What a successful AI team really looks like

As more companies scale AI projects, turning proof-of-concepts into drivers of business transformation, a clearer picture of what it takes to succeed with real-world AI is taking shape.

When it comes to AI teams, a broader set of skills are required than previously known, with a particular need for people with experience in operations and in translating AI concepts into business terms and vice versa. In other words, AI success no longer hinges on just a group of data scientists anymore.

In fact, enterprises need blended teams to succeed with AI, says Louise Herring, partner at McKinsey & Co. “If you look at the technical side, the emphasis is increasingly on how we can make sure we have production-ready code and we have elements available for reuse throughout the organization,” she says. “But the key area of emphasis that we see first of all is about translators: people who can make the connection between the business and the technical side.”

Here is a look at how several organizations are assembling AI teams to solve business issues — and how advances in AI technology are changing the baseline skills necessary for success. 

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