Companies adopting AI across the organization are investing as much in people and processes as in technology.

Simon London: Hello, and welcome to this episode of the McKinsey Podcast, with me, Simon London. Today we are going to be getting practical with artificial intelligence. By now, it’s common knowledge that AI holds immense promise across a wide range of applications—everything from diagnosing disease to personalizing websites. But how far are most companies along the road to adoption at scale? When you look at the organizations furthest ahead, how did they get there and what are they doing differently?To answer these questions, I spoke with a couple of McKinsey partners who are working with clients on exactly these issues.

Tim Fountaine is a partner based in Sydney, Australia, and Tamim Saleh is a senior partner based in London. Tamim and Tim, welcome to the podcast. Thank you very much for being here.

Tamim Saleh: Thank you.

Tim Fountaine: It’s a pleasure to be with you.

London: We’re going to be talking not just about the exciting promise of AI, which to be clear is very real, but how in practice—on the ground in real organizations—the promise can be realized. Tim, maybe you take first crack at this. What do we know about how far along most companies are in the journey?

Fountaine: Well, I think you’re right. There’s a lot of excitement about the potential of AI, and there are some wonderful examples of AI making real progress and being able to help with diagnosing diseases and healthcare, improving customer experiences, and so forth. But most companies that we’ve talked to in the last few years are not making progress at the pace you might assume from all the newspaper articles. In fact, we did a recent survey of 1,000 companies, and we found that only 8 percent of firms that we surveyed engaged in practices that allowed widespread adoption of AI.

The vast majority of companies are still at the stage of running pilots and experimenting. We still believe that AI will add something like $13 trillion to the global economy over the next decade, but putting AI to work at scale remains a work in progress for most companies.

London: The companies that are doing this well—the 8 percent you mentioned that are putting the practices in place to get to scale with AI—what are they doing differently?

Fountaine: The first thing is they tend to be ahead [in] digitization, generally. There are particular industries where that’s happening more. For example, financial services, telecoms, media, high tech—they’re really leading the way, as you can imagine. They don’t have physical products to the same extent as other industries. They’re really about data and digital information, so, of course, AI is highly applicable in these industries. But no matter which industry companies are in, the ones that are doing the best are paying real attention not only to the technology but also thinking about how it changes their organizations and what kind of culture they need to build in order to be able to take advantage of these new technologies.The ones we see doing well are doing three things right.

The first is, organizationally, they’re moving from siloed functional work to cross-functional teams where people from the business, people from analytics, IT, operations all work side by side to achieve particular outcomes.

The second one is changing how they make decisions. It’s much less top-down, much less judgment based, but much more empowering frontline teams to make decisions not only using judgment but also using algorithms to help improve the way they make decisions.

Finally, there’s something about mind-set, something about moving from being risk averse and only acting when you have the perfect answer to being much more agile, willing to experiment, being adaptable, being willing to fail fast, but learn fast and get things out quickly.

London: Yes. I mean, on the one hand, that makes a lot of sense. On the other, what you’re describing there, Tim, sounds like wholesale change. It’s a lot of change on a lot of different organizational dimensions. Tamim, let me bring you in here. In practical terms, in your work with clients, where do you even begin?

Saleh: One of our clients, for example—a leading European steel manufacturer—wanted to industrialize AI. It wasn’t just about doing a number of pilots or MVPs [minimum viable products] or tests. The CEO, who I remember in the very first discussion we had with him, looked at the problem as a people problem. He didn’t want a technology story or “here are the use cases.” He actually asked a question: “How will my people deliver AI? What kinds of skills do they need to have? How do I fit this into our culture?”

Some of the things that they looked at, for example, were to understand what proportion of their organization needs to be [technologically] literate. They quickly came to the conclusion that the concept of a translator—people in the business, whether they are in operations or in sales or in quality management, who understand how analytics are applied—was needed.

Then they used their knowledge to work with the data scientists and the data engineers to produce the initiatives and the use cases and industrialize and deploy them and make sure that they continuously developed. They budgeted, for example, for the adoption, the training, and the development of people as much [as], if not more than, for the technology itself.

They spent a lot of time on training. They built an academy for analytics that trained 400 of their 9,000 workers in the first year. That led them, within a period of 18 months, to produce 40 initiatives, with a 15 percent EBITDA [earnings before interest, taxes, depreciation, and amortization] improvement. If anything, they are continuing to accelerate the level of application of analytics. In fact, the objective is that the penetration of analytics will be in everything that they are doing. It becomes business as usual. The key lesson learned out of all of this is that when a company wants to apply analytics, they should look at the problem not just from the technology end or the data quality but the people side and the mind-set.

Fountaine: One of the things we often see companies getting wrong is they’re building analytical models—AI models—but really failing to think through how does that change the business. I think one of the things the companies that are getting it right have realized is that AI is just another tool for solving business problems or achieving business outcomes. As such, AI is a way of changing a workflow, changing the way that people work together.

One of the things we’ve found in our survey is the companies that were doing best were spending as much of their money or budget on change and adoption—workflow redesign, communication, training—as they were on the technology itself.

London: Let me just clarify there. Companies are spending as much on training and adoption as they are on the actual technology. Because I think a lot of people might find that surprising.