Now decides next:
Getting real about
Generative AI
Deloitte’s State of Generative AI in the Enterprise
Quarter two report
April 2024

deloitte.com/us/state-of-generative-ai

Table of contents
Foreword
Introduction
Now: Key findings
1

Value creation2

Scaling up

3B uilding trust4E volving the workforce

Next: Looking ahead
Authorship & Acknowledgments
About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications
Methodology

2Introduction

Foreword
We have traveled a long way since the Generative AI space race kicked off in November 2022—and
yet, we know we are still at the beginning of this long and exciting transformation. Every day, wetalk with clients about how much there is to focus on in the moment, how explosive the pace ofchange is, and how challenging it can be amid the excitement to take a longer-term view.
We see organizations starting to achieve benefits and move toward a near future where thisearly stage of Generative AI tools is widely dispersed and driving new value. But there are also
some hard realities to deal with as business leaders look to scale and realize the potential of thispowerful technology.
The State of Generative AI in the Enterprise: Getting real about Generative AI captures a new snapshot“We are in the first inning of athousand-inning game and there’s
so much to be figured out.”of this transformative time from the perspectives of nearly 2,000 business and technology leaders,
all from organizations that are actively deploying and scaling Generative AI today. Echoing ourmany clients, from these executives we hear that while excitement persists it may be at its peak as-Chief analytics officer in financial services

leaders come up against cultural challenges, questions about how to manage their workforces, andissues with trust that—at least for now—stand in the way of unlocking Generative AI’s full value.
All told, it is exciting that Generative AI’s potential is beginning to weave its way deeper into thefoundations of how organizations operate and we continue to learn more about emerging leading
practices. Amid those developments, we also continue to see that achieving value with Generative
AI connects hand in hand with keeping humans at the center.
Learn more about the series and sign up for updates at http://deloitte.com/us/state-of-generative-ai.
Nitin Mittal, Costi Perricos, Kate Schmidt, Brenna Sniderman and David Jarvis

3Introduction

Getting real about Generative AI
Is the infatuation phase over? Quarter two of Deloitte’s

Our research shows that organizations are increasingly

Two of the most critical challenges for scaling areglobal quarterly survey found many organizations

prioritizing value creation and demanding tangible

building trust (in terms of making Generative AI

beginning to get down to the serious work of making

results from their Generative AI initiatives. This requires

both more trusted and trustworthy) and evolving

Generative AI’s vast potential a reality.

them to scale up their Generative AI deployments—

the workforce (addressing Generative AI’s potentiallyadvancing beyond experimentation, pilots and proofs

massive impact on worker skills, roles and head count).

This report presents findings from the second in
Deloitte’s ongoing series of quarterly global surveys
on Generative AI in the enterprise. To gain additional
context for our wave two research, we also
conducted a series of in-depth interviews with senior
executives from a broad range of industries.

of concept. Transitioning to large-scale deployments
will increase Generative AI’s impact on the businessand expand its reach to a much larger segment of theworkforce. Successful scaling, in turn, presents a wide
range of challenges, encompassing everything fromstrategy, processes and people to data and technology.

Here we’ll take an in-depth look at all four of theseareas—value, scaling, trust and workforce—to helporganizations move forward more effectively on their
Generative AI journeys. Future survey reports will
focus selectively on other key challenges to successful
Generative AI scaling and value creation.

4

Introduction

Getting real about Generative AI (cont’d)Value creation

Scaling up• The percentage of organizations reporting they were already achieving their expected• Leaders see scaling as essential for creating value, increasing Generative AI’s impact

benefits to a “large” or “very large” extent is 18%–36%, depending on the type ofon the business and expanding the technology’s user base. The scaling phase is when

benefit being pursued.

Generative AI’s potential benefits are converted into real-world value. It’s also, however,• Organizations that reported “high” or “very high” levels of Generative AI expertise

when an organization’s potential concerns can become real-world barriers to success.

are scaling Generative AI much more aggressively—and are achieving their desired• Common areas of concern include data security and quality, explainability of Generative AI

benefits to a much greater degree than others.

outputs, and worker mistrust or lack of familiarity with Generative AI tools.

• Organizations primarily plan to reinvest the savings from Generative AI into innovation• Workforce access to approved Generative AI tools and applications remains quite low,(45%) and improving operations (43%)—addressing the value equation from both sides.

with nearly half of surveyed organizations (46%) reporting they provided approved
Generative AI access to just a small portion of their workforces (20% or less). However,most workers with internet access will have access to public Generative AI tools andcould be using them without consent.

All statistics noted in this report and its graphics are derived from Deloitte’s second quarterly survey, conducted
January – February 2024; The State of Generative AI in the Enterprise: Now decides next, a report series. N (Total
leader survey responses) = 1,982.
Generative AI is an area of artificial intelligence and refers to AI that in response to a query can create text,
images, video and other assets. Generative AI systems can interact with humans and are often built using large
language models (LLMs). Also referred to as “GenAI.”

