The state of AI in early 2024:
Gen AI adoption spikes and
starts to generate value
May 2024
If 2023 was the year the world discovered generative AI (gen
AI), 2024 is the year organizations truly began using—and
deriving business value from—this new technology. In the latest
McKinsey Global Survey on AI, 65 percent of respondents report
that their organizations are regularly using gen AI, nearly double
the percentage from our previous survey just ten months ago.
Respondents’ expectations for gen AI’s impact remain as high as
they were last year, with three-quarters predicting that gen AI will
lead to significant or disruptive change in their industries in the
years ahead.
Organizations are already seeing material benefits from gen AI
use, reporting both cost decreases and revenue jumps in the
business units deploying the technology. The survey also provides
insights into the kinds of risks presented by gen AI—most notably,
inaccuracy—as well as the emerging practices of top performers to
mitigate those challenges and capture value.
As generative AI adoption accelerates, survey
respondents report measurable benefits and
increased mitigation of the risk of inaccuracy.
A small group of high performers lead the way.
1The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
This article is a collaborative effort by Alex Singla, Alexander Sukharevsky, Lareina Yee, and
Michael Chui, with Bryce Hall, representing views from QuantumBlack, AI by McKinsey and
McKinsey Digital.
AI adoption surges
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For
the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent.
This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest
is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any
region; however, this year more than two-thirds of respondents in nearly every region say their
organizations are using Looking by industry, the biggest increase in adoption can be found in
professional
Exhibit 1
Web <2024>
<State of AI>
Exhibit <1> of <12>
Organizations that have adopted Al in at least 1 business function,¹ % of respondents
1In 2017, the de
nition for AI adoption was using AI in a core part of the organization’s business or at scale. In 2018 and 2019, the de
nition was embedding at
least 1 AI capability in business processes or products. Since 2020, the de
nition has been that the organization has adopted AI in at least 1 function.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Al adoption worldwide has increased dramatically in the past year, after
years of little meaningful change.
McKinsey & Company
2017 2018 2019 2020 2021 2022 2023 2024
Adoption of AI
Use of generative AI
0
20
40
60
80
100
0
20
40
60
80
100
20
47
58 56
55
72
33
6550 50
1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations
based in Central and South America reporting AI adoption.
2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market
research, R&D, tax preparation, and training.
2The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Also, responses suggest that companies are now using AI in more parts of the business. Half of
respondents say their organizations have adopted AI in two or more business functions, up from
less than a third of respondents in 2023 (Exhibit 2).
Exhibit 2
Web <2024>
<State of AI>
Exhibit <2> of <12>
Business functions at respondents’ organizations that have adopted AI,¹ % of respondents
1In 2021, n = 1,843; in 2022, n = 1,492; in 2023, n = 1,684; in early 2024, n = 1,363.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Survey �ndings suggest that organizations are using AI in more business
functions now than in previous years.
McKinsey & Company
0
2021 2022 2023 2024
10
20
30
40
50
60
70
80
90
100
72
27
50
15
8
1 or more functions
2 or more functions
3 or more functions
4 or more functions
5 or more functions
3The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Gen AI adoption is most common in the functions where it can create the most value
Most respondents now report that their organizations—and they as individuals—are using gen AI.
Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one
business function, up from one-third last year. The average organization using gen AI is doing so
in two functions, most often in marketing and sales and in product and service development—
two functions in which previous research determined that gen AI adoption could generate the
most value3—as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing
and sales, where reported adoption has more than doubled. Yet across functions, only two use
cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Exhibit 3
Web <2024>
<State of AI>
Exhibit <3> of <12>
Respondents’ organizations regularly using generative AI (gen AI), by function, % of respondents
Most commonly reported gen AI use cases within function, % of respondents
1Eg, providing real-time assistance and script suggestions to help desk employees during human-to-human conversations.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Respondents most often report generative AI adoption in their marketing-
and-sales, product- and service-development, and IT functions.
