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McKinsey: You can't do enterprise AI without digital transformation

NEW YORK — Hardly a day goes by without a prediction about how artificial intelligence will radically change our lives — driving our cars, running errands, sucking up jobs. But what is the state of enterprise AI?

In a McKinsey & Co. survey of 3,000 executives conducted earlier this year on enterprise AI, only a small percentage reported they are using AI either in their core business or at scale. The vast majority of respondents — 80% — are either thinking about AI or experimenting with it. In both scenarios, companies are still figuring out use cases and what kind of talent and technology is necessary to reap value out of their AI investments.

Michael Chui, partner and head researcher at the McKinsey Global Institute, used the phrase “scratch the surface,” to characterize the involvement of CXOs in AI projects. Wholehearted buy-in is what it will take for enterprise AI to succeed.

“We do think that that’s actually going to be required in order to move the needle from a corporate standpoint,” Chui said during his presentation at the Strata Data Conference.

CIO role in jump-starting enterprise AI

Still, CIOs shouldn’t sit idly by. Chui said they can lay the groundwork for enterprise AI by understanding where the potential is and where companies want to prioritize “based on both the size sides of the prize as well as the ease of capturing that prize.” That may come off as an obvious piece of advice, but “most people don’t do it,” he said. “Lots of people just buy what’s in the sales person’s bag when they show up.”

And CIOs can continue advancing the company’s digitization efforts. “If we just look at technical assets deployed, what we found was those companies that have actually moved the furthest in their digitization story are also the ones who are able to take most advantage of AI,” Chui said. “So there are no shortcuts here, is another way to put it. If you’re going to go on an AI journey, you simultaneously need to go on the digitization journey.”

Understanding the data ecosystem, digitizing infrastructure, accumulating training data, and making data easily to get to are the foundation of enterprise AI.

Early adopters have moved beyond experimentation, he said, to integrate AI into core processes and find ways to scale the technology across the enterprise. The McKinsey research shows that, as with cutting-edge technologies that have come before it, CIOs should expect talent and culture — not technology — to be the biggest enterprise AI hurdle.

“If you have terrific insights, if you have a better forecast, and you don’t change the way that your company operates … you’re not going to move the needle. So you’ll need these two-handed athletes,” he said, referring to employees who are capable of solving the technical problems as well as moving the organization forward.

AI market behavior

While Chui’s advice may sound like familiar territory, not everything about the adoption of AI technology is following the usual patterns.

One striking difference is how the AI market is currently shaping up. Venture capital companies, which typically take the lead in emerging tech by investing heavily in startups, are being outpaced by the investments big companies are making in their internal AI capability. Indeed, companies appear to be spending three to four times more on their internal AI research and development than the amount currently being invested in startups by the VCs, according to McKinsey research.

“That isn’t necessarily what we think of as happening early on in the development of technology,” Chui said. “We often think VCs invest in the small companies, they become big companies, and the big companies are slow getting to it.”

Still, for VCs, the AI market is “one of the areas that has the greatest growth rate in terms of where the dollars are going,” Chui said. And, at least in 2016, VCs are placing “a slight majority” of their bets on machine learning technologies .

Chui warned the Strata audience to take the data he was presenting with a grain of salt. What constitutes an external investment in AI can be hard to categorize and internal investments can be hard to track because they are not precisely reported.

“But it is interesting that even at this early stage, a lot of the investment is being made by the giants, and it’s being made on internal R&D,” he said.