The state of AI in 2022—and
a half decade in review
December 2022
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The results of this year’s McKinsey Global Survey on AI show the expansion of the
technology’s use since we began tracking it five years ago, but with a nuanced
picture underneath.
1
Adoption has more than doubled since 2017, though the pro-
portion of organizations using AI has plateaued between 50 and 60 percent for
the past few years. A set of companies seeing the highest financial returns from AI
continue to pull ahead of competitors. The results show these leaders making larger
investments in AI, engaging in increasingly advanced practices known to enable
scale and faster AI development, and showing signs of faring better in the tight
market for AI talent. On talent, for the first time, we looked closely at AI hiring and
upskilling. The data show that there is significant room to improve diversity on AI
teams, and, consistent with other studies, diverse teams correlate with outstanding
performance.
This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we
have seen shifts over this period.
First, AI adoption has more than doubled.² In 2017, 20 percent of respondents reported adopting AI in
at least one business area, whereas today, that figure stands at 50 percent, though it peaked higher in
2019 at 58 percent.
Meanwhile, the average number of AI capabilities that organizations use, such as natural-language
generation and computer vision, has also doubled—from 1.9 in 2018 to 3.8 in 2022. Among these
1
In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example,
natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics,
autonomous driving, and manufacturing work).
2
In 2017, the definition for AI adoption was using AI in a core part of the organization’s business or at scale. In 2018 and 2019, the definition
was embedding at least one AI capability in business processes or products. In 2020, 2021, and 2022, the definition was that the
organization has adopted AI in at least one function.
Five years in
review: AI adoption,
impact, and spend
2 The state of AI in 2022—and a half decade in review
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Responses show an increasing number of AI capabilities embedded in
organizations over the past ve years.
Average number of AI capabilities that
respondents’ organizations have embedded
within at least one function or business unit¹
Share of respondents who say their organizations
have adopted AI in at least one function, %
% of respondents who say given AI capability is embedded in products or business processes in
at least one function or business unit²
McKinsey & Company
The number of capabilities included in the survey has grown over time, from 9 in 2018 to 15 in the 2022 survey.
Question was asked only of respondents who said their organizations have adopted AI in at least one function.
¹
²
Transformer
s
Generative adversarial networks (
GAN)
Transfer
learning
Natural-language genera
tion
Facial r
ecognition
Reinforcemen
t learning
Physical robo
tics
Natural-language speech understanding
Digital
twins
Recommender syst
ems
Knowledge gr
aphs
Deep learning
Virtual agents or conversational interf
aces
Natural-language text unders
tanding
Computer
vision
Robotic process automa
tion
39
34
33
33
30
25
25
24
23
20
20
18
18
16
11
11
2017 2018 2019 2020 2021 2022
20
47
58
50
56
50
2018 2019 2020 2021 2022
1.9
2.3
3.1
3.9
3.8
capabilities, robotic process automation and computer vision have remained the most commonly deployed each
year, while natural-language text understanding has advanced from the middle of the pack in 2018 to the front of the
list just behind computer vision.
3The state of AI in 2022—and a half decade in review
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The most popular AI use cases span a range of functional activities.
Most commonly adopted AI use cases, by function, % of respondents¹
Top use cases Use cases by function
McKinsey & Company
Out of 39 use cases. Question was asked only of respondents who said their organizations have adopted AI in at least one function.¹
Service operations optimization
Creation of new AI-based products
Customer service analytics
Customer segmentation
New AI-based enhancements of products
Customer acquisition and lead generation
Contact-center automation
Product feature optimization
Risk modeling and analytics
Predictive service and intervention
24
20
19
19
19
17
16
16
15
14
Service operations² Product and/or service development Marketing and sales Risk
Eg, eld services, customer care, back o ce.²
The top use cases, however, have remained relatively stable: optimization of service operations has taken
the top spot each of the past four years.
Second, the level of investment in AI has increased alongside its rising adoption. For example, five years
ago, 40 percent of respondents at organizations using AI reported more than 5 percent of their digital
budgets went to AI, whereas now more than half of respondents report that level of investment. Going
forward, 63 percent of respondents say they expect their organizations’ investment to increase over the
next three years.
4 The state of AI in 2022—and a half-decade in review
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Third, the specific areas in which companies see value from AI have evolved. In 2018, manufacturing and
risk were the two functions in which the largest shares of respondents reported seeing value from AI
use. Today, the biggest reported revenue effects are found in marketing and sales, product and service
development, and strategy and corporate finance, and respondents report the highest cost benefits
from AI in supply chain management. The bottom-line value realized from AI remains strong and largely
consistent. About a quarter of respondents report this year that at least 5 percent of their organizations’
EBIT was attributable to AI in 2021, in line with findings from the previous two years, when we’ve also
tracked this metric.
Lastly, one thing that has remained concerningly consistent is the level of risk mitigation organizations
engage in to bolster digital trust. While AI use has increased, there have been no substantial increases in
reported mitigation of any AI-related risks from 2019—when we first began capturing this data—to now.
The most popular AI use cases span a range of functional activities.
Top use cases Use cases by function
McKinsey & Company
¹
Question was asked only of respondents who said their organizations have adopted AI in at least one function.¹
Eg,
eld services, customer care, back o ce.²
Most commonly adopted AI use cases within each business function, % of respondents¹
Service operations
optimization
24
Contact-center
automation
16
Service operations²
Customer service
analytics
19
Customer segmentation
19
Marketing and sales
Sales and demand
forecasting
10
Logistics network
optimization
9
Supply chain management
P
redictive maintenance
13
Simulations (eg, using digital
twins, 3
D modeling)
11
Yield, energy, and/or
throughput optimization
11
Manufacturing
Optimization of talent
management
10
Optimization of workforce
deployment
5
Human resources
Risk modeling and
analytics
15
Fraud and debt
analytics
11
Risk
Capital allocation
7
M&A support
4
Treasury management
4
Strategy and corporate nance
Creation of new AI-based
products
20
New AI-based enhancements
of products
19
Product and/or service development
5The state of AI in 2022—and a half decade in review
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