McKinsey & Company
Marketing
and sales
IT
Marketing and sales
Content support for marketing strategy
Personalized marketing
Sales lead identi�cation and prioritization
Product and/or service development
Design development
Scienti�c literature and research review
Accelerated early simulation/testing
IT
IT help desk chatbot
Data management
34 23 17 16 16 13 12 8 7 6 4
Service
operations
Human
resources
Strategy and
corporate �nance
Manufacturing
Product and/or
service development
Other corporate
functions
Software
engineering
Risk Supply chain/
inventory management
16
15
8
10
6
6
7
7
6
IT help desk AI assistant¹
4The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
3 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023,
respondents are much more likely to be using gen AI at work and even more likely to be using
gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use
across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at
the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and
outside of work compared with their midlevel-management peers. Looking at specific industries,
respondents working in energy and materials and in professional services report the largest
increase in gen AI use.
Exhibit 4
Personal experience with generative AI tools, by job title, and age, 2023–24,¹ % of respondents
Respondents are much more likely now than in 2023 to say they are using generative AI.
Regularly use
for work
Regularly use for work
and outside of work
Regularly use
outside of work
Have tried
at least once
No exposure Don’t know
Born
1981–96³
Born
1965–80³
Born in 1964
or earlier³
6
17
21
30
18
9
15
22
20
28
12
4
Overall
average¹
2
9
34
16
31
7
3
7
18
18
37
17
3
5
22
24
36
11
3
15
31
17
32
4
Midlevel
managers²
Senior
managers²
C-level
executives²
2 2
12
28
15
31
10
44
10
14
16
42
15
3
7
16
20
35
19
4
8
24
23
39
5
13
26
16
35
8
8
14
16
40
18
15
26
15
33
8
8
16
13
42
18
Note: Figures may not sum to 100%, because of rounding.
¹In 2023, n = 1,684; in 2024, n = 1,363.
²In 2023, C-suite respondents, n = 541; senior managers, n = 437; and middle managers, n = 339. In 2024, C-suite respondents, n = 474; senior managers, n = 406; and middle
managers, n = 206.
³In 2023, for respondents born in 1964 or earlier, n = 143; for respondents born between 1965 and 1980, n = 268; and for respondents born between 1981 and 1996, n = 80. In 2024,
for respondents born in 1964 and earlier, n = 158; for respondents born between 1965 and 1980, n = 331; and for respondents born between 1981 and 1996, n = 184. Age details were
not available for all respondents.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
2023 2024
McKinsey & Company
5The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Exhibit 4 (continued)
Asia–
Paci�c
12
27
14
34
10
3
4
18
19
36
19
3
3
31
30
30
4
1
Developing
markets
9
11
20
34
23
3
15
27
12
30
11
5
Europe
10
14
11
45
15
6
11
21
14
44
8
2
Greater
China
9
10
18
46
14
3
19
27
14
34
6
1
North
America
6
22
13
38
19
3
Personal experience with generative AI tools, by industry, 2023–24,¹ % of respondents
Personal experience with generative AI tools, by location, 2023–24,¹ % of respondents
Respondents are much more likely now than in 2023 to say they are using generative AI.
Regularly use
for work
Regularly use for work
and outside of work
Regularly use
outside of work
Have tried
at least once
No exposure Don’t know
Business, legal,
and professional
services
Advanced
industries
Consumer
goods and
retail
Energy
and
materials
Financial
services
Healthcare,
pharma, and
medical products
Media and
telecom
Technology
7
20
26
34
11
3
5
15
21
42
13
5
19
38
6
28
5
3
7
16
13
41
21
2
8
25
16
38
10
2
7
11
12
40
26
4
8
27
26
29
8
2
4
7
16
44
26
3
8
18
22
43
8
3 3
8
16
18
41
14
4
14
17
8
51
8
6
10
17
44
15
7
15
30
23
24
3
5
9
12
27
31
17
4
15
39
9
32
3
1
17
22
14
39
6
2
2023 2024
2023 2024
Note: Figures may not sum to 100%, because of rounding.