5

Introduction

Getting real about Generative AI (cont’d)
Building trust

Evolving the workforce• Lack of trust remains a major barrier to large-scale• Most organizations (75%) expect the technology toaffect their talent strategies within two years; 32%of organizations that reported “very high” levels ofGenerative AI adoption and deployment. Two key
aspects of trust we observed are: (1) trust in the quality
and reliability of Generative AI’s output and (2) trustfrom workers that the technology will make their jobs
easier without replacing them.
• Trust issues have not prevented organizations fromrapidly adopting Generative AI for experiments
and proofs of concept, with 60% reporting they
are effectively balancing rapid implementation withrisk management. Trust is likely to become a bigger
issue, however, as organizations transition to largescale deployment. Many reported they are currently
investing significant time and effort into building
guardrails around Generative AI.
• Organizations that reported “high” or “very high” levels of
expertise recognize the importance of building trust in

Generative AI expertise are already making changes.
• The most expected talent strategy impacts are process
redesign (48%) and upskilling or reskilling (47%).
• Generative AI is expected to increase the value ofsome technology-centered skills (such as data analysis)
as well as human-centered skills (such as critical thinking,creativity and flexibility), while decreasing the value ofother skills.

About the State ofGenerative AI in theEnterprise: Wave twosurvey results
The wave two survey covered in this report was fielded
to 1,982 director- to C-suite-level respondents across
six industries and six countries between January
and February 2024. Industries included: Consumer;
Energy, Resources & Industrials; Financial Services;Life Sciences & Health Care; Technology, Media &Telecom; and Government & Public Services. Our
Q2 survey findings are augmented with over 20 executive
interviews. This second report is part of a yearlong
series by the Deloitte AI Institute to help leaders
in business, technology and the public sector track• In the short term, more organizations said they expect
the technology to increase head count (39%) than todecrease head count (22%)—perhaps due to increased
needs for Generative AI and data expertise.

the rapid pace of Generative AI change and adoption.
The series is based on Deloitte’s State of AI in the Enterprise
reports, which have been released annually the past five years.
Learn more at deloitte.com/us/state-of-generative-ai.

Generative AI across numerous dimensions (e.g., input /output quality, transparency, worker empathy) and are
implementing processes to improve it to a much greater
extent than are other organizations.
6Now: Key findings

7

1

Now: Key findings

Value creation
Proving, measuring and communicating value is crucial

Value objectives and priorities for Generative AI can—Therefore, many forward-thinking organizations are

to an organization’s Generative AI journey. In our survey

and should—vary by organization, industry and use case.

implementing Generative AI without specific ROI targets

and interviews, many organizations reported they

Where the technology’s potential impact is strategic

as they realize they can’t afford to get left behind in thiswere increasingly emphasizing the need for Generative

and truly game-changing, the need and latitude for

critical and fast-moving market.

AI initiatives and investments to have clear value

experimentation, learning and innovation are muchobjectives and deliver tangible results, rather than simply

greater (with less emphasis on immediate payback) thanbeing viewed as experiments or learning experiences.

in situations where productivity and cost savings are the

As one executive at a Fortune 500 manufacturing

primary expected benefits.

company noted: “We have a very strict internal rule

Moreover, Generative AI is so new—and advancing sothat if we don’t see value from our Generative AI

quickly—that accurately estimating benefits is muchsolutions, we won’t do it or we won’t scale it.”harder than for an established technology with a proven

That said, there are many ways to define andtrack record.

measure value—especially for a technology with the“Any technology that’s a little over a year old, nobody’stransformational potential of Generative AI. Although

going to have a year’s worth of data to do a backward-

financial return on investment (ROI) is important, value

looking ROI,” said one tech company executive wedrivers such as innovation, strategic positioning andinterviewed. “And with the fundamental and foundational

competitive differentiation can be even more important.

changes Generative AI offers, it’s very hard to even offer
a forward-looking [total cost of operating] or ROI because
there’s so many possibilities of impact and varied ways
to integrate it into your business.”8

Now: Key findings
Organizations are starting to demand tangible
business value from Generative AI, and someare beginning to achieve real-world benefits.
The organizations we surveyed expect Generative AI todeliver a broad range of benefits, with the most common
objective—at least in the short term—being improved
efficiency and productivity (56%), which is consistent withthe results from last quarter’s survey. The percentage of
respondents who said their organizations’ Generative AI
initiatives were already achieving expected benefits to a“large” or “very large” extent is 18%–36%, depending on
the type of benefit being pursued.

Generative AI “experts” are achieving their desired
benefits to a much greater degree.

As one public sector executive told us, “The big selling
point is if I make an investment and do something like
this, what’s the tangible return and what are some easy
returns? And then what are more complicated longer-term
returns that take more investment money? If I can do someof the easier ones and build on them, it can translate into
‘I think this would be worth it to invest a lot more money.’ Ibelieve a lot of entities in our sector are at that point.”

In every category, organizations that rated themselves
as having “high” or “very high” levels of Generative AI
expertise reported much greater success at achieving their
desired benefits. Their advantage was greatest in strategic
and growth-related areas such as improving products andservices and encouraging innovation and growth.

