Adoption of Artificial Intelligence has more than doubled since 2017, though the proportion of organizations using AI has plateaued between 50 and 60% for the past few years. A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors, according to consultancy McKinsey’s Global Survey on AI.
“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.”
The report says there is significant room to improve diversity on AI teams. “Consistent with other studies, diverse teams correlate with outstanding performance”, McKinsey says.
“The average share of employees on these teams at respondents’ organizations who identify as women is just 27%. The share is similar when looking at the average proportion of racial or ethnic minorities developing AI solutions: just 25%. What’s more, 29% of respondents say their organizations have no minority employees working on their AI solutions.”
46% say their organizations have active programs to increase gender diversity within the teams that are developing AI solutions, through steps such as partnering with diversity-focused professional associations to recruit candidates.
One-third say their organizations have programs to increase racial and ethnic diversity. We also see that organizations with women or minorities working on AI solutions often have programs in place to address these employees’ experiences.
“In line with previous McKinsey studies, the research shows a correlation between diversity and outperformance. Organizations at which respondents say at least 25% of AI development employees identify as women are 3.2 times more likely than others to be AI high performers. Those at which at least one-quarter of AI development employees are racial or ethnic minorities are more than twice as likely to be AI high performers”, the report says.
AI adoption has more than doubled. 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%.
“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.
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 level of investment in AI has increased alongside its rising adoption, the report says. Five years ago, 40% at organizations using AI reported more than 5% of their digital budgets went to AI, whereas now more than half of respondents report that level of investment.
“Going forward, 63% of respondents say they expect their organizations’ investment to increase over the next three years.”
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.
“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”, says Michael Chui, partner at McKinsey Global Institute.
“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.
“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.”
“As the AI frontier advances, we continue to be inspired by some truly innovative applications of AI, such as the use of AI to identify new drugs, create hyper personalized recommendations for consumers, and power AI simulations in digital twins to optimize performance across a variety of settings”, says McKinsey associate partner Bryce Hall.
“As individual AI capabilities, such as natural-language processing and generation, continue to improve and democratize, we’re excited to see a wave of new applications emerge and more companies capture value from AI at scale.”
McKinsey partner, Helen Mayhew, says it’s important to remember that AI jobs are among some of the highest paid, and demand will only increase.
“We risk undermining the progress we’ve made to date on closing pay gaps for women and ethnic minorities if they are not equally represented in this high-demand skills base. We must continue to find ways to get more women and minorities engaged in STEM in their education years and beyond.”