¹In 2023, media, entertainment, and telecommunications, n = 69; technology, n = 175; business, legal, and professional services, n = 215; energy and materials,
n = 152; advanced industries (includes automotive and assembly, aerospace and defense, advanced electronics, and semiconductors), n = 112; consumer goods and retail, n =
128; �nancial services, n = 248; healthcare, pharmaceuticals, and medical products, n = 130. In 2024, media, entertainment, and telecommunications, n = 70; technology, n =
184; business, legal, and professional services, n = 166; energy and materials, n = 113; advanced industries, n = 86; consumer goods and retail, n = 100; �nancial services, n =
201; healthcare, pharmaceuticals, and medical products, n = 109. Analyses for 2023 were updated to include additional industries within advanced industries and energy and
materials.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Note: Figures may not sum to 100%, because of rounding.
¹In 2023, Asia–Paci�c, n = 164; Europe, n = 515; North America, n = 392; Greater China (includes Hong Kong and Taiwan), n = 337; and developing markets (includes India,
Latin America, and Middle East and North Africa), n = 276. In 2024, Asia–Paci�c, n = 116; Europe, n = 457; North America, n = 401; Greater China (includes Hong Kong and
Taiwan), n = 153; and developing markets (includes India, Latin America, and Middle East and North Africa), n = 234.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
McKinsey & Company
6The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Investments in gen AI and analytical AI are beginning to create value
The latest survey also shows how different industries are budgeting for gen AI. Responses
suggest that, in many industries, organizations are about equally as likely to be investing more
than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI
solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their
organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead,
most respondents—67 percent—expect their organizations to invest more in AI over the next
three years.
Where are those investments paying off? For the first time, our latest survey explored the
value created by gen AI use by business function. The function in which the largest share of
respondents report seeing cost decreases is human resources. Respondents most commonly
report meaningful revenue increases (of more than 5 percent) in supply chain and inventory
management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits
in service operations—in line with what we found last year—as well as meaningful revenue
increases from AI use in marketing and sales.
7The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Looking ahead, most respondents—
67 percent—expect their
organizations to invest more in
AI over the next three years.
Exhibit 5
Share of organization’s digital budget spent on generative AI,¹ % of respondents
Share of organization’s digital budget spent on analytical AI technology,¹ % of respondents
In most industries, organizations are about equally likely to invest more than 5 percent of
their digital budgets in generative AI and analytical AI.
Technology
Energy and materials
Financial services
Media and telecommunications
Consumer goods and retail
Advanced industries
Overall
Business, legal, and
professional services
Healthcare, pharmaceuticals,
and medical products
18 28 135
12 44 1411
11 48 213
16
11 47 34
48 133
15 37 226
11 45 166 8 14
18
8 11
67 7171
1
2
2
3 3
6
7 10
6 13
3 66 75 9 11
13 23
Technology
Energy and materials
Financial services
Media and telecommunications
Consumer goods and retail
Advanced industries
Overall
Business, legal, and
professional services
Healthcare, pharmaceuticals,
and medical products
11 40 710
17 46 137
7 60 164
5 63 194
3 61 218
6 55 127 7 13
52
3 7
6 7
64 121265
70 65 18
1
1 1
11 4 26 47 12
9 8
16 16 >20%
16–20%
11–15%
6–10%
Don’t know
≤5%
Note: Figures may not sum to 100%, because of rounding.
1Question was asked only of respondents who said their organizations have adopted AI in at least 1 business function. For technology, n = 128; for energy and materials, n = 63;
for �nancial services, n = 107; for media, entertainment, and telecommunications, n = 50;
for consumer goods and retail, n = 67; for advanced industries, n = 50; for business, legal, and professional services, n = 101; and for healthcare,
pharmaceuticals, and medical products, n = 58.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
McKinsey & Company
8The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Exhibit 6
Cost decrease and revenue increase from generative AI adoption in 2023, by function,¹ % of respondents
Cost decrease and revenue increase from analytical AI adoption in 2023, by function,¹ % of respondents
Organizations most often see meaningful cost reductions from generative AI use in HR
and revenue increases in supply chain management.
Use of analytical AI most often yields cost reductions in service operations and revenue
increases in marketing and sales.
