AI Talent: Meet The Guardians Of The AI Algorithms

When machine learning algorithms are talking the talk, and making decisions about who, how, when and where organizations should walk the walk, it behooves leaders to understand more about the people who train and tame the machines.

Surprisingly, the AI talent pool isn’t filled with your average techies. Far from it.

In fact, to even talk about the AI “talent pool” is somewhat misleading, because it barely exists. That’s the problem: Individuals with AI know-how are a comparatively small sub-group within the digital and information technology talent pool, which itself is inadequate to meet existing demand for IT-related talent.

How big is the talent gap? The U.S. Bureau of Labor Statistics earlier this year projected that the United States would average “about” 356,700 computer and IT job “openings” every year through 2033—and the BLS projection didn’t include AI and GenAI specialists. To be clear, BLS defines job opening as a specific position “to be filled” at an establishment. In short, an existing vacancy.

This is critical because organizations everywhere, and of every description, are scrambling to find the people they need to develop and implement their AI plans.

While virtually everyone will soon need some level of AI fluency, the AI talent hunt presents a greater challenge; and it’s just beginning. In other words, staffing new AI/GenAI initiatives will add to the already difficult recruitment and retention challenges companies face in the digital-technology arena.

AI Talent Profile

To help leaders better understand the background, wants, needs, likes and dislikes of AI talent, we teamed up last December with researchers from GLG (Gerson Lehrman Group) on a wide-ranging survey of AI talent preferences.

Here’s what we learned from the respondents and how it might help your organization find and keep the people you need.

Education: While nearly half (45%) of the AI/GenAI professionals surveyed had degrees in computer or data science, other STEM subjects ran a close second (38%). More important, however, three out of four respondents—including a large percentage of those with computer or data science degrees—said they didn’t acquire their AI/GenAI knowledge as part of their formal education, but on their own, on the job, by taking online courses, or through some combination thereof.

The takeaway: Don’t limit your talent search to computer and data science graduates.

Other STEM graduates also should be considered hot prospects. Looking further afield, more than 15% of respondents had degrees in totally unrelated fields (11.5% in business or economics, and 4.4% in the arts, humanities or social sciences.) If someone shows a genuine interest in AI, consider giving them a shot and an opportunity to learn.

Upskilling: Formal education is only part of the story. The ways in which survey respondents said they acquired their AI knowledge indicates the vast majority were upskilling on their own—often in multiple ways. Nearly 90% (87.5% to be exact) said they had read research articles on AI/GenAI; 86% said they followed GenAI news, podcasters and influencers; 80% learned by doing; and 52.5% said they had attended local meetings dealing with the topic. Lower on the list were activities like participating in hackathons.

The takeaway: The AI archetype is a curious self-starter who is not only willing, but eager to take on the challenge of learning something new using his or her discretionary time to do so.

Recruiting them: AI specialists listed compensation as their top consideration when looking for a job. Work-life balance also ranked very high (#3), according to the BCG/GLG survey. Then their priorities moved in new directions; job security and benefits and perks (high priorities for most workers in other fields, including more general tech talent) didn’t rank among the top five priorities—remote work, interesting work, and impactful work did.

The takeaway: Money matters, but job flexibility, and the opportunity to use their skills in interesting and meaningful ways may be just as important to many AI/GenAI job candidates. So be ready to discuss the purposeful and personal, as well as the monetary, rewards of the work that an AI job candidate will be doing—because that may be what ultimately differentiates your organization, and your job, from another.

Retaining them: While compensation is their top consideration when looking for a job, other factors weigh more heavily when AI specialists consider whether to keep their current job or move on. If you didn’t see my recent column on joy at work, you might want to read it —because the key to retaining AI talent is whether they enjoy the work they are doing. Other top-five considerations, in descending order, were “autonomy/control” in their work, learning and growth, career advancement, and “having a good workspace” (preferably at home, I would guess, based on the emphasis they also placed on flexible and remote work.)

The takeaway: Though AI/GenAI talent prioritizes the enjoyment they get from their work and their independence, they also are looking to advance in their careers, both professionally and intellectually. Provide them with opportunities to increase their knowledge, along with project ownership, decision-making, and career advancement opportunities, and you’ll be looking to hire replacements far less frequently.

The artificial intelligence/GenAI talent race is just beginning. Your challenge is to make your organization a place where AI talent wants to work—and wants to stay.