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AI implementation is a winner-take-all race, analyst says

Your AI strategy should focus on growth rather than efficiency, says McKinsey analyst Jacques Bughin -- advice that enterprises rarely hear when launching projects.

Conventional wisdom states that in the early stages of AI adoption, enterprises should grab low-hanging fruit. They should start a point project to automate a repeatable process and pocket the efficiency gains.

But that's not Jacques Bughin's advice for building a strategy around AI implementation.

"AI is more about growth than efficiency," said Bughin, director of the McKinsey Global Institute at McKinsey and Company, in a webinar hosted by MIT. "Don't look for marginal projects. Be bold. It's about thinking about what's new. It's about a reinvention rather than a marginal use case."

According to Bughin, technologies like natural language processing and generation, computer vision, and advanced robotics -- some of the most advanced areas of AI today -- don't generally have much to do with efficiency or the automation of existing processes. They have the potential to enable entirely new processes, making enterprises more competitive in existing markets, helping them reach into new markets and enabling them to potentially lead the development of new products. He mentioned Uber as an example of a company that used machine learning to create a market.

AI is more about growth than efficiency.
Jacques Bughindirector of McKinsey Global Institute, McKinsey and Company

Bughin's advice runs counter to what a lot of enterprises are doing with AI today. Most are taking a cautious approach and only proceeding with AI implementations in incremental ways, if at all.

At the Gartner Data and Analytics Conference in Dallas in March, analyst Whit Andrews said most enterprises today assume that they are behind their competition on implementing AI, but the truth is that a relatively small number of companies are using AI, and even fewer in a pervasive manner throughout their organization. Because the risk of falling behind competitors is minimal at this point, Andrews recommends a more deliberate approach to AI adoption

IBM chief architect Ruchir Puri explains the top three challenges to implementing AI in business.

But Bughin said businesses shouldn't aim to stay even with their competition; they should look to beat them to new customers and markets. And since AI implementation is relatively limited at this point, making smart use of it can be a substantial competitive advantage.

"There's no value in waiting to get to AI," he said. "Your competitors will, so you will either implement it or be playing catch up. If you do it, you get a chance to increase your profit pool. If you don't, competitors are going to eat your lunch."

In researching AI implementations in enterprises, Bughin has reviewed about 700 projects. His research shows that only 3-5% of businesses are using AI in a pervasive manner, and most of those are digital native companies. But for those that have implemented AI projects, he said the ROI on smaller point projects is practically nonexistent. The only companies he has studied that are seeing any kind of return on AI technology are the ones that are trying to drive its use throughout their organization.

Of course, this kind of AI-focused transformation is difficult, which is why most enterprises have yet to attempt it. And he said enterprises need to make sure they've undergone a general digital transformation, which will form the foundation for AI efforts. However, according to Bughin, the fact that it's hard probably means it's worthwhile.

"It's not easy," he said. "That means you're possibly going to be one of the few who dare to do it. If you are that person, you're likely to reap a lot of the benefits."

This was last published in April 2018

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