4
7
5
10
10
11 12 27
12
11
8
6 17 35
8 24
14 19
10 35
14 39
16 30
8 52 65
11 35
23 44
Marketing and sales
Risk, legal, and compliance
Human resources
Product or service development
Supply chain and inventory management
Service operations
IT
Software engineering
Other corporate functions
Average across all functions
Decrease by <10% Decrease by 10–19% Decrease by ≥20% Increase by >10% Increase by 6–10% Increase by ≤5%
7
3
6
4
5
9 13 10
3
4
7
5 10 29
9 30
10 42
13 29
18 30
8 23
6 21
13 46
12 3441122
15315
8623
41131
4734
51621
91213
6924
7926
151619
2428
9520
8411
31129
51628
41720
915
1
5723
41122
8821
Marketing and sales
Risk, legal, and compliance
Human resources
Product or service development
Supply chain and inventory management
Service operations
IT
Software engineering
Other corporate functions
Average across all functions
37
33
37
46
45
42
34
39
42
50
32
44
46
56
45
53
35
33
62
53
50
58
40
44
57
63
56
53
7134
34
23
43
49
41
25
35
37
37
1Questions were asked only of respondents who said their organizations have adopted AI in a given function. Respondents who said “cost increase,” “no change,”
“not applicable,” or “don’t know” for the e�ects of analytical AI on costs are not shown, and respondents who said “revenue decrease,” “no change,” “not applicable,” or “don’t
know” for the e�ects of analytical AI on revenues are not shown. Data for manufacturing and strategy and corporate �nance are not shown, because the base sizes were too
small to meet the reporting threshold.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–March 5, 2024
McKinsey & Company
9The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
McKinsey commentary
Alex Singla
Senior partner and global coleader of QuantumBlack, AI by McKinsey
In 2024, generative AI (gen AI) is no longer a novelty. Nearly two-thirds of respondents to
our survey report that their organizations are regularly using gen AI, nearly double what our
previous survey found just ten months ago, and four in ten are using gen AI in more than
two business functions. The technology’s potential is no longer in question. And while most
organizations are still in the early stages of their journeys with gen AI, we are beginning to get
a picture of what works and what doesn’t in implementing—and generating actual value with—
the technology.
One thing we’ve learned: the business goal must be paramount. In our work with clients, we
ask them to identify their most promising business opportunities and strategies and then
work backward to potential gen AI applications. Leaders must avoid the trap of pursuing tech
for tech’s sake. The greatest rewards also will go to those who are not afraid to think big. As
we’ve observed, the leading companies are the ones that are focusing on reimagining entire
workflows with gen AI and analytical AI rather than simply seeking to embed these tools into
their current ways of working.
For that to be effective, leaders must be ready to manage change at every step along the
way. And they should expect that change to be constant: enterprises will need to design a
gen AI stack that is robust, cost-efficient, and scalable for years to come. They’ll also need
to draw on leaders from throughout the organization. Realizing profit-and-loss impact from
gen AI requires close partnership with HR, finance, legal, and risk to constantly readjust the
resourcing strategies and productivity expectations.
Inaccuracy: The most recognized and experienced risk of gen AI use
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks
associated with the technology. These can range from data management risks such as data
privacy, bias, or intellectual property (IP) infringement to model management risks, which tend
to focus on inaccurate output or lack of explainability. A third big risk category is security and
incorrect use. Respondents to the latest survey are more likely than they were last year to say
their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI,
and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations
consider workforce and labor displacement to be relevant risks and are not increasing efforts
to mitigate them. In fact, inaccuracy—which can affect use cases across the gen AI value chain,
ranging from customer journeys and summarization to coding and creative content—is the only
risk that respondents are significantly more likely than last year to say their organizations are
actively working to mitigate.
10The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
11The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Exhibit 7
Gen AI risks that organizations consider relevant,¹ % of respondents
Gen AI risks that organizations are working to mitigate,¹ % of respondents
Inaccuracy and intellectual property infringement are increasingly considered relevant
risks to organizations’ generative AI use.
