in an AI-driven wor(...TRUNCATED) |
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