Achieving benefits
Of those seeking the benefit, the percentage of respondents achieving the benefit to a large extent or more

Very high expertise

70%

63%

54%

55%

48%

48%

48%

40%

36%

42%

Overall

28%

35%

27%

18%

36%

30%

25%

29%

30%

22%

Improve existingproducts
and services

Encourage
innovationand growth

Improveefficiency andproductivity

Reducecosts

Increase speed /ease of dev new
systems / software

Uncover new
ideas andinsights

Increaserevenue

Enhancerelationships withclients / customers

Detect fraud /manage risk

Shift workers
from lower- tohigher-level tasks

Figure 1

Q: What are your anticipated benefits and to what extent are you achieving those benefits to date?
(Jan./Feb. 2024); N (Total) = 1,982; N (very high) = 96

9

Now: Key findings“Expert” organizations are scaling Generative AI
much more aggressively.

According to our survey, organizations reporting “veryhigh” levels of Generative AI expertise are deploying AI
much more rapidly and extensively than others. In fact,
73% said they are adopting the technology at a “fast”
or “very fast” pace (versus only 40% of organizations
with “some” level of expertise). They are also scaling
Generative AI at higher rates across functions and using
it more within functions. For example, those with “veryGenerative AI expert organizations are likely having
more success at capturing benefits because they arescaling up much more aggressively, compared to theother categories, which provides a larger base forgenerating benefits.

high” expertise reported, on average, implementing
at scale in 1.4 functions, out of eight total functions,while those with “some” expertise are doing so in only
0.3 functions. Further, 38% of those with “very high”
expertise reported implementing Generative AI atscale in marketing, sales and customer service—versus only 10% of organizations with “some” level ofexpertise.

Companies that report expertise are moving quickly.
80%

73%

66%
40%

62%

61%

48%
33%

64%
47%

39%

34%

23%

Adopting at afaster pace
Adopting Generative AI“fast” or “very fast”

Figure 2

Providing more of theirworkforce access to
GenAI>40% of workforce hasaccess to Generative AI
tools / applications

19%

Adopting at higher levels
across functions

Investing more in
hardwareInvesting more incloud consumption

Using code
generators more

Using open-source
LLMs more

Implementing Generative
AI for marketing, sales
and customer service

Increasing hardware
investment because ofGenerative AI strategy

Increasing cloudinvestment because ofGenerative AI strategy

Currently using Generative
AI code generator

Currently using
open source large
language models(Jan./Feb. 2024 ) N (Total) = 1,982; N (Very high) = 96; N (Some) = 1,021

Very high expertise

Some expertise10

Now: Key findings
Insights from our executive interviews align closely withand has saved us significant amounts of money … and thatare industry-specific and narrowly focused but moresurvey findings, showing that leading organizations are

we have scaled very broadly across many of our sites andstrategically impactful (e.g., Generative AI tools for

aggressively scaling up their Generative AI efforts both

continue to scale further across more equipment acrosssemiconductor design that are used only by a small

horizontally (across multiple functions or domains)

more sites.”

subset of workers but have a very large impact on

and vertically (within a single function or domain). This
combination of horizontal and vertical scaling may help
achieve value creation more effectively.

Similarly, from a broad market perspective we are seeing

the business).

an increasingly sharp distinction between horizontal use
cases that cut across industries (e.g., office productivity

As one chief transformation officer in manufacturing noted,

suites and enterprise resource planning systems with“[We have] an application that is being incredibly successful

integrated Generative AI) and vertical use cases that

11

Areas to reinvest time and cost savings

Now: Key findings
Organizations primarily plan to reinvest thesavings from Generative AI into innovation andadditional operations improvements.
Among the overall respondent pool, organizations
said they primarily planned to reinvest cost
and timesavings from Generative AI into driving
innovation (45%) and improving operations (43%),
addressing the value equation from both sides. It’s
interesting to note that a significant percentage
of organizations (27%) also planned to reinvest in
scaling Generative AI adoption, creating a cycle ofGenerative AI reinvestment and growth.
Organizations with “very high” Generative AI
expertise are even more focused than others on
driving innovation (51%). They are also less inclined
than others to reinvest savings from Generative
AI into improving operations and more inclined toprioritize developing new products and services.
The right reinvestment approach depends on anorganization’s specific needs. Organizations currently
facing strategic disruption or transformation from
Generative AI have a greater imperative to focus on

strategic objectives such as innovation and growth,and are likely already working more aggressively todevelop strong Generative AI capabilities.
By contrast, organizations in industries that are
currently not being disrupted by Generative AI are

Drivinginnovation
opportunities

45%

more likely to focus on benefits such as individual

43%

worker productivity and operations improvement,
areas with less of a sense of urgency and lesstolerance for risk. Such organizations can still benefit

Developing
new products
and services

29%

Scaling GenAI
adoption acrossthe organization

27%

Training andupskilling
employees

23%

Enhancing IT
infrastructure

19%

greatly from Generative AI—just in a different way.
They also have a valuable opportunity to watch
and learn from the experiences of other industries
that are currently being disrupted—lessons that
could serve them well if and when Generative AI
disruption reaches their own industry.
“To enable GenAI value in our business, we need tochange our mindset and develop R&D capabilities
to realize a long-term vision enabled by GenAI,” said
the CEO of a digital media company. “Right now, [ourmindset] is short-term and just about tangible cash

