AI’s ‘most wanted’: Which artificial intelligence skills are adopters most urgently seeking?

Most early adopters face AI skill gaps and are looking for expertise to boost their capabilities. According to Deloitte’s global study of AI early adopters, 68 percent report a moderate-to-extreme AI skills gap.¹ What are the “most wanted” roles to fill these gaps?

According to AI early adopters, the top four most-needed positions are “AI builders” who are involved in creating AI solutions:

• AI researchers to invent new kinds of AI algorithms and systems
• Software developers to architect and code AI systems
• Data scientists to analyze and extract meaningful insights from data
• Project managers to ensure that the AI projects are executed according to plan

Beyond these AI builders, adopters are seeking “AI translators” who bridge the divide between the business and technical staff—both at the front and back end of building AI solutions:

• Business leaders to translate business problems/needs into requirements that guide the building of a solution, and to interpret results from an AI system and make decisions
• Change management/transformation experts to implement change strategies and help integrate AI into the organization’s processes
• User-experience designers to make AI systems easier to navigate
• Subject-matter experts to infuse their domain expertise into AI systems

When we compare companies with relatively little AI experience (they’ve built five or fewer production systems) with those possessing extensive AI experience (they’ve built 20 or more production systems), we observe an interesting shift in “most wanted” roles (see chart). Early on, AI researchers are the most sought-after, with about a third of the less-experienced rating them a top-two needed role. Business leaders rank near the bottom. By the time adopters have become highly experienced at building AI solutions, business leaders have bubbled to the top, and AI researchers have sunk almost to the bottom.

What can we make of this curious flip? Many companies embarking on AI initiatives may feel they need to hire AI superstars—researchers with advanced degrees who can invent new AI algorithms and techniques—to spearhead their efforts.² By the time organizations have amassed a lot of AI experience, they may have filled their ranks with enough of these brilliant experts. At that stage, they’re eager to find business leaders who can play the crucial “translator” role: figuring out what the results from AI systems mean, and how they should factor into business decisions and actions.

Is it possible that the less-experienced AI adopters are placing too much emphasis on finding AI researchers, who are scarce and in such high demand that they command princely salaries?³ These AI heavyweights are called for when one needs to invent new AI algorithms and techniques or create highly customized, domain-specific solutions.⁴ But not all companies will need to push these boundaries, and many can turn to an array of AI tools that can be used by software developers without deep AI expertise, such as machine learning application program interfaces (APIs), cloud-based AI services, pretrained machine learning models, and even automated machine learning (AutoML).⁵ It’s worth noting (see chart) that software developers, data scientists, and project managers retain their rankings—these are the professionals that can plan, architect, and build AI projects, and make use of existing AI tools and techniques to bring a project from concept to production.

From the start, AI adopters should first take a close look at how specialized their AI needs are. Then they can consider whether they really need AI superstars, or whether they can achieve their goals with available AI tools and an engineering team that can be trained to use them. They should also consider whether they’re placing too little focus on business leaders. In “The AI Roles Some Companies Forget to Fill,” the authors underscore the importance of involving business leaders early in the process: “Many companies rush into the AI race without clear objectives, hope a brilliant AI researcher and a technology team can create something great without guidance, and end up with little to show for it. Recruiting an AI quarterback to provide the business input, and ensuring success with well-defined metrics, is the most important job that most companies [frequently overlook]."⁶

This charticle authored by Susanne Hupfer on October 10, 2019.

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Endnotes

¹ Twenty-seven percent of executives surveyed view their AI skills gap as “major” or “extreme.” See: Jeff Loucks et al., “Future in the balance? How countries are pursuing an AI advantage,” May 2019.
² Megan Beck et al., “The AI Roles Some Companies Forget to Fill,” Harvard Business Review, March 14, 2019.
³ Cade Metz, "Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent," The New York Times, October 22, 2017.
⁴ George Seif, “Don’t make this big machine learning mistake: research vs application,” Towards Data Science, August 10, 2018.
⁵ “Cloud AI building blocks,” Google; “Machine Learning on AWS,” Amazon; “AI tools for business,” IBM; Parul Pandy, “AutoML: The Next Wave of Machine Learning,” April 18, 2019.
⁶ Megan Beck, et al., “The AI Roles Some Companies Forget to Fill,” Harvard Business Review, March 14, 2019.