AI-related hiring difficulty has decreased since 2022

The latest McKinsey Global Survey on the current state of AI confirms the explosive growth of generative AI (gen AI) tools. But the survey report also shows changes in the roles that organisations are filling to support their AI ambitions.

In the past year, organisations using AI most often hired data engineers, machine learning engineers, and Al data scientists — all roles that respondents commonly reported hiring in the previous survey. The respondents were asked to share their insights into the challenges organisations face when hiring for AI-related roles. The data is categorised into two groups, representing the level of difficulty in filling these positions: Less difficult and more difficult.

Roles like Machine Learning Engineers and AI Data Scientists saw a drop in hiring difficulty. For instance, in 2022, it is approximately 70 per cent difficult to hire people in Machine Learning. The figure came down to approximately 65 per cent. Similarly, in AI data scientists, in 2022, it was 80 per cent difficult to hire but in 2023, it fell to approximately 67 per cent.

For translators the difficulty level fell to 60 per cent from 80 per cent; data architects went down to 60 per cent (2023) from nearly 80 per cent (2022); data-visualisation specialists the difficulty hiring level fell to less than 60 per cent (2023) from nearly 70 per cent (2022). The largest fall seen in hiring difficulty was in data engineers; in 2022 it was nearly 80 per cent; it fell to less than 50 per cent in 2023. A new entrant in hiring was prompt engineers where the hiring difficulty hovered at 60 per cent.

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles like AI data scientists, data engineers, and data-visualisation specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.

Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Nearly four in 10 respondents reporting AI adoption expect more than 20 per cent of their companies’ workforces will be reskilled, whereas eight per cent of respondents say the size of their workforces will decrease by more than 20 per cent.

Impact of AI adoption on workforces

Some respondents believe that there will be minimal to no change in the workforce size, with uncertainty about reskilling efforts. A segment of respondents predicts a moderate decrease in the number of employees, with six-10 per cent of the workforce expected to be reskilled. Another group envisions a significant decrease in the workforce, with 11-20 per cent of employees expected to undergo reskilling.

While the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 per cent, up from 50 per cent), this doesn’t necessarily translate into the automation of an entire role.

Respondents at organisations considered AI high performers stand out in their expectations for workforce reskilling due to AI adoption. Organisations anticipate that over 30 per cent of their employees will require reskilling. A larger percentage of respondents at AI high performers foresee a substantial portion of their workforce, between 21-30 per cent, being reskilled.

In contrast, other respondents outside the AI high-performing category are more inclined to predict that a smaller share, between 11-20 per cent, will require reskilling. A significant number of respondents outside the AI high-performing group expect that 10 per cent or less of their employees will undergo reskilling, reflecting a more conservative outlook on workforce adaptation to AI.

In most instances, companies see generative AI as a tool to augment human activities, not necessarily replace them. So far, we’re mainly seeing companies that are leaning forward with generative AI, focusing on pragmatic areas where the routes to improvements in top-line growth or productivity are clearest.