Creating a
return forshareholders

13%

Improving
operations
across theorganization

28%

Expandingour market

28%

Improving
cybersecurity
infrastructure

20%

Enhancing risk
management
systems

16%

Exploring new
business models

9%Creating
new jobs

value for one-off use cases.”
Figure 3

Q: Where does your company plan to reinvest cost or timesavings
generated through implementation of GenAI capabilities (select top 3)?
(Jan./Feb. 2024 ) N (Total) = 1,982

122

Now: Key findings

Scaling up
A key to value creation, scaling increases Generative

potential issues become real-world barriers. And withorganization, incorporating more datasets, expanding theAI’s impact on the business and expands its user

Generative AI, many of those barriers are still being

user base (internal and external) to improve upon existingbase—both of which have a strong multiplier effect on

identified and understood.

results, and refining the current solution for more value.

Generative AI’s benefits. Yet, many organizations find it
challenging to make the leap from pilots and proofs ofconcept to large-scale deployment.

“There are always issues that emerge through the
adoption and scaling transition that aren’t expected—
the question we have to consider is how hard are they toThis phased approach gives us a sense of assurance theinvestment is worthwhile before we commit significantly
more resources.”

Scaling is complex and requires effort across a variety ofovercome,” said a chief technology officer we interviewed.

Off-the-shelf Generative AI solutions for common use

interrelated elements spanning strategy, process, people,“For example, [one of our] use cases had some technical,

cases such as office productivity are arguably the easiest

data and technology. Although the challenges associatedpolicy and cybersecurity issues, but they were relatively

to deploy at scale, but they still require substantial

with scaling Generative AI are common to many digitaleasy to overcome, so we scaled. Conversely, for [twoinvestment, effort and training. For unique and/or moretransformation initiatives, issues such as risk management

other] use cases more issues emerged linked to the skill

strategic Generative AI solutions and use cases, theand governance, workforce transformation, trust and data

level to work with the outputs of the AI solution. These

complexity and challenges increase by leaps and bounds,

management take on even greater importance. What

have been harder to address, so scaling has been slower.”along with the potential for greater returns.

worked well in the past might not work the same way withthis new technology.

A public sector chief information officer outlined anotherapproach: “[For us, successful scaling is] building on

The scaling phase is when potential benefits areprevious successes and then taking those initiatives

converted into real-world value. It is also, however, when

to another level. Expanding to other areas of the13Now: Key findings
Workforce access to approved GenAI tools and applications remains low.

Our executive interviews pointed to a number of reasons for this overall low penetration
rate, mostly revolving around risk versus reward—especially data-related risks. Do the

Nearly half of our respondents (46%) reported they provided approved Generative AI

potential rewards of Generative AI justify the risks, and can the risks be mitigated? In

access to just a small portion of their workforces (20% or less). Organizations reporting

particular, we found widespread concern that allowing workers to use public large language“very high” levels of Generative AI expertise are further along, with nearly half (48%)
providing approved Generative AI access to at least 40% of their workforces. Even for these“expert” organizations, worker access to approved tools remains the exception, not the rule.

models (LLMs) and Generative AI tools might lead to problems with protection of intellectual
property and customer privacy.

Percentage of workforce with access to Generative AI
76%
Overall

46%

Little expertise

49%
31%

Some expertise

25%

28%

29%

36%

16%

27%
16%
5%

14%

23% 24%High expertise
Very high expertise

Up to 20%

20%–40%40%–60%

3%

1%

3%

4%

60%–80%

8%

6%

2%

6%

7%

16%

More than 80%

Percentage of the workforce
Figure 4

Q: How much of your overall workforce, do you estimate, have access to your organization’s sanctioned (approved) Generative AI tools/applications?
(Jan./Feb. 2024) N (Total) = 1,982, N (Very high) = 96, N (High) = 606, N (Some) = 1,021, N (Little) = 257

14

Now: Key findings
Other concerns that came up in our executive

sensitive data and intellectual property into public LLMs

interviews include:in an entirely uncontrolled way. This status is likely to•	Generative AI outputs that can be unpredictable andsubject to inaccuracies (i.e., “hallucinations”)—which

continue in the absence of practical policies for allowing
and managing widespread Generative AI access.

undermine trust, particularly when combined with

Organizations should be actively developing sustainablelack of transparency and explainability

processes and policies for enabling ubiquitous but•	Potential loss of control over what Generative AI
apps are being used within the organization and
who is using them

responsible Generative AI use and managing theassociated risks at scale. Widespread but controlled
access to Generative AI will help people get more
comfortable with the technology and enable them to•	Worker resistance to using Generative AI due to lackunderstand what it can and cannot do—giving them aof familiarity or concerns about being replaced

more realistic and informed perspective while opening

Given the potential challenges and risks, a cautious
approach to allowing workers to use Generative AI tools

the door to new opportunities for Generative AI value
creation across the enterprise.

arguably makes sense. However, tight restrictions on
Generative AI are best viewed as a temporary stopgap
measure—not a viable long-term solution. Logically,
any worker with internet access will have access topublic Generative AI tools and could be using themwithout their employer’s consent—potentially leaking

15

“It has been surprising to see how
low the bar is to do something
quick and dirty—this is both
exciting and scary, but the big
challenge is to scale—this is awhole new ball game … but scalingis hard without centralization.”-Director of data science and AI in the technology industry

16

3

Now: Key findings

Areas of strength

Building trust

Growing trust

Lack of trust continues to be one of the biggest barriers

According to a chief technology officer we interviewed,

to large-scale adoption and deployment of Generative AI.

