I’m testing deep research, and it’s 🤯!
I asked, What are the primary bottlenecks hindering AI adoption?—specifically regarding Talent and Expertise Gaps (among other topics). The initial report was around 5,000 words (about 10 pages) and referenced McKinsey’s 2022 data. So, I requested a follow-up with insights from the 2024 edition of the same report.
(Before this, I tested Grok and Perplexity, but they don’t even come close to the quality of what OpenAI delivers).
Here’s the follow-up:
AI Talent Hiring: 2022 vs Early 2024
Hiring Challenge Trend: In McKinsey’s 2022 AI report, a clear talent crunch was observed – “a majority of respondents report difficulty in hiring for each AI-related role”. In other words, most organizations struggled to fill every type of AI position, with no signs of relief at that time. Fast forward to McKinsey’s “The State of AI in Early 2024” survey data, the talent gap has slightly improved but remains a significant hurdle. McKinsey’s latest findings indicate that hiring AI talent is still challenging, though “reported difficulty has decreased since 2022 for many roles”. This easing of pressure is partly attributed to tech-sector layoffs in late 2022 and early 2023, which expanded the pool of available AI specialists. In summary, the hiring challenges have improved somewhat from 2022 to early 2024 – not solved, but less acute than the peak difficulties reported in 2022.
Hardest AI Roles to Fill (2022 vs 2024)
Certain AI-related roles continue to be tough to staff, though the pain points have shifted slightly:
Data Scientists: In 2022, AI data scientist roles were the scarcest – the largest share of companies cited this role as “difficult to fill”. By 2023/24, this pressure eased a bit: the percentage of respondents struggling to hire data scientists dropped from around 80% in 2022 to roughly 67% in 2023 (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). While still high, this improvement suggests more data scientists became available or companies got better at hiring them.
Machine Learning Engineers: Demand for ML engineers remains very high and supply tight. In 2022, about 70% of organizations had difficulty hiring ML engineers (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). A year later this was still ~65% – essentially “as much of a challenge as in the previous year”. ML engineers in 2024 continue to be one of the hardest roles to fill, reflecting sustained demand for those who can productionize AI models.
AI Product Managers / AI Product Owners: This role emerged as critical when organizations moved from experimentation to deployment. The 2022 survey already highlighted the importance of AI product managers (especially among AI high-performers). By early 2024, hiring an AI product owner/manager remains just as difficult as before. Companies report no real improvement in filling these roles, indicating that experienced AI product leaders are still relatively scarce.
Data Engineers: In 2022, data engineers were in very short supply (nearly 80% of companies struggled to hire them) (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). However, by 2023 this difficulty saw the biggest drop among AI roles – falling to under 50% of respondents citing hiring challenges (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). This suggests the talent market for data engineers loosened considerably, possibly due to an influx of candidates from tech layoffs or more professionals upskilling into data engineering.
“Translator” & Design/Visualization Specialists: Roles like analytics translators (experts who bridge business and AI teams) and data visualization or design specialists became somewhat easier to hire by 2024. McKinsey notes that the share of firms struggling to hire translators fell from about 80% to 60%, and similar improvements were seen for data visualization specialists (dropping below 60% difficulty in 2023) (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). These roles, while still in demand, are not quite as scarce as they were in 2022.
New Roles (Prompt Engineers): The generative AI boom created new roles like prompt engineers. This role didn’t appear in the 2022 survey, but by 2023 about 60% of companies reported difficulty hiring prompt engineers (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). It’s a new talent gap reflecting how quickly companies are seeking expertise in crafting AI prompts and working with generative AI tools.
Bottom Line: In 2022 virtually all AI roles were hard to fill, with data scientists topping the list. By early 2024, the situation improved for many “core” roles (data engineers, data scientists, etc.), but specialized positions like ML engineers and AI product managers remain as difficult as ever. Even with slight improvements, companies still face steep competition for skilled AI talent across the board.
