Where McKinsey—and Consulting—Go From Here

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1:21Bob Sternfels:I'm Adi Ignatius. I'm Alison Beard, and this is the HBR IdeaCast.

1:33Bob Sternfels:All right, Alison, this question's going to seem random, but I promise you it's not. What was your major in college? I was a double major in journalism and politics at Washington and Lee University. Okay, I was history at Haverford, another liberal arts college. If you're like me, you probably figured that your majors equipped you well for something like journalism, but maybe not so well for something like management consulting, which has tended to recruit people who studied economics or engineering, you know, or business. Yeah, I always thought of consulting and finance as a place where sort of all the most ambitious, highest achievers, most capitalistic students wanted to go.

2:13Bob Sternfels:And those firms definitely focused on the Ivy League and the big universities. The world is changing. We know that. AI is remaking everything, including the world of management. And my guest today, McKinsey's global managing partner, has a lot to say about how it is rethinking its business in this era. So McKinsey already views its first AI agents as very much part of its workforce and is rapidly expanding that part of its team. But while AI is really good at linear problem solving, it's not so good at out-of-the-box thinking, which means McKinsey is rethinking its talent needs. I'm not going to spoil it, but your journalism politics double major might line up well with what the consulting giant is increasingly looking for.

2:57Bob Sternfels:So here's my interview with Bob Sternfeld, global managing partner at McKinsey, which is celebrating its 100th anniversary this year.

3:08Bob Sternfels:So I want to start. McKinsey is turning, maybe has turned 100. HBR, by the way, we're 103. So welcome to the Century Club. You know, how would you summarize the company's 100-year legacy? To what extent has McKinsey created the ideas that have shaped the business world? Or to what extent is it about identifying and suggesting best practices that come from elsewhere?

3:33Diana Wu David:I guess I would start with the nature of how we do our work. And the idea is, look, we co-create with our clients. And when we're at our best, it is figuring out how do we help clients get to places they can't get to themselves. The whole notion in some ways of credit, of did you create something novel or did you best practice? Maybe the way we frame is we're co-creating with clients to help them come up with things that they may not have come up with themselves, or we might not have come up with themselves. And we clearly invest a boatload in proprietary IP. We invest over a billion dollars a year in innovation, new thought, new ideas.

4:11Diana Wu David:Our McKinsey Global Institute, for example, which is a bit of our independent research tank, but we've now created McKinsey Health Institute. I think if you do look at their history, a lot of that is novel thinking. The most recent looking at a global balance sheet and kind of saying, how's the world when you look at assets and liabilities, that's not a best practice. That's new thought. But equally, part of the reason we stay global as an operating model is then at the more micro levels, the clients that we serve don't want to understand what the leading edge innovation is in their particular country.

4:49Diana Wu David:They want to understand innovation around the world and perhaps even cross sector. And that is part of our model. So I'd probably say it's, if I had to guess, it's probably half-half on this around where are there truly novel co-creation and where is this idea of figuring out how do you bring innovation around the world to clients who may not have access to that innovation based on their, you know, the reality that they're in.

5:16Bob Sternfels:So one area of innovation obviously is AI, and I'm sure you're advising companies all the time on how to adapt to an AI-driven world. I want to talk about that, but I'm interested in the internal discussions that are happening at McKinsey about this. To what extent is AI shifting the economics of your industry, headcount, pricing, productivity? Are margins improving? Are they under stress? I mean, what are the internal conversations about AI for your business? AI?

5:45Diana Wu David:We haven't talked about that at all. No, I'm just - You should. It's really cool. I mean, it's hard to have a conversation in any context right now that doesn't link back to some form of AI. You know, I think this question around AI and how it impacts McKinsey, there's kind of two sides of that coin. There's a client-facing side, and then there's a way of the implications on McKinsey. there's a unique moment to help all of our clients reimagine themselves leveraging this technology. And to be able to do that, we have to rewire ourselves to be able to deliver that. I may, Adi, just start with a bit of what I'm actually hearing from clients around the world, across all industries and across all geographies.

6:30Diana Wu David:And I really hear in the truth room maybe two things. On the one hand, an enormous belief in the potential for this wave of technological change. And that's everything from enormous productivity gains in areas like customer care or back office processes, but also to growth stakes. If you think about radically shortening the time of drug discovery and what that actually means for longevity and life, and that's top line growth. So folks are excited and they're believers. But at the same time, from the conversations that I have with CEOs, they'll often say, hey, Bob, so do I listen to my CFO or my CIO right now?

7:15Diana Wu David:My CFO is in my ear that we're spending a lot of money on technology, but we're not yet seeing enterprise level value from this. And so do we really need to be at the cutting edge or why can't we be a fast follower? Let other folks figure out where this is and then we'll adopt quickly because it's a lot more efficient to be a follower than a leader. CIO is saying, are you crazy? This is one of those moments. And if we're not in the lead, we're going to get disrupted. You know, we spent a lot of time looking at at least what we think the answer is here. And I know we spent a lot of time talking about technology.

7:51Diana Wu David:But what we're finding is half, if not more, of the secret sauce is organizational change as opposed to technology implementation. This is for large, large enterprise. And, you know, it's things like, well, what does your work look like after you're implementing things? Could you have, for example, a much flatter organization that cuts out a lot of middle layers and makes your organization faster? When you think about really complicated workflows, think about a mortgage process. You got all these steps, you know, origination, credit scoring, collection, after service, etc. Those are all departments in a bank.