“The explainability piece is really holding us back right now

In this context, two key aspects of trust are: (1) trust in the… once we get a better handle on that, I think we will reallyquality and reliability of Generative AI’s output (supported

be able to accelerate our adoption.”by improved transparency and explainability), and (2) trustfrom workers that Generative AI will make their jobs easier
and won’t replace them.

balancing integrating Generative AI

domain, industry and use case, rather than just scaling
Generative AI produce outputs that are more precise,the benefits the tools have to their work, that will drive

transparent and explainable.

how it can help them do their jobs.
As for trusting Generative AI’s outputs, the technology’s
fallibility in the form of “hallucinations” is well known andis actively being addressed through improved training
and guardrails. For many organizations, transparency andexplainability are even bigger issues. In its current form,
Generative AI still operates largely as a black box—taking
an input and producing an output with no real way forhumans to understand how that output was reached.

Balancing speed and risk

LLMs customized and fine-tuned for their specificnoted that “once people start seeing efficiencies andmore comfortable with the technology and understandGenerative AI emerged in late 2022

reported their organization is effectively

up a general-purpose LLM. This specificity will help

exposure to Generative AI tools will help people become

all forms of AI has increased since

Ultimately, most organizations will likely each end up using

Regarding worker trust, one executive we interviewed

adoption and sustained success.” In other words, greater

72%

said their organization’s trust in

Lack of trust and related risks have thus far notprevented organizations from rapidly adopting

60%

that mitigate potential risks

Opportunities for improvement
Lacking confidence

Generative AI for experiments and proofs ofconcept; however, this will likely change asorganizations transition to large-scale deployment.
According to our wave two survey, 60% of respondents
believed their organization is effectively balancing

rapidly while implementing processes

33%

selected “lack of confidence in results” as
one of their top risks related to Generative
AI tools / applications (#3 of 10 overall)

Measuring trust

rapid integration of Generative AI while implementingof organizations said they are measuring

processes that mitigate potential risks. Also, 72% said theirworker trust and engagement as part

organization’s trust in Generative AI has increased since the36%of altering their talent strategies becauseof the adoption of Generative AI

technology emerged in late 2022.
Figure 5(Jan./Feb. 2024 ) N (Total) = 1,982

17

Now: Key findings
Our executive interviews suggest, however, that addressing trust issues is likely to“Expert” organizations recognize the importance of building trust in

become critically important as organizations transition from experimentation toGenerative AI and are putting effort into it.

large-scale deployment—especially for organizations where the imperative to deploy
Generative AI is more tactical than strategic, and thus less time sensitive.

Despite the importance of trust for successful Generative AI deployment and scaling, 40%–
45% of our overall respondents said they are, to a “large” or “very large” extent, implementing

Generative AI when deployed at scale becomes far more important to the businessprocesses to improve trust in their Generative AI initiatives through various aspects (such as

and affects a much larger pool of human users, making trust a much bigger issue. Trustdata quality, output reliability and organizational empathy). However, among organizations that

related to data quality, LLM training and output reliability becomes particularly important.

reported “very high” Generative AI expertise, the focus on trust is much higher across every“If you don’t have the right dataset or data quality, it is very hard for the application to be
helpful,” said a chief technology officer we interviewed. “GenAI solutions are very sensitive

aspect (59%–73%). This likely reflects both their greater appreciation for the importance of trust
and their greater reliance on Generative AI as an integral and crucial part of the business.

to good quality and well-structured data. If the data is not correct, it is very hard to knowthat the output is wrong.”
In our survey, 33% of respondents cited lack of confidence in results as one of Generative
AI’s top risks (third in the list of top 10 risks). Only 36% of the organizations surveyed wereCompanies implementing processes to generate trust in GenAI
To a “large” or “very large” extent
Overall

Very high expertiseTransparency with employees

45%

60%

Demonstration of consideration,
empathy and kindness in use of GenAI

40%

59%

Quality Generative AI input data

43%

73%

Reliable Generative AI output

41%

67%

measuring worker trust and engagement as part of adapting their talent strategies toGenerative AI.

Figure 6(Jan./Feb. 2024 ) N (Total) = 1,982; N (Very high) = 9618

4

Now: Key findings

Evolving the workforce
Workforce challenges affect Generative AI scaling on both the front and back ends. On thefront end, organizations need valuable and scarce talent with expertise in Generative AI(and data management) to develop and refine their solutions. They also need the overall
workforce to be comfortable enough with the technology to be willing to use it for improving
efficiency and effectiveness. On the back end, organizations need to understand how the
workforce could be affected by large-scale Generative AI deployment and then develop
talent strategies, programs and policies that make sense for the business and workers alike.