Inaccuracy
Intellectual
property
infringement
Cybersecurity
Personal/
individual
privacy
Regulatory
compliance
Explainability
Equity and
fairness
Workforce
labor
displacement
Organizational
reputation
National
security
Environmental
impact
Political
stability
Physical
safety
None
of the
above
56
46
53
39
45
39
31
34
29
14
11 10 11
1
63
52 51
43 42
40
30 27
24
13 13 12
8
1
2023 2024
Inaccuracy
Intellectual
property
infringement
Cybersecurity
Personal/
individual
privacy
Regulatory
compliance
Explainability
Equity and
fairness
Workforce
labor
displacement
Organizational
reputation
National
security
Environmental
impact
Political
stability
Physical
safety
None
of the
above
4 4
33
25 24
17
23
9
12 11
3
5
3
12
38 38
25
28
18
20
13
16 16
65
2
8
32
1Question was asked only of respondents whose organizations have adopted Al in at least 1 function. Respondents who said “don’t know/not applicable” are
not shown. In 2023, n = 913; in 2024, n = 1,052.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
McKinsey & Company
12The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
In fact, some organizations have already experienced negative consequences from the use of
gen AI, with 44 percent of respondents saying their organizations have experienced at least one
consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected
their organizations, followed by cybersecurity and explainability.
Exhibit 8
Web <2024>
<State of AI>
Exhibit <8> of <12>
1Question was asked only of respondents whose organizations have adopted generative Al in at least 1 function, n = 876. The 17 percent of respondents who
said “don’t know/not applicable” are not shown.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Nearly one-quarter of respondents say their organizations have
experienced negative consequences from generative AI’s inaccuracy.
McKinsey & Company
23
16
12 11 10 9 8 7 7
4 4 4 4
39
Inaccuracy
Cybersecurity
Explainability
Intellectual
property
infringement
Regulatory
compliance
Personal/
individual
privacy
Organizational
reputation
Workforce
labor
displacement
Equity and
fairness
Physical
safety
National
security
Political
stability
Environmental
impact
None
of the
above
Generative-AI-related risks that caused negative consequences for organizations,¹ % of respondents
McKinsey commentary
Lareina Yee
Senior partner, McKinsey; chair, McKinsey Technology Council
Responsible AI needs to start on day one, and there is still much work to be done in terms
of education and action. It begins with a company’s values—organizations must establish
clear principles for how they apply generative AI (gen AI) and set up guardrails to ensure its
safe implementation. For example, recognizing the importance of data security means that
company-level data and prompts remain within the enterprise walls. For that to happen, the
enterprise must have secure contracts with large language model and application providers,
as well as robust training, to make sure employees understand the difference between
enterprise tools and public tools so that code or proprietary data are not inadvertently shared
in public models.
Responsible AI also starts upstream of compliance and monitoring. Leading companies in
deploying gen AI incorporate risk practices in the development of their AI applications. This
includes ensuring that technical teams understand risk and mitigation practices. Gen AI
solutions are probabilistic models that can make mistakes or inadvertently amplify biases
in training data, so testing models before they are deployed is essential. Without a robust
testing approach, it is hard to deliver on responsible AI.
Finally, companies must develop a clear governance model to help ensure that gen AI
applications conform to governing principles. What we see in the survey results and in
our conversations with clients is a growing awareness of responsible AI and an urgency to
get it right. Still, even with increasing understanding, a little less than one-quarter of the
respondents in our survey report having a clear process to embed risk mitigation in their
solutions. Moving from awareness to action will be critical.
4 “Implementing generative AI with speed and safety,” McKinsey Quarterly, March 13, 2024.
13The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Our previous research has found that there are several elements of governance that can help in
scaling gen AI use responsibly, yet few respondents report having these risk-related practices
in For example, just 18 percent say their organizations have an enterprise-wide council or
board with the authority to make decisions involving responsible AI governance, and only
one-third say gen AI risk awareness and risk mitigation controls are required skill sets for
technical talent.
5 “Technology’s generational moment with generative AI: A CIO and CTO guide,” McKinsey, July 11, 2023.
Bringing gen AI capabilities to bear
The latest survey also sought to understand how, and how quickly, organizations are deploying
these new gen AI tools. We have found three archetypes for implementing gen AI solutions:
takers use off-the-shelf, publicly available solutions; shapers customize those tools with
proprietary data and systems; and makers develop their own foundation models from
Across most industries, the survey results suggest that organizations are finding off-the-shelf
offerings applicable to their business needs—though many are pursuing opportunities to
customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within
respondents’ business functions are utilizing off-the-shelf, publicly available models or tools,
with little or no customization. Respondents in energy and materials, technology, and media
and telecommunications are more likely to report significant customization or tuning of publicly
available models or developing their own proprietary models to address specific business needs.