How Organizations are Addressing the AI Talent Gap
Faced with persistent talent shortages, organizations have adjusted their strategies between 2022 and 2024. Key trends and shifts include:
Reskilling and Upskilling Internal Talent: Rather than relying solely on hiring, companies are investing in their existing workforce. In 2022, McKinsey found that the most popular strategy for sourcing AI skills was reskilling current employees – nearly half of surveyed organizations were training their own people to fill AI roles. This trend continues into 2024 as AI adoption grows. Companies anticipate large-scale upskilling; nearly 40% of respondents in the latest survey expect over 20% of their workforce will need to be reskilled in the next three years (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). In practice, this means more internal AI training programs, certification courses, and on-the-job learning opportunities to build the needed skills from within.
Broadening Recruitment Channels: Organizations are casting a wider net to find AI talent. High-performing AI organizations distinguish themselves by tapping many recruiting sources. According to McKinsey, AI leaders are more than twice as likely as others to recruit from top-tier technical universities and leading tech firms, while also hiring from nontraditional sources – regional universities, coding bootcamps, online training academies, and diversity-focused programs. This marks a shift from just competing for a small pool of candidates toward creatively sourcing talent in varied places. Between 2022 and 2024, more companies have adopted this multi-channel approach to alleviate hiring bottlenecks.
Leveraging AI “Industrialization” Roles: The mix of roles companies hire is shifting to support scaling AI. In 2022, many firms were moving from experimentation to deployment, leading to a spike in hiring software engineers, data engineers, and ML engineers to embed AI into products. High performers were particularly focused on roles like ML engineers, AI product managers, and analytics translators that ensure AI delivers business value. By 2024, this trend has broadened – organizations recognize the need for end-to-end AI development teams. The continued difficulty in hiring ML engineers and AI product managers underscores that these “AI industrialization” roles remain priority hires. Some companies address this by grooming internal candidates (e.g. turning experienced software engineers into ML engineers via training) or splitting responsibilities among existing teams until specialists can be hired.
Adapting through Technology and Partners: Another shift is that organizations are trying to do more with fewer specialized hires. The rapid rise of user-friendly AI tools and platforms (especially in generative AI) allows teams to implement AI solutions with less specialized coding. Some companies are also partnering with external providers or contractors for AI expertise rather than hiring full-time, especially if budgets are constrained. While McKinsey’s report emphasizes internal talent strategies, industry observers note that businesses without deep AI benches often turn to consultants, vendor solutions, or “AI as a service” offerings to bridge skill gaps. This helps alleviate immediate hiring pressures while longer-term talent pipelines are developed.
Trends and Outlook
The comparison between 2022 and early 2024 shows a softening but still significant AI talent shortage. Organizations have become slightly more successful in hiring for certain roles (thanks in part to a larger talent supply in the market). At the same time, the explosion of interest in AI – particularly generative AI – has created new demands for skills (like prompt engineering and AI product leadership) that companies are scrambling to meet. The net effect is that the overall hiring challenge remains, even if its composition has shifted.
Notably, companies are not standing still. The emphasis on reskilling/upskilling suggests a strategic shift: instead of waiting for the perfect candidate, businesses are growing their own AI talent from within (AI-related hiring difficulty has decreased since 2022 – Apeejay Newsroom). There’s also a greater appreciation for cross-functional expertise (e.g. translators who link AI teams with business units), which were hard to find in 2022 but are being cultivated more proactively now.
In summary, McKinsey’s data indicates that while hiring for AI roles has improved marginally by early 2024 (it’s slightly easier now to find certain experts than it was in 2022), it is still far from easy. Critical roles like machine-learning engineers and AI product managers remain highly competitive hires with no relief in sight. Organizations are responding by broadening how they source talent and by heavily investing in developing the skills of their current workforce – a necessary shift to navigate the enduring AI talent shortage.
Sources
McKinsey & Company. “The State of AI in 2022.” McKinsey & Company
McKinsey & Company. “The State of AI in Early 2024.” McKinsey & Company
Apeejay Newsroom. “AI-related hiring difficulty has decreased since 2022.” Apeejay Newsroom