Top 7 Reasons Why Recruiting AI Talent Is Critical To Your Firm’s Success

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Apr 2, 2018

It’s hard to find a business or technology publication these days that don’t have an article covering the upcoming dominance of artificial intelligence or its more advanced cousin, machine learning. And artificial intelligence is even beginning to make inroads into the operations of the recruiting function itself. But unfortunately, many recruiting leaders have yet to realize that executives at their firm will soon begin to demand that their function develop the capability to recruit a sufficient number of people in the area of artificial intelligence/machine learning.

If you’re not convinced of the impending need for this new recruiting capability, this article provides a quick overview of the top seven reasons why recruiting functions need to immediately begin developing a sophisticated recruiting plan covering this extremely difficult talent acquisition area.

The Top 7 Reasons Why Your Firm Must Have A Plan For Recruiting AI/ML Talent 

The top firms already have a huge lead, so you must catch up — the top five global firms by market cap value are already in the lead in making machine learning their primary focus. A study by the research firm Paysa revealed that Amazon is investing $228 million in new AI positions, followed by Google ($130 million) and Microsoft ($75 million). The top 20 AI recruiters spend more than $650 million annually to woo elusive researchers and engineers. The CEOs of the top five firms have revealed their view on the need for a strong AI capability. Those perspectives include:

  • Amazon — Jeff Bezos in his annual letter to shareholders said, “The key to the company’s future success lies in artificial intelligence.” Amazon leads the AI recruiting field (one source reveals that Amazon’s average annual investment is $228 million with 1,178 AI jobs posted).
  • Google — Chief Executive Sundar Pichai now calls Google an “AI first” company.”
  • Microsoft — Microsoft CEO Satya Nadella stressed the immense potential of artificial intelligence, calling it the “ultimate breakthrough in technology.”
  • Facebook — CEO Mark Zuckerberg listed AI as “one of the company’s 10-year bets” back in 2014.
  • Apple — Tim Cook says Apple thinks artificial intelligence can amplify human performance and deliver breakthroughs which reshape peoples’ lives. However, its high level of internal secrecy seems to limit its AI recruiting success.
  • It’s a global battle — even Vladimir Putin told Russian students “the country that leads in artificial intelligence will rule the world.”

ML/AI will be applied throughout every firm — Jeff Bezos, CEO of Amazon, has made it crystal clear that “there’s no institution in the world that cannot be improved with machine learning.” “It will empower and improve every business, every government organization, every philanthropy.” Bezos also notes that “machine learning and AI is a horizontal enabling layer.” And that means that AI/ML will impact every function within the corporation, provided that they can attract the necessary number of high-quality machine-learning experts. Because every major business function may need a team of up to five AI/ML experts, your recruiting plan must be able to successfully recruit both high-volume and high-quality AI/ML talent.

The machine-learning performance advantage is huge — in many cases, machine learning can do things that no human could ever be expected to do. However, there are already numerous examples of how machine learning can dramatically improve both process and product performance. And in many cases, the differential improvement is over 50 percent when compared to human intuitive approaches.

The supply of available talent compared to the demand is an ugly ratio — there is perhaps no other area in business where the demand for talent so far exceeds the available supply. In fact, a recent study by Element AI concluded that there are “about 22,000 Ph.D.-level computer scientists around the world are capable of building AI systems.” “In contrast, at least 10,000 related positions are open in the U.S. alone.” And American universities only graduate about 100 new researchers and engineers each year. And unfortunately, many recent grads don’t have the required business acumen that is necessary to apply their computer science knowledge to significant business problems. And finally, as the demand for this talent skyrockets, we will all be involved in what CIO magazine and Fast Company magazine both call “a War for AI talent.” And with the Russians and the Chinese also heavily focusing on AI/ML, recruiting talent from around the world will become even more difficult.

Standard recruiting approaches simply won’t work — because only approximately 14 percent of the available Ph.D. talent is looking for a job, all desirable candidates are currently getting multiple offers. And that means that standard sourcing and recruiting approaches simply won’t work. Instead, a sophisticated “purple-squirrel” type pipeline poaching approach will be required. And because the available talent is so sophisticated, most recruiters simply won’t have the knowledge base that is required to successfully identify, communicate with, assess, and sell top candidates. And incidentally, if you don’t have the capability to make a decision quickly, you may find that top AI talent will be gone within one third of your normal time to fill.

Currently, the most effective sourcing channels for AI/ML talent include referrals from your own AI employees, boomerang rehiring of lost talent, academic journals and conferences, contests/hackathons, and Masters-level interns. More sophisticated firms will use acqui-hiring or team lift-outs to capture intact AI teams.

Market research data will be required to identify their attraction factors  all potential prospects will be currently employed and well treated. And as a result, a sophisticated personalized data-driven marketing research approach (much like the executive search model) will be needed to identify each individual candidate’s attraction factors. And rather than being focused on pay, the most important attraction factors are likely to be the excitement of the work, the strength of the team, the ability to have a direct impact and your firm’s visibility in academic journals and conferences.

Firms will also need a strong retention component — because the demand for top machine learning talent is likely to continue to increase, the poaching of talent by other firms will become rampant. And that means that in addition to great recruiting, firms will also need a continuous retention program in order to keep the talent that they attract.

Final Thoughts

After nearly 40 years and recruiting, I don’t hesitate when I predict that this upcoming “War for AI talent” will be the most challenging recruiting competition in recent recruiting history. And a primary contributing factor covering why the recruiting competition will be so intense is because it’s almost impossible to increase the supply of available talent. The supply will remain relatively fixed for the near term first because universities produce so few Ph.D.s each year in this area. But also because unlike coding and other technology specialties, you can’t really learn AI/ML on your own, or even with corporate support. And finally, if you don’t get in on the ground floor and establish the fact that your firm is a serious AI player, you may never be able to catch up.

Author’s note: If you’re attending the spring recruiting conference in San Diego, please attend my Tuesday afternoon session entitled: Machine Learning/AI Roles Will Dominate Your Firm – How Recruiting Must Prepare


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