Leaders are seeking AI talent, even during an economic crunch. Companies at every level of AI sophistication see skill gaps—and are aiming to fill them.
Companies across all industries have been scrambling to secure top AI talent from a pool that’s not growing fast enough. Even during the economic disruptions and layoffs caused by the COVID-19 pandemic, the demand for AI talent has been strong. Leaders are looking to reduce costs through automation and efficiency, and AI has a real role to play in that effort.1
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In Deloitte’s third edition of the State of AI in the Enterprise survey, we found something unexpected when it came to skill gaps for AI implementations.2 Although a majority of the most mature AI adopters, the Seasoned, reported little or no gap between their AI needs and current abilities, 23 percent said they had a major or extreme one—a higher percentage than the less mature organizations. How could this be?
It is said that what really counts is what you learn after you know everything. Perhaps the Seasoned, having worked with AI technologies more extensively, now know what skills they actually need, not what they think they need. It could also be that the Seasoned tend to pursue more transformational projects using AI, focusing more on creating new products and services than cost reduction.
To understand better, let’s take an in-depth look at the talent profiles of the three maturity segments:
Types of talent needed. All three segments have an AI talent gap. Some feel the lack of necessary skills more acutely. The type of talent most in demand was fairly equal across the three segments: AI developers and engineers, AI researchers, and data scientists. Business leaders, domain experts, and project managers fell lower on the list.
Where talent comes from. All three different segments have the same top source for getting the skills they need: experienced AI professionals from outside their organization. With everyone after the same small group of individuals, companies need to further develop alternatives—in particular, hiring university graduates with AI skills and retraining internal resources. Seasoned adopters rely more on internal resources already trained in AI; Starters lean on partnerships with other companies with AI expertise a bit more.
Training activities. Seasoned AI adopters feel their skills shortages more intensely than the other segments, but they may be selling themselves short, considering they are much more focused on internal training and education. Roughly two-thirds of Seasoned adopters are currently training their developers to create new AI solutions (64 percent compared to 55 percent for Skilled and 43 percent of Starters) and training their AI staff to deploy AI solutions. They are also providing training for employees to use AI in their jobs (67 percent compared to 53 percent of Skilled and 48 percent of Starters).
Even if you have been working with AI for years and have dozens of implementations, you may still lack the in-house talent you need for the next round of projects.
Use AI technologies that maximize what you have. Work to reduce the burden on your AI team where you can. Build skills around sourcing the best AI technologies and suppliers.3 Leverage cloud-based platforms with pre-built solutions and accelerators.
Diversify your sources of AI talent. Look at experienced hires, university hires, and how your partners and vendors can help fill gaps. Aim to build a bench of business talent that can “speak AI” as well.
Focus on future rock stars. A world-class AI expert may not drive competitive advantage as much as a strong and broad team, so try to build for the long run.
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