Artificial intelligence research: Dr. Michael Chui, a partner at the McKinsey Global Institute, speaks with CXOTalk about his latest report on AI, automation and the impact of technology in the workplace.

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Chui leads McKinsey’s business and economics research arm in analysis of Big Data, Web 2.0 and collaboration technology, and the Internet of Things. He’s a frequent speaker at global conferences and consults for high-tech, media, and telecom industries on strategy, innovation and product development, IT, sales and marketing, M&A and organization.

From the transcript:

Michael Chui:

(00:03:28) Defining artificial intelligence, you could go for hours debating it. Roughly speaking, we would describe it as using machines to do cognitive work, to do the work that comes about primarily because of our brains. But, as it turns out, even from my graduate research studies, we know that not all of our intelligence is just trapped in our brains. It's also part of our bodies, et cetera. And so, we understand that, in many cases, artificial intelligence itself might enter the physical world and be things like robotics and autonomous vehicles, et cetera. But, it roughly has to do with intelligence and then the machines that instantiate it.

(00:11:07) Michael, we're at this phase where it's clear that there's all of this potential across many, many different, almost every sector of industry, as you described. Yet, very few companies have deployed artificial intelligence technologies at scale. That means that at this stage there is also a large understanding gap and execution gap. And so, how do organizations fill those gaps? What steps do they need to take?

Michael Chui:

(00:11:42) Well, I think, in terms of steps that need to be taken, it's not the first time when we've seen a new family of technologies enter the scene, which has great potential and where it's fairly early. If you think back to cloud, if you think back to mobile, these are technologies which are transformative but take time to actually embed into a business.

(00:12:05) First of all, as we were talking about, you need to have an understanding of the technology itself. You need to raise the waterline. That can often start with the technologists. We've said this so many times when we've talked about any technology. It's not just the technologists who have to understand something about it because you really do need business leaders writ large to start to understand what's possible. If you don't understand the art of the possible, you're not going to be able to identify and prioritize opportunities. I think that understanding the technology is number one.

(00:12:37) Secondly, again, because we're at this early stage where the potential is increasingly being recognized, we're starting to see more and more vendors show up at our doors carrying AI solutions in their bag. That's great. I think that's part of taking those meetings as part of that education. But, I think often a failing would be to be captured; to find something so exciting that you just want to go ahead and do it. I think making sure that, again, not that you may need to take years to do this, but very quickly understand the portfolio of possibilities so that you're actually spending your time on the types of solutions which will drive the needle for your business.

(00:13:21) For instance, I talked about the 500 or so use cases that we looked at. Just two broad categories where we've seen huge amounts of potential: one is basically on the customer-facing, sales, marketing, [and] customer experience side. This is everything from improving your next product to buy recommendation to being able to segment a customer who comes to you so that you provide them with much more personalized and customized interactions. Then another broad category, which let's just describe it as operations improvement, and so whether or not it's identifying waste, reducing inventory costs, managing your energy costs, logistics, supply chain, et cetera. It's another broad, broad category.

(00:14:08) Again, if you understand what type of business you're in, you'll understand which of these needles, if you tuned it up by 10%, 15%, 50%, which one will actually drive the most value for you. I think understanding that and then, of course, the execution against it is so, so important because, again, that last mile, right? We see this pilotization, you know, pilotize-itis, maybe, right? It's great to run a pilot. It seems to be successful, but the real hard work then is how do you, day-to-day, change that process which is really going to drive benefit at scale? That's the hard work we do on change management every day, but this is a different set of underlying technologies. We'll need to learn how to do that in every business.

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