14The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Exhibit 9
Energy and materials
Technology
Media and telecommunications
Consumer goods and retail
Financial services
Advanced industries
Overall
Healthcare, pharmaceuticals,
and medical products
Business, legal, and
professional services
Web <2024>
<State of AI>
Exhibit <9> of <12>
Strategy for developing generative AI (gen AI) capabilities, % of reported instances of gen AI use¹
1Question was asked only of respondents who said their organizations regularly use generative AI in at least 1 business function. Figures were calculated after
removing respondents who said “don’t know.”
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Organizations are pursuing a mix of o�-the-shelf generative AI capabilities
and also signi�cantly customizing models or developing their own.
McKinsey & Company
40
44
50
58
63
53
53
53
46
60
56
50
42
37
47
47
47
54
Signi�cant
customization
or developed
own model
Primarily o�
the shelf, with
little or no
customization
McKinsey commentary
Alexander Sukharevsky
Senior partner and global coleader of QuantumBlack, AI by McKinsey
Despite the spike in adoption of generative AI (gen AI), we are still in the experimentation
phase, with many organizations seeking relatively simple, one-step solutions. Although
it varies by industry, roughly half of our survey respondents say they are using readily
available, off-the-shelf gen AI models rather than custom-designed solutions. This is a very
natural tendency in the early days of a new technology—but it’s not a sound approach as
gen AI becomes more widely adopted. If you have it, your competitor probably has it as well.
Organizations need to ask themselves: What is our moat? The answer, in many cases, likely
will be customization.
But even there, the answer is not so simple. The spine and brain of the enterprise of the
future will rely on a well-orchestrated mix of multiple foundational models—both off-the-
shelf solutions and tools that have been finely tuned to the enterprise’s specific needs. In
fact, with gen AI we are moving from a binary world of “build versus buy” to one that might be
better characterized as “buy, build, and partner,” in which the most successful organizations
are those that construct ecosystems that blend proprietary, off-the-shelf, and open-source
models. Finally, leaders must understand that gen AI models generally comprise just
15 percent of any given solution. In other words: it’s not just tech. To create value,
organizations must have all the elements in place—domain reimagining abilities; relevant
skill sets (including the upskilling of nontechnical colleagues); a robust operating model;
proprietary data. It’s only when those factors are in place that organizations will be able to
unlock impact and move from experimentation to scale.
Across most industries, the survey
results suggest that organizations
are finding off-the-shelf offerings
applicable to their business
needs—though many are pursuing
opportunities to customize models
or even develop their own.
15The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
16The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Respondents most often report that their organizations required one to four months from the
start of a project to put gen AI into production, though the time it takes varies by business
function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not
surprisingly, reported uses of highly customized or proprietary models are times more likely
than off-the-shelf, publicly available models to take five months or more to implement.
Exhibit 10
Web <2024>
<State of AI>
Exhibit <10> of <12>
Time for organization to put generative AI capabilities to use, from project launch,¹ % of respondents
1Question was asked only of respondents who said their organizations regularly use generative AI in the given business function. Respondents who said “don’t
know/not applicable” are not shown.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Business functions are most often able to put their generative AI
capabilities to use within one to four months.
McKinsey & Company
<1 month
1–4 months
5–8 months
>8 months
Marketing
and sales
Risk Product and/or
service development
Manufacturing IT Other corporate
functions
Strategy and
corporate �nance
Human
resources
Supply chain/
inventory
management
Software
engineering
Service
operations
17
27
22
11
21
31
22
11
19
15
29
17 16
34
14 13
16
27 25
14
18 20
22 22
10
15
34
26
13
36
10 12
13
36
16
1213
24
18
21
15
21 19
9
17The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Gen AI high performers are excelling despite facing challenges
Gen AI is a new technology, and organizations are still early in the journey of pursuing its
opportunities and scaling it across functions. So it’s little surprise that only a small subset of
respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can
be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining
closely. These, after all, are the early movers, who already attribute more than 10 percent of their
organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more
than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they
span industries and regions—though most are at organizations with less than $1 billion in annual
revenue. The AI-related practices at these organizations can offer guidance to those looking to
create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three
functions, while others average two. They, like other organizations, are most likely to use gen AI
in marketing and sales and product or service development, but they’re much more likely than
others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance;
and in supply chain and inventory management. They’re more than three times as likely as
others to be using gen AI in activities ranging from processing of accounting documents
and risk assessment to R&D testing and pricing and promotions. While, overall, about half of
reported gen AI applications within business functions are utilizing publicly available models
or tools, gen AI high performers are less likely to use those off-the-shelf options than to either
implement significantly customized versions of those tools or to develop their own proprietary
foundation models.
