Demystifying AI and machine learning for executives

In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them.

Sean Brown: From McKinsey’s Strategy and Corporate Finance Practice, I’m Sean Brown, and welcome to Inside the Strategy Room. I’m joined today by Tamim Saleh. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he’s speaking about AI and machine learning.

Tamim, one of the things you’ve talked about is the notion of five different developments of AI. I’d like to first focus our discussion on the impact of machine vision on AI.

Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don’t make the same mistakes we do, because they capture things the way they are, and they don’t see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera.

So there are a lot of business problems [that image technology can help]. Take, for example, mining, where traditionally people—geologists—will go and look at the ore, spend some time, and write a report.

And then you adjust the angle of digging accordingly, and then you do this once a week to optimize the yield. Now you can do this in real time. There are cameras that can actually monitor the geology and, in real time, adjust the angles of digging. For a mining company this could be worth hundreds of millions of dollars.

This also could be applied in safety, for example, in oil and gas, where the cameras monitor people’s movements. And if there are any likely compromises, the algorithms would give warnings and something could be done. In fact, there are hundreds and hundreds of use cases or real-life business problems that will be resolved by image.

Imagine this: the amount or the level of information that you get through a combination of image and sensors is up to a billion times more than the traditional methods. And with machine learning, when you get so much input, you get the most out of machine learning much faster. And this era is just starting.

Sean Brown: What are some of the gating factors? What is keeping companies from adopting this notion of machine vision?

Tamim Saleh: There are probably two things. And neither of them is technology. One is talent—people who know how to use image analytics in a useful way in business; people who could translate this into actionable, tangible outcomes. There is a quiet revolution going on in the world where, gradually, different companies are training and skilling people to translate these types of problems into action, but it’s taking time.

Problem number two is, how do you connect these types of propositions to an existing business model that doesn’t work that way? If you’re an insurance company, for example, to capture a claim, you still have to fill forms and call the call center. Some insurance companies are beginning to say, well, actually, if I take the image, and I run algorithms on it, it’s much more accurate than somebody calling me. They could lie in the first place, and I fill a form through an agent. But it’s going to take time.

Sean Brown: So they take an image of the accident, of where the accident was?

Sean Brown: Interesting. You talked a little bit about machine learning. That is one of the other big developments of AI.