Here is a brief excerpt from the transcript of a conversation involving Peter Breuer, Michael Chui, and Simon London for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.
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The potential for AI to infuse business and value chains across various industries is greater than ever before—but where should executives start?
Typically understood as being all about robots and whiz-bang machines, artificial intelligence (AI) can be tough for executives to wrap their business minds around. In this episode of the McKinsey Podcast, senior partner Peter Breuer and McKinsey Global Institute partner Michael Chui speak with McKinsey Publishing’s Simon London about burgeoning business applications of artificial intelligence, the line between hype and true use cases for AI, and how business leaders can separate one from the other.
Artificial intelligence in business: Separating the real from the hype.
Simon London: Hello and welcome to the McKinsey Podcast. I’m Simon London with McKinsey Publishing. Today, we’re going to be talking about artificial intelligence. It’s certainly a hot topic in the business press and also starting to attract quite a lot of attention in the mainstream media.
You’ve probably read pieces about everything from killer robots to the impact of AI on jobs. But what exactly is artificial intelligence? Just as important, what isn’t it? How can companies put artificial intelligence to work today in ways that are useful?
I’m joined today by two McKinsey partners who advise clients and conduct research on these issues. I notice they also have PhDs in adjacent fields, so as a liberal arts major, I’m finding this somewhat intimidating. First we have Peter Breuer, a senior partner based in Cologne, in Germany. Hello, Peter.
Peter Breuer: Hello.
London: And we have Michael Chui, a partner with the McKinsey Global Institute, based in San Francisco. Hello, Michael.
Michael Chui: Hi, Simon. Pleasure to be with you today.
London: Good. Let’s start, if you don’t mind, by defining our terms. When we talk about artificial intelligence, or AI, what do we mean? Michael, why don’t you just give us your view.
Chui: It’s interesting, this term is actually not a new term. It was first used over half a century ago. But basically it refers to using machines to do things that we consider to be “intelligent”—being able to either simulate or do things that we describe people as doing with their cognitive faculties.
London: Peter, anything you’d like to add to that?
Breuer: As Michael pointed out, the term was invented by Alan Turing in 1950. So it’s actually a pretty well-known field. We have seen a little bit of an acceleration lately because of two main factors.
Number one, the computational power is going upward with exponential growth. And, number two, the available data is going upward with exponential growth. Therefore you’ll see methodologies around machine learning and now going even deeper into deep learning with new neural networks that are applied to those vast amounts of data. So that you see, to some extent, the exponential growth in data, in computational power, leads now to the AI hype or AI development.
London: Michael, in a report that we published this summer, a McKinsey Global Institute report, we talked about there being five technology systems of which machine learning is just part of it. Did you want to run us quickly through what those five are?
Chui: Earlier this year we surveyed over 3,000 different business executives around the world to understand the degree to which they were deploying these types of technologies. They’re broad families of technologies, and they overlap a bit, but they are where some of the recent advancements and developments have been happening.
One of them is around physical AI, and so that’s robotics and autonomous vehicles. We’re seeing a lot of interesting things happen there. Second, computer vision—whether it’s image processing, video processing, et cetera—the deep-learning systems that Peter made reference to have made a lot of advancements there.
Similarly, around natural-language processing, whether it’s spoken language particularly, which is interesting, but also written language, we’re seeing a lot of natural-language work being done. Also, virtual agents or conversational interfaces. It’s a bit of an extension on natural language, which is more of a feature, but this is the ability for systems to roughly converse with you whether by voice or online through chats.
Finally, machine learning actually has tremendous applicability beyond the application of the other types of technologies I just mentioned. And hopefully we’ll have an opportunity to talk more about that.
London: Great. That’s really helpful. At least we know the territory we’re dealing with here. Maybe we can bring it even closer to reality with, as I go about my daily life, I suspect I’m already running into artificial intelligence in action. Are there things, Peter, that you see in your daily life that people will recognize are powered by AI?
Breuer: I think we all do, actually. With our smartphones, we all have supercomputers at our fingertips. Some of the elements that Michael mentioned, you can experience in your daily life. The improved spell-checks that you have when you’re typing an email or a message in your smartphone, this is all powered by machine learning.
Michael also mentioned language, the spoken word. You will notice that your Siri or Google Assistant learns every day, and the understanding becomes better every day the more you use it. That’s obviously machine learning in the background.
Most of us followed the exciting introduction of the new iPhone X, and there you also saw in the press conference, it’s all about machine learning now for face recognition, applied also, machine learning in face recognition to unlock your phone. So, I think we all experience it already with our smartphones, and going forward, we’ll see much more of it.
What we’re starting to see is these AI technologies underpinning a lot of the things, all the online and mobile services that we’re now increasingly taking advantage of. So, for instance, in e-commerce or media, when systems are providing you with suggestions for things you might be interested in, things you might be interested in reading or things you might be interested in buying—the next-product-to-buy use case, as we describe it—increasingly, those types of systems are powered not only by statistical methods, but by some of these AI technologies as well, hopefully bringing consumers closer to the things that they’d be most interested in.
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Peter Breuer is a senior partner in McKinsey’s Cologne office, Michael Chui is a partner of the McKinsey Global Institute and is based in the San Francisco office, and Simon London is a member of McKinsey Publishing and is based in the Silicon Valley office.