McKinsey commentary
Michael Chui
Partner, McKinsey Global Institute
The rapid pace of adoption of generative AI (gen AI) is reflected in the investments that
companies are making in these technologies, with similar percentages of respondents to
our survey stating that they are spending at least 5 percent of their digital budgets on gen
AI and analytical AI. That said, given the longer track record in analytical AI, larger shares of
respondents are spending more than 20 percent of their budgets on analytical AI than on
gen AI.
Organizations are also finding themselves able to deploy gen AI quickly, with the most
common project length being less than four months, reflecting the ease of using natural
language as an interface. Organizations that are doing more customization or building their
own models take longer to bring these systems online.
What else are these high performers doing differently? For one thing, they are paying more
attention to gen-AI-related risks. Perhaps because they are further along on their journeys,
they are more likely than others to say their organizations have experienced every negative
consequence from gen AI we asked about, from cybersecurity and personal privacy to
explainability and IP infringement. Given that, they are more likely than others to report that their
organizations consider those risks, as well as regulatory compliance, environmental impacts, and
political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more
risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-
related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve
the legal function and embed risk reviews early on in the development of gen AI solutions—that
is, to “shift left.” They’re also much more likely than others to employ a wide range of other best
practices, from strategy-related practices to those related to scaling.
Gen AI high performers are much
more likely than others to use
gen AI solutions in risk, legal,
and compliance; in strategy and
corporate finance; and in supply
chain and inventory management.
18The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
19The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Exhibit 11
Gen AI risk awareness and mitigation
are required skills for technical talent
Have clear processes to embed risk mitigation in
gen AI solutions (eg, involving the legal function)
Gen AI models are designed to allow audits,
bias checks, and risk assessment
Have an enterprise-wide council or board to make
decisions on responsible AI governance
0 20 40 60 80 100
Gen AI high performers²All other respondentsRisk
Strategy
Talent
Operating model
68
44
43
2418
18
23
34
Senior leaders understand how gen AI
can create value for the business
Have an enterprise-wide road map for gen AI,
prioritized based on value, feasibility, and risk
Have appointed a credible, empowered
leader of gen AI initiatives
0 20 40 60 80 100
64
59
3221
25
39
Have curated learning journeys, tailored by role,
to build critical gen AI skills for technical talent
Have clearly de�ned the talent (ie, both roles and skills)
needed to execute the gen AI strategy
Have a talent strategy that allows e�ective recruitment,
onboarding, and integration of gen-AI-related talent
0 20 40 60 80 100
43
32
3116
15
18
Organizations engaging in each practice,¹ % of respondents
Organizations seeing the largest returns from generative AI are more likely than others
to follow a range of best practices.
¹Asked only of respondents who said their organizations are regularly using generative AI in at least 1 business function.
²Respondents who said that at least 11% of their organizations’ EBIT in 2023 was attributable to their use of gen AI. For gen AI high performers, n = 46; for all other
respondents, n = 830.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Have a centralized team that coordinates
and links gen AI e�orts across the organization
Deliver gen AI solutions following well-de�ned
agile team processes and standards
Have funding and budgeting processes that
support agile delivery of gen AI solutions
0 20 40 60 80 100
49
43
2714
19
35
McKinsey & Company
20The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Exhibit 11 (continued)
Gen AI high performers²All other respondentsTechnology and data
Adoption and scaling
Data are used consistently to create
insights that a�ect bottom-line performance
There is a clear performance management infrastructure
(eg, KPIs) to measure and track value of gen AI
0 20 40 60 80 100
Nontechnical personnel understand the potential value
and risks of using gen AI in their day-to-day work 52
44
3713
24
21
Testing and validation are embedded
in release process for each model
Clear processes are in place to iteratively
improve model outputs
Processes are de�ned to determine when models
need human validation (eg, human in the loop)
Gen AI foundations are built with a
strategy to enable reuse across solutions
There is a de�ned, comprehensive data strategy
to enable the gen AI road map
Live monitoring of entire system is set up,
enabling rapid issue resolution
Modular components are developed that
can be reused across solutions
0 20 40 60 80 100
5817
4615
4319
4315
42
417
17
3111
Organizations engaging in each practice,¹ % of respondents
Organizations seeing the largest returns from generative AI are more likely than others
to follow a range of best practices.
