Health Transformation Institute (HTI)
institute for continuous health transformation
Joaquim Cardoso MSc*
Founder, Chief Researcher & Editor
December 9, 2022
MSc* from London Business School
MIT Sloan Master Program (European Version)
This is an excerpt of the report “The state of AI in 2022 — and a half decade in review”, with the title above, focusing on the topic in question.
Executive Summary
What are the findings of the survey?
- AI Adoption has more than doubled since 2017, though the proportion 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.
Five years in review: AI adoption, impact, and spend
In the past 5 years we have seen the following shifts:
- 1.First, AI adoption has more than doubled.
- 2.Second, the level of investment in AI has increased alongside its rising adoption.
- 3.Third, the specific areas in which companies see value from AI have evolved.
- 4.Lastly, one thing that has remained concerningly consistent is the level of risk mitigation organizations engage in to bolster digital trust.
Commentary
- Over the past half decade, during which we’ve been conducting our global survey, we have seen the “AI winter” turn into an “AI spring.”
- However, after a period of initial exuberance, we appear to have reached a plateau, a course we’ve observed with other technologies in their early years of adoption.
- We might be seeing the reality sinking in at some organizations of the level of organizational change it takes to successfully embed this technology.
- We’ve encountered companies that get discouraged because they went into AI thinking it would be a quick exercise, …
- … while those taking a longer view have made steady progress by transforming themselves into learning organizations that build their AI muscles over time.
- These companies gradually incorporate more AI capabilities and stand up increasingly more applications progressively faster and more easily thanks to lessons from past successes as well as failures.
- They not only invest more, but they also invest more wisely, with the goal of creating a veritable AI factory that enables them to incorporate more AI in more areas of the business, first in adjacent ones where some existing capabilities can be repurposed and then into entirely new ones.
There is, at a high level, an emerging playbook for getting maximum value from AI.
- We see a group of leaders engaging in the types of practices that help execute AI successfully.
- It’s paying off in the form of actual bottom-line impact at significant levels. We also see it every day as we guide others on their AI journeys
- It’s not easy work, but as has been the case with previous technologies, the gains will go to those who stay the course.
ORIGINAL PUBLICATION
The state of AI in 2022 — and a half decade in review [excerpt]
MGI
Michael Chui
December 2022
The results of this year’s MGI 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 proportion of organizations using AI has plateaued between 50 and 60 percent for the past few years.
Adoption has more than doubled since 2017, though the proportion 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.
Five years in review: AI adoption, impact, and spend
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.
- 1.First, AI adoption has more than doubled.
- 2.Second, the level of investment in AI has increased alongside its rising adoption.
- 3.Third, the specific areas in which companies see value from AI have evolved.
- 4.Lastly, one thing that has remained concerningly consistent is the level of risk mitigation organizations engage in to bolster digital trust.
1.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.
In 2017, 20 % of respondents reported adopting AI in at least one business area,
whereas today, that figure stands at 50 %, though it peaked higher in 2019 at 58 percent.
Responses show an increasing number of AI capabilities embedded in organizations over the past five years.
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 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.
% of respondents who say given AI capability is embedded in products or business processes in at least one function or business unit²
1 The number of capabilities included in the survey has grown over time, from 9 in 2018 to 15 in the 2022 survey.
2 Question was asked only of respondents who said their organizations have adopted AI in at least one function.
The top use cases, however, have remained relatively stable: optimization of service operations has taken the top spot each of the past four years.
The most popular AI use cases span a range of functional activities.
1 Out of 39 use cases. Question was asked only of respondents who said their organizations have adopted AI in at least one function.
2 Eg, field services, customer care, back office.
2.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 50% of respondents report that level of investment.
the level of investment in AI has increased alongside its rising adoption — organizations using AI reported more than 5 percent of their digital budgets went to AI — increased from 40% 5 years ago, to more than 50% now.
Going forward, 63 percent of respondents say they expect their organizations’ investment to increase over the next three years.
Going forward, 63 percent of respondents say they expect their organizations’ investment to increase over the next three years.
The most popular AI use cases span a range of functional activities.
1 Question was asked only of respondents who said their organizations have adopted AI in at least one function.
2 Eg, field services, customer care, back office.
3.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.
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.
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.
4.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.
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.
AI-related cost decreases are most often reported in supply chain management and revenue increases in product development and marketing and sales.
AI-related cost decreases are most often reported in supply chain management and revenue increases in product development and marketing and sales.
Cost decrease and revenue increase from AI adoption in 2021, by function, % of respondents¹
1 Question was asked only of respondents who said their organizations have adopted AI in a given function. Respondents who said “no change,” “cost increase,” “not applicable,” or “don’t know” are not shown.
There has been no substantial increase in organizations’ reported mitigation of AI-related risks.
AI risks that organizations consider relevant and are working to mitigate, % of respondents¹
1 Question was asked only of respondents who said their organizations had adopted AI in at least one function; n = 1,151. Respondents who said “don’t know/not applicable” are not shown.
2 That is, the ability to explain how AI models come to their decisions.
Commentary
Over the past half decade, during which we’ve been conducting our global survey, we have seen the “AI winter” turn into an “AI spring.”
However, after a period of initial exuberance, we appear to have reached a plateau, a course we’ve observed with other technologies in their early years of adoption.
We might be seeing the reality sinking in at some organizations of the level of organizational change it takes to successfully embed this technology.
We might be seeing the reality sinking in at some organizations of the level of organizational change it takes to successfully embed this technology.
In our work, we’ve encountered companies that get discouraged because they went into AI thinking it would be a quick exercise, …
… while those taking a longer view have made steady progress by transforming themselves into learning organizations that build their AI muscles over time.
These companies gradually incorporate more AI capabilities and stand up increasingly more applications progressively faster and more easily thanks to lessons from past successes as well as failures.
They not only invest more, but they also invest more wisely, with the goal of creating a veritable AI factory that enables them to incorporate more AI in more areas of the business, first in adjacent ones where some existing capabilities can be repurposed and then into entirely new ones.
These companies gradually incorporate more AI capabilities and stand up increasingly more applications progressively faster and more easily thanks to lessons from past successes as well as failures.
They not only invest more, but they also invest more wisely,
There is, at a high level, an emerging playbook for getting maximum value from AI.
Each year that we conduct our research, we see a group of leaders engaging in the types of practices that help execute AI successfully.
It’s paying off in the form of actual bottom-line impact at significant levels. We also see it every day as we guide others on their AI journeys.
It’s not easy work, but as has been the case with previous technologies, the gains will go to those who stay the course.
Those taking a longer view have made steady progress by transforming themselves into learning organizations that build their AI muscles over time.