Most organizations expect Generative AI to affect their talent strategies.
Three-quarters of survey respondents (75%) expect to change their talent strategies
within two years in response to Generative AI. Organizations reporting “very high”
Generative AI expertise expect to change their talent strategies even faster, with
32% already making changes. This is consistent with our broader finding that suchorganizations are scaling up their initiatives much more aggressively than are others,
leading to greater and more immediate talent impacts.

Addressing these critical and complex workforce issues is an urgent enabler for Generative
AI adoption and scaling, even as organizations work to figure out the technology sideof the problem.

Timeline for change in talent strategies
18%
Now

Figure 726%
Within 1 year

Q: When do you expect to make changes in talent strategies because of generative AI?

31%

16%

10%

1-2 years

2+ years

Don’tknow /
no formal time
frame

(Jan./Feb. 2024 ) N (Total) = 1,982

19

Now: Key findings
The most common talent strategy responses arewith “very high” expertise were much more focused onresistance to change. In the longer term, upskilling orprocess redesign and upskilling or reskilling.

developing AI fluency (47%) and redesigning career paths

reskilling and redesigning work processes and career(38%), and much less focused on assessing changes topaths will likely be essential for capturing Generative AI’s

the anticipated supply and demand of skills (25%).

full value and positioning workers for future success.

respondent pool involve redesigning work processes

These survey results suggest a strong need for more

As one executive noted, “In general, I think it’s more

(48%) and upskilling or reskilling (47%).

attention paid to Generative AI’s talent impacts. In theabout upskilling the people you have, because what’s

near term, AI education and fluency will be especiallyreally valuable is the domain knowledge and theimportant to fostering adoption and overcoming initial

relationships and all that.”

In response to Generative AI adoption, the mostcommon changes to talent strategy among the overall

Relative to the overall respondent pool, organizations

How companies are adjusting talent strategies

Figure 8

Q: How is your organization adjusting its talent strategies because of the adoption of generative AI?
(Jan./Feb. 2024 ) N (Total) = 1,982

48%

47%
36%

Redesigning work
processes to takeadvantage of Generative AI

Designing andimplementing upskilling
and reskilling strategies

Measuring worker trustand engagement35%

Launching AI fluency
development programs

35%

Assessing changes tothe anticipated supply
and demand of skills

34%

Assessing target talentacquisition levels andworkforce ecosystemaccess strategy

29%

29%

Redesigning career
paths and career
mobility strategies

Providing
performance-based
incentives for leveraging
Generative AI

20

Now: Key findings

Skills rising in value
More or much more valuable

Generative AI is expected to increase the value of certain technology-centeredand human-centered skills, while decreasing the value of others.
In the Generative AI era, the competencies that organizations require from their

Data analysis

70%
62%

workforces will evolve. An emphasis on new technical skills will combine with a renewed
focus on the skills that make people uniquely human and valuable. According to thesurvey results:

Promptengineering

60%

Creativity

59%•	Technology-centered skills that respondents most expect to increase in value include:
data analysis (70%), prompt engineering (60%), information research (59%), andsoftware engineering / coding (57%).
•	Human-centered skills that respondents most expect to increase in value include: critical

Software engineering/ coding

Information
research

58%

Flexibility /resilience

54%

Communication
skills53%

Emotional
intelligence51%

The value of specific skills will likely vary depending on timing and organizational level.
A key challenge for today’s organizations is figuring out how to help workers harness the

59%

57%

thinking and problem-solving (62%), creativity (59%), flexibility / resilience (58%), and theability to work in teams (54%).

Critical thinking /problem-solving

power of Generative AI to do their jobs more efficiently and effectively and create more

Ability to workin teams

51%

Application
development

31%

Manual content
creationvalue for the business.
Human-centered skills

Figure 9

Technology-centered skills

Q: To what extent are the following workforce skills going to be more or less valuable across yourorganization because of the adoption of generative AI tools/capabilities?
(Jan./Feb. 2024 ) N (Total) = 1,982

21

Now: Key findings
Head count is expected to increase slightly

Overall enterprise head count change(at least in the short term).
The survey results show that more organizations
expect to increase head count (39%) than to

2%
Don’t
know

3%

6%

38%

Decreasemoderately

IncreasemoderatelyNochange(by 10%–20%)(by 10%–20%)

decrease head count (22%) over the next 12months due to implementation of their Generative
AI strategy. This is especially true for organizations
with “very high” Generative AI expertise (45%increase in head count vs. 23% decrease in headcount) or high expertise (46% increase vs. 25%

1%

Decreasesignificantly

18%

Decreaseslightly

32%
Increaseslightly

Increasesignificantly(by more than 20%)

(up to 10%)(up to 10%)(by more than 20%)

1%

decrease).
Our executive interviews present a cloudier picture

Head count change by enterprise expertise levelon long-term head count. A widespread focus onvalue creation through efficiency and productivity
improvement implies that organizations are striving
to do more with less, which could lead to headcount reduction or reduced hiring.

