ESSENTIALS FOR LEADERS AND THOSE THEY LEAD

By now, the potential economic payoff of generative AI (gen AI) is almost undisputed: our research shows that the technology could add trillions of dollars in value to the global economy. But despite its beneficial impact on a range of functions and industries, gen AI poses considerable challenges and risks that leaders may need to prepare for even as they pilot the technology. Here’s a roundup of our latest thinking on gen AI and its implications—positive and negative—for organizations.

AN IDEA

Pursue both speed and safety

The buzz around gen AI has been increasing, but so has concern about its risks—and that can be a problem for leaders who wish to take advantage of its potential for immediate positive impact. In a recent survey, we found that 63 percent of respondents rated gen AI implementation as a high priority, yet 91 percent did not feel ready to manage the process in a responsible manner. We suggest that leaders address four primary sources of “inbound” risk—threats likely to affect organizations regardless of whether they deploy gen AI. For example, it’s important to establish a governance structure that includes expertise, oversight, and training for end users. “Organizations should develop a set of guiding principles agreed on by the executive team and the board that will guide AI adoption and serve as a guardrail for acceptable use cases,” note McKinsey senior partners Ida Kristensen and Lareina Yee and their colleagues.

A BIG NUMBER

7

That’s the number of actions data leaders should consider to help scale generative AI solutions. The first of these involves letting value determine data strategy. For example, gen AI may be just one part of an organization’s broader data portfolio. “Much of the potential value to a business comes from traditional AI, business intelligence, and machine learning,” caution McKinsey experts. “If CDOs [chief data officers] find themselves spending 90 percent of their time on initiatives related to generative AI, that’s a red flag.”

A QUOTE

“It’s important to bear in mind that successful gen AI skills are about more than coding proficiency.”

That’s McKinsey experts on what it takes for a worker to be truly prepared for gen AI on a day-to-day basis. A successful AI team member may need to have design and collaboration skills, contextual understanding to ensure that the most high-quality answers are generated, and forensic skills to figure out the reasons for common breakdowns. “We see a rapid market emerging for people who have learned these skills over the past year,” observe senior partners Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel. “If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies.”

A SPOTLIGHT INTERVIEW

“What we know from our product development research is that, oftentimes, the problem is you close the funnel too quickly,” says McKinsey partner Michael Chui in an episode of The McKinsey Podcast on how companies can use gen AI to gain competitive advantage. “You don’t continue to look at multiple alternatives.” Chui adds that gen AI enables organizations to consider a variety of creative options, as well as enabling individual knowledge workers to increase their productivity. “You can end up with better products at the end of the day, create whole new categories, create new drugs, create systems that are more sustainable and produce less carbon emissions,” he says. “All the kinds of things we want to do with R&D, we can create a superpower by using these technologies.”

– Edited by Rama Ramaswami, senior editor, New York

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