Is AI adoption slowing down?

McKinsey report cites 'plateau' in usage — but investments still increasing as value of tech evolves

Is AI adoption slowing down?

The adoption of artificial intelligence (AI) 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.

That’s according to a new report from McKinsey that found 20 percent of respondents reported adopting AI in at least one business area in 2017 — today, that figure stands at 50 percent, though it peaked higher in 2019 at 58 percent.

“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,” says Michael Chui, partner at the McKinsey Global Institute. “We might be seeing the reality sinking in at some organizations of the level of organizational change it takes to successfully embed this technology.”

Meanwhile, the average number of AI capabilities that organizations use, such as natural-language generation and computer vision, has 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, says the McKinsey report, while natural-language text understanding has moved from the middle of the list in 2018 to near the top, just behind computer vision.

Increasing investment

In addition, the level of investment in AI has increased alongside its rising adoption, says the report — based on surveys in May and August of 2022, with responses from 1,492 participants. For example, five years ago, 40 percent of respondents at organizations using AI reported more than five 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, says McKinsey.

Also notable: the specific areas in which companies see value from AI have evolved. In 2018, manufacturing and risk were the two functions where 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.

Many companies get discouraged because they went into AI thinking it would be a quick exercise, says Chui, “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.”

More than half (56 per cent) of Canadian companies plan to spend between 10 per cent and 20 per cent more on technology and software in 2023, and 12 per cent are looking to spend 21 per cent or more than their 2022 spend, according to a recent survey. 

Hiring trends in AI

All the employers surveyed by McKinsey report that hiring AI talent, particularly data scientists, remains difficult.

The most popular hire in AI is software engineers, more often than data engineers and AI data scientists. However, shortages in tech talent are challenging employers, and AI data scientists remain particularly scarce.

TD Bank Group announced in early 2022 that it planned to hire over 2,000 technology roles in 2022 with a specific focus on key skills in new technologies and processes.

When it comes to sourcing AI talent, the most popular strategy is reskilling existing employees, with nearly half of the respondents doing so. Recruiting from top-tier universities and technology companies that aren’t in the top tier, such as regional leaders, are also common strategies.

“Despite knowing for close to a decade about the growing need for roles like data scientists and data engineers, we still haven’t moved the needle enough on the supply side,” says Helen Mayhew, partner at McKinsey in Sydney.

“Hiring from boot camps is picking up because experienced talent is just not available. It isn’t easy to set up learning pathways for this fresh talent, but organizations have little choice. Reskilling efforts are also a big undertaking, but it’s necessary to fill the gaps. To meet the need, we actually need many more organizations reskilling than what we’re seeing in these results.”

As for diversity within organizations’ AI-focused teams, there is significant room for improvement, says McKinsey. The average share of employees on these teams at respondents’ organizations who identify as women is just 27 percent, and the share is similar when looking at the average proportion of racial or ethnic minorities developing AI solutions: just 25 percent. What’s more, 29 percent of respondents say their organizations have no minority employees working on their AI solutions.

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