Don’t know

Very high expertise

23%

28%

45%
46%

Decrease

High expertise

25%

29%

No change

Some expertise

21%

39%

Increase

Figure 10

Little expertise

14%

57%

38%
27%

Q: Which of the following best describes the full-time head count change you anticipate will result over the next 12 months due to implementation ofyour organization’s generative AI strategy?
(Jan./Feb. 2024 ) N (Total) = 1,982; N (Very high) = 96; (High) = 606; N (Some) = 1,021; N (Little) = 257

22

Now: Key findings
On the other hand, many breakthrough technologies in human history have raised

Most organizations acknowledge they are still in the early stages of adapting their talent

the specter of widespread job displacement—yet society has always found new andand HR strategies to Generative AI’s impacts—and they recognize the need to focus on

valuable ways to employ its human capital. Will the impact of AI be fundamentallythem sooner rather than later.

different? Maybe. Maybe not. One thing that seems certain, however, is that someroles and skills will be more affected than others. Although Generative AI’s net impact
on employment at the societal level might be neutral or even positive, the impact for
affected individuals at the personal level could be profoundly challenging.
“GenAI is already having a significant impact on our talent requirement perspective—“Best-case GenAI scenario for us is that we have high-quality jobs that create a
differentiating place to work relative to our competitors [that helps us] attract and retain
talent,” said a chief analytics officer we interviewed. “The worst case is we are so behindthat we can’t hire, our products are inferior, and we took a risk on something thatcreated a significant legal issue that is impacting our business.”it has already had an impact on our head count and we are now looking for different
talent than we did in the past,” said a Fortune 500 executive in the manufacturingindustry we interviewed. “The tipping point is coming and we are not far away fromthis: Once we can truly scale process improvement and plug GenAI into the productlife cycle, this would be a major shift.”23

Next: Looking ahead24

Next: Looking ahead

Efficiency is good—innovation and growth are better.
In the short term, most organizations primarily view Generative AI as a tool to improve productivity at theindividual level and efficiency at the functional level. And while some organizations are starting to see tangible
results in those areas, our survey findings show that organizations reporting higher levels of Generative AI
expertise tend to be more focused than others on innovation and developing new products and services.
Don’t let the uncertainty of this moment stop you from imagining a fundamentally different future for yourorganization. In the long term, we believe the big winners will use the technology to differentiate themselves,
enable broad enterprise transformation and create value in new ways.

25

Next: Looking ahead

Proving, measuring and communicating ALL
types of value is critical.

Tackle barriers to scaling.
To succeed at scaling, you will likely need to concurrently evolve your strategy, processes,
people, data and technology. Also, your organization will probably need to develop

As Generative AI moves from possibilities to practicalities, choosing the right use cases,

strong capabilities for both horizontal and vertical scaling—meaning bringing Generative

selecting appropriate tools, getting to scale, and accurately measuring progress will

AI capabilities to as many workers as possible while at the same time deeply embedding

all be important steps. A holistic approach to value realization—both financial andother capabilities in specific functions or processes. Establishing a center of excellence

nonfinancial—is vital. Having the right processes in place to measure all value createdfor Generative AI can help. It should provide centralized resources (teams, tools,by Generative AI will help you determine if you are achieving value in unexpected ways.

processes, policies, knowledge and experience) that can accelerate deployment ofCommunicating the value created by Generative AI will also be critical to helping build

similar use cases and enable you to make the most of scarce expertise. More broadly,

momentum and support for continued progress.

organizations need to invest in the foundations of Generative AI: data modernization,
talent, and technology and infrastructure. These foundational investments will likely
deliver value across multiple projects—and across the entire enterprise. Most important:
don’t wait for rock-solid proof of financial benefits before starting on scaling. Although it
might require an organizational leap of faith, the way to maximize the value of Generative
AI is to advance from proofs of concept to full implementations.

26

Next: Looking ahead

Build trust through transparency, familiarity,
technology and guardrails.
Trust is the foundation for increased adoption. Without it, widespread use of Generative AI
won’t happen. Improving AI fluency and providing broader access to Generative AI tools canhelp people get more comfortable with the technology and gain a more realistic perspectiveon what it can and cannot do. Having the right data management, technology infrastructureand governance in place to help ensure high-quality inputs as well as verified and explainable
outputs will also help build trust. As a leader, you can also actively instill trust in Generative
AI throughout your organization by (1) clearly and regularly communicating your strategic
objectives for Generative AI, (2) fostering a culture of curiosity that encourages employees

To scale up, you need to skill up.
In today’s extremely competitive market for AI talent, organizations are not only
aggressively pursuing new talent, but also training their broader workforces on
Generative AI. When Generative AI is deployed at scale, different technical and humanfocused skills will become more important. To succeed, organizations will likely needto move beyond simple fluency and create new roles, new work processes, and a neworganizational culture—with an active focus on developing junior talent into seniortalent that can use Generative AI to its full advantage. Organizations might also need torealign their existing internal resources around projects, which could include centralizing
Generative AI talent to simultaneously support multiple initiatives across the enterprise.

to experiment with the new tools, and (3) frequently measuring worker trust to uncoverpotential frustrations and barriers to adoption.