¹Asked only of respondents who said their organizations are regularly using generative AI in at least 1 business function.
²Respondents who said that at least 11% of their organizations’ EBIT in 2023 was attributable to their use of gen AI. For gen AI high performers, n = 46; for all other
respondents, n = 830.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
McKinsey & Company
21The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
In addition to experiencing the risks of gen AI adoption, high performers have encountered other
challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have
experienced difficulties with data, including defining processes for data governance, developing
the ability to quickly integrate data into AI models, and an insufficient amount of training data,
highlighting the essential role that data play in capturing value. High performers are also more
likely than others to report experiencing challenges with their operating models, such as
implementing agile ways of working and effective sprint performance management.
Exhibit 12
Web <2024>
<State of AI>
Exhibit <12> of <12>
Elements that have posed challenges in capturing value from generative AI (gen AI), % of respondents
Note: Figures do not sum to 100%, because respondents could choose multiple answer options.
¹Respondents who said that at least 11% of their organizations’ EBIT in 2023 was attributable to their use of generative AI. For respondents at AI high perform-
ers, n = 46; for all other respondents, n = 830. Respondents who said “don’t know/not applicable” are not shown.
Source: McKinsey Global Survey on AI, 1,363 participants at all levels of the organization, Feb 22–Mar 5, 2024
Generative AI high performers report experiencing a range of challenges in
capturing value from the technology.
McKinsey & Company
Adoption and scaling
Talent
Strategy
Technology
Operating model
Risk and responsible AI
Data 70 36
34
28
30
39
35
38
48
47
43
42
37
33
Gen AI high performers¹ All other respondents
About the research
The online survey was in the field from February 22 to March 5, 2024, and garnered responses
from 1,363 participants representing the full range of regions, industries, company sizes,
functional specialties, and tenures. Of those respondents, 981 said their organizations had
adopted AI in at least one business function, and 878 said their organizations were regularly
using gen AI in at least one function. To adjust for differences in response rates, the data are
weighted by the contribution of each respondent’s nation to global GDP.
22The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
McKinsey commentary
Bryce Hall
Associate partner
We’ve been conducting research on AI for seven years now, and the pace of innovation,
the evolution of new companies and capabilities, and the wave of investment have been
remarkable. And now we’re seeing how leading companies are capturing business value from
these often-dazzling AI and generative AI (gen AI) capabilities.
One of the most interesting findings in this year’s survey is that among the high performers
capturing the most value from gen AI, most solutions are highly customized or bespoke (what
we refer to as “shaper” or “maker” archetypes of gen AI solutions). While many companies
are finding value from off-the-shelf gen AI solutions (or the “taker” archetype), capturing the
full value of this technology often requires significant customization—for example, training
models on proprietary company and customer data or tuning models to improve performance
within a specific industry or business context.
The survey also sheds new light on high performers’ practices. High performers, for example,
are significantly more likely than others to embed testing and validation in the release process
for models, as well as to develop clear processes to iteratively improve model outputs. Over
time, these kinds of practices will become even more important, as highly customized and
bespoke solutions are the ones that will truly be differentiating for companies. Off-the-shelf
solutions, by contrast, are likely to become table stakes. Collectively, these data on practices
are consistent with our ongoing work and research on digital and AI transformations, which
shows that competitive advantage comes from building organizational and technological
capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the
business for distributed digital and AI innovation.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior
partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area
office, where Michael Chui, a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner
in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
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