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Functional specialists, not techies, will decide the AI talent race

Computer scientists are important, but business specialists with the skills to work with AI—and AI experts— are crucial for enterprise success. This is just one of eight PwC predictions about how AI will shape business in the coming year.

As AI spreads into more specific areas, it will require knowledge and skill sets that data scientists and AI specialists usually lack.

Consider a team of computer scientists creating an AI application to support asset management decisions. The AI specialists probably aren’t experts on the markets. They’ll need economists, analysts, and traders working at their side to identify where the AI can best support the human asset manager, help design and train the AI to provide that support, and be willing and able to use the AI effectively.

And since the financial world is in constant flux, once the AI is up and running, it will need continual customizing and tweaking. For that too, functional specialists—not programmers—will have to lead the way. The same is true not just throughout financial services, but in healthcare, retail, manufacturing, and every sector that AI touches.

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Citizen data scientists wanted

AI is becoming more user friendly. Users no longer need to know how to write code in order to work with some AI applications. But most still demand far more technical knowledge than a spreadsheet or word processing program does.

For example, many AI tools require users to formulate their needs into machine learning problem sets. They also require an understanding of which algorithms will work best for a particular problem and a particular data set.

The exact level of knowledge required will vary, but we can broadly divide AI’s demands on human knowledge into three categories. First, most members of an AI-enabled enterprise will need some basic knowledge of AI’s value as well as what it can and can’t do with data. Second, even the most mature AI program will always need a small team of computer scientists. The third group, which many organizations aren’t yet paying attention to, are AI-savvy functional specialists.

They won’t have to be programmers. They will have to understand the basics of data science and data visualization and the basics of how AI “thinks.” They’ll have to be citizen data scientists.

Retail analysts, engineers, accountants, and many other domain experts who know how to prepare and contextualize data so AI can make optimal use of it will be crucial to enterprise success. As AI leaves the computer lab and enters everyday work processes, these functional specialists will be even more important than computer scientists.

Implications

Faster upskilling means faster AI deployment

Enterprises that intend to take full advantage of AI shouldn’t just bid for the most brilliant computer scientists. If they want to get AI up and running quickly, they should move to provide functional specialists with AI literacy. Larger organizations should prioritize by determining where AI is likely to disrupt operations first and start upskilling there.

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Upskilling will lead to new approaches to learning

Organizations will have to upskill many of their employees to learn the basics of data science and how to think like an AI application. Given the enormity of this task, companies must find ways to assess the skills of high-potential learners and put them on individual learning paths, to get them up to speed quickly.

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Explore our other predictions

Work, not jobs Practical AI Big data ROI AI cyberwar Explainable AI AI arms race Responsible AI

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Anand Rao

Anand Rao

Global AI Lead; US Innovation Lead, Emerging Technology Group, PwC US

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Chris Curran

Chris Curran

Chief Technologist, New Ventures, PwC US

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Michael Shehab

Michael Shehab

Principal, Labs & Tax Technology, PwC US

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