27

Authorship and Acknowledgments
Business leadership
Nitin Mittal

Costi Perricos

Kate Schmidt

Global AI Leader

Global Office of Generative AI Leader

Chief Operating Officer

Deloitte LLP

Deloitte UK

Deloitte AI Strategic Growth Offering

nmittal@deloitte.com

cperricos@deloitte.co.uk

Deloitte Consulting LLPkateschmidt@deloitte.com

Research leadership
Brenna Sniderman

David Jarvis

Executive Director

Senior Research Leader

Deloitte Center for Integrated Research

Deloitte Center for Technology,

Deloitte Services LLPMedia & Telecommunications

bsniderman@deloitte.com

Deloitte Services LP
davjarvis@deloitte.com

Acknowledgments
We would like to thank our leaders and many talented professionals who brought this research to life: Beena Ammanath, Deborshi Dutt, Kevin Westcott, Lynne Sterrett and Jeff Loucks; Ahmed Alibage,
Eric Alons-Cruz, Siri Anderson, Sean Benton, Natasha Buckley, Amber Bushnell, Maria Fernanda Castro, Tracy Fulham, Jordan Garrick, Gerson Lehrman Group (GLG), Lou Ghaddar, Jessi Hendon, Tatum
Hoehn, Karen Hogger, Jonathan Holdowsky, Lisa Iliff, Justin Joyner, Lena La, David Levin, Michael Lim, Nina Lukina, Joe Mariani, Rajesh Medisetti, Sharonjeet Meht, Judy Freeman Mills, Melissa Neumann, Jamie
Palmeroni-Lavis, Jose Porras, Jonathan Pryce, Negina Rood, Lesley Stephen, Kelcey Strong, 10 EQS, Sandeep Vellanki, Ivana Vucenovic, Marianne Wilkinson and Sourabh Yaduvanshi.
We would also like to thank additional Deloitte subject matter specialists who contributed to the development of the survey and report: Ed Bowen, Bjoern Bringmann, Lou DiLorenzo, Amelia Dunlop, Maggie
Fletcher, Rohan Gupta, Oz Karan, Kellie Nuttal, Elizabeth Powers, Ashley Reichheld, Dany Rifkin, Jim Rowan, Baris Sarer, Mike Segala, Laura Shact, Rohit Tandon, Ed Van Buren and Greg Vert.

28

About the Deloitte AI Institute
The Deloitte AI Institute™ helps organizations connect all the different dimensions of the robust, highly dynamic and rapidly evolving AI ecosystem. The AI
Institute leads conversations on applied AI innovation across industries, using cutting-edge insights to promote human-machine collaboration in the Ageof With™.
The Deloitte AI Institute aims to promote dialogue about and development of artificial intelligence, stimulate innovation, and examine challenges to AI
implementation and ways to address them. The AI Institute collaborates with an ecosystem composed of academic research groups, startups, entrepreneurs,
innovators, mature AI product leaders and AI visionaries to explore key areas of artificial intelligence including risks, policies, ethics, future of work and talent,
and applied AI use cases. Combined with Deloitte’s deep knowledge and experience in artificial intelligence applications, the institute helps make sense of thiscomplex ecosystem and, as a result, delivers impactful perspectives to help organizations succeed by making informed AI decisions.
Learn more

About the Deloitte Center for Integrated Research
The Deloitte Center for Integrated Research (CIR) offers rigorously researched and data-driven perspectives on critical issues affecting businesses today. We sitat the center of Deloitte’s industry and functional expertise, combining the leading insights from across our firm to help leaders confidently compete in today’s
ever-changing marketplace.
Learn more

About the Deloitte Center for Technology, Media & Telecommunications
The Deloitte Center for Technology, Media & Telecommunications (TMT Center) is a world-class research organization that serves Deloitte’s TMT practice andour clients. Our team of professional researchers produce practical foresight, fresh insights, and trustworthy data to help clients see clearly, act decisively andcompete with confidence. We create original research using a combination of rigorous methodologies and deep TMT industry knowledge.
Learn more

29

Methodology
To obtain a global view of how Generative AI is being adopted by organizations on the leading edge of AI, Deloitte surveyed 1,982
leaders between January and February 2024. Respondents were senior leaders in their organization and included board and C-suite
members, and those at the president, vice president and director level. The survey sample was split equally between IT and line ofbusiness leaders. Six countries were represented: Australia (99 respondents), France (131 respondents), Germany (150 respondents),
India (200 respondents), the United Kingdom (200 respondents), and the United States (1,202 respondents).
All participating organizations have one or more working implementations of AI being used daily. Plus, they have pilots in place toexplore Generative AI or have one or more working implementations of Generative AI being used daily. Respondents were required
to meet one of the following criteria with respect to their organization’s AI and data science strategy, investments, implementation
approach and value measurement. They: influence decision-making, are part of a team that makes decisions, are the final decisionmaker, or manage or oversee AI technology implementations.
All statistics noted in this report and its graphics are derived from Deloitte’s second quarterly survey, conducted January – February2024; The State of Generative AI in the Enterprise: Now decides next, a report series. N (Total leader survey responses) = 1,982

30

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