Although adoption of AI has more than doubled since 2017, the proportion of organisations using AI has plateaued between 50–60%
After five years of steady, sometimes heady, growth, AI adoption has plateaued, according to “The state of AI in 2022”, McKinsey’s annual survey of 1,500 companies. Use cases are stable, and the market for tech talent is tight, with new “hot jobs” surfacing every year. But much remains to be done about managing risk and building inclusive teams.
Adoption of AI has more than doubled since 2017, though the proportion of organisations 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.
High-performing companies use frontier technologies
Pack of high-performer companies leveraging AI better than their competitors are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes.Also important, they are engaging more often in “frontier” practices that enable AI development and deployment at scale, or what some call the “industrialization of AI.” For example, leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly.
They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms. And AI high performers are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI applications by using emerging low-code or no-code programs, which allow companies to speed up the creation of AI applications. In the past year, high performers have become even more likely than other organizations to follow certain advanced scaling practices, such as using standardized tool sets to create production-ready data pipelines and using an end-to-end platform for AI-related data science, data engineering, and application development that they’ve developed in-house.
Hiring AI talent remains tough
On talent, for the first time, McKinsey 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. All organizations report that hiring AI talent, particularly data scientists, remains difficult. AI high performers report slightly less difficulty and hired some roles, like machine learning engineers, more often than other organizations.
Most organizations haven’t yet maximized the opportunity of the technology, according to the latest McKinsey research. It’s that companies aren’t investing in the resources needed for the organizational change required to effectively implement Artificial Intelligence (AI). One reason is that there’s a talent crunch.
A typical AI project requires a highly-skilled team including a data scientist, data engineer, machine-learning engineer, product manager, and designer—and there simply aren’t enough skilled professionals available, even with the recent contraction across the technology industry, the survey found.
AI space evolving with greater specialization
According to the research, the AI space is evolving quickly with a greater specialization in roles. One example is the machine learning engineer who designs, builds, and productionizes predictive models and AI systems for automation, performance, and scalability. When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half of the companies we surveyed are doing so.
Responses suggest that both AI high performers and other organizations are upskilling technical and nontechnical employees on AI, with nearly half of respondents at both AI high performers and other organizations saying they are reskilling as a way of gaining more AI talent. However, high performers are taking more steps than other organizations to build employees’ AI-related skills.
No let-up in talent shortage
Unfortunately, the tech talent shortage shows no sign of easing, threatening to slow that shift for some companies. A majority of respondents report difficulty in hiring for each AI-related role in the past year, and most say it either wasn’t any easier or was more difficult to acquire this talent than in years past. AI data scientists remain particularly scarce, with the largest share of respondents rating data scientist as a role that has been difficult to fill, out of the roles we asked about.
The new report shines a light on the industry’s challenges with diversity. Addressing them will be a critical factor to long-term success. The average share of employees on AI teams at respondents’ organizations who identify as women is just 27 percent; the share is similar among the average proportion of racial or ethnic minorities: 25 percent. Diverse and inclusive perspectives are especially critical in AI to prevent issues of bias in datasets and models, and distrust in outcomes.
Looking ahead, as companies evolve their strategies for developing AI tech talent, they may find lessons that are applicable to other parts of their business; the newest wave of generative AI models, for example, promises to reinvent functions such as communications, sales, and human resources.
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