8:32Diana Wu David:Well, why do you have four or five departments in a process if you can really enable this through AI? Can't you break those walls down? And so we're spending a lot of time thinking through not only what's the strategy and how do you implement, but how do you change the organization? How do you rewire the organization to realize the value? And if you can get that right, all of a sudden the CFO and the CIO are actually on the same page. But we're finding that's harder and it's taking longer than people thought. And so I do think we're going to be in this period where really enterprise fundamentally changing themselves.

9:04Diana Wu David:Yes, enormous potential. It's going to take a little while to get there. So then you kind of say, okay, what does that mean for McKinsey? We're applying this to ourselves. I often get asked, how big is McKinsey? How many people do you employ? I now update this almost every month, but my latest answer to you would be 60 ,000, but it's 40 ,000 humans and 20 ,000 agents. A little over a year and a half ago, that was 3 ,000 agents. And I originally thought it was going to take us to 2030 to get to one agent per human. I think we're going to be there in 18 months. And we'll have every employee enabled by at least one or more agents.

9:43Diana Wu David:That's kind of one piece of what are the assets and technologies that we're building in ourselves. The other big piece is how does it change our model? We're coming around to the conviction that we're migrating pretty quickly away from, let's call it, pure advisory work, which was a lot of the origins of our firm and kind of a fee-for-service model, etc. And what's it moving to? it's moving to much more of an outcomes-based model where we say, look, let's identify a joint business case together and we will underwrite the outcomes of that business case. And it aligns our interests with our clients a lot more.

10:21Diana Wu David:And I think will be the way of the future.

10:24Bob Sternfels:Now, I mean, AI is going to get better, right? We're still in the early phases of generative AI. You know, if technology can continue to commoditize, you know, even the kinds of analysis and insight that, you know, McKinsey has long provided, what will clients actually be paying for when they could probably do a lot of that themselves?

10:47Diana Wu David:The kind of problems that we have tackled with our clients over 100 years has not been static. It has changed radically. I'm now considered a dinosaur in our firm because I'm a little over 30 years with us. But the stuff that I did when I joined as an associate 32 years ago, we wouldn't consider even doing right now. Why? Because clients do that stuff themselves. And we are solving much more complicated, interconnected questions with our clients. And I think what this is going to then mean is this is just going to be that next evolution of there'll be a whole bunch of things that a couple of years ago we did for our clients that our clients will do for themselves.

11:30Diana Wu David:And the imperative will then be to move to the even more complicated questions. And to your point, what are clients going to pay us for? They're going to pay us to find ways to double their market cap. And until we get to CEOs who say, I don't want to double my market cap, I think there'll always be a more complicated set of questions and opportunities out there.

11:50Bob Sternfels:What is the evolving skills profile then of a management consultant? Do you even know yet? I mean, what are you looking for in new hires? And that must be evolving pretty quickly.

11:59Diana Wu David:I might frame it in what are some things we're confident about now? And then what are some things we're exploring? When I came into this role, and it was a little over four years ago, I asked our talent attraction team, how are we doing on attracting talent? And I got a, we're doing great, Bob. We're doing great. I said, well, why? We get over a million applications a year and we hire anywhere between eight and 10 ,000 people. And even the million aren't a normal distribution, right? It's some of the brightest minds in the world that are applying. So effectively, what's the problem? Why are you asking me?

12:32Diana Wu David:Let me go back and do my job. And I kind of kept asking the question, but how many profiles are we really looking for? What are we systematically screening out? And it turned out that really when you boiled it down worldwide, there's really only 500 pathways that would lead you to McKinsey. And that wasn't tying with what our own organizational research was saying, that the half-life on skills was getting shorter, that people were too focused on paper sealing, did you have the right credentials? And so we actually applied analytics on ourselves and took the last 20 years of data and said, what are the skills and characteristics that are most likely to make partner in McKinsey?

13:13Diana Wu David:Because that's not perfect, but it's kind of a marker of success. Only one in six hires make partner. And it turned out we had some bias in our system. We had like 50 different implications, but I'll give you the biggest three. One was that we were too focused on did you have perfect marks versus did you have a setback and recover. And the applicant who had a setback and recovered was more resilient and more likely to be a higher probability of making partner in McKinsey. And we weren't screening for that. So we've changed the process to look for resilience in the application process. You know, it may sound shocking, but we weren't indexing enough on had you had real experience in a significant way working with others.

14:02Diana Wu David:You know, fundamentally, we're about helping our clients change. And if you've done a team sport or if you've worked in retail, you know, working your way through college, you've had to engage with others. And it's built a skill, human to human skill, that we're now indexing a lot more on. And then the last one was, did you have the aptitude to learn new stuff versus had you mastered the subject that you chose to study? My youngest guy used this one against me, actually, when he was changing his major for the third time. And I was a little frustrated with him. And he's like, but dad, you published a paper that said, you know, if you have the aptitude to learn new stuff.

14:42Diana Wu David:And I'm like, yeah, you have to do well in the subject that you go to.

14:46Bob Sternfels:This kid's smart.

14:47Diana Wu David:Yeah. Send his resume to me. It's the only time he quoted me, Gadi. I was like, really? But it did get us to then change our assessment techniques where we purposely create an environment where no human in the world will have any pattern recognition in this environment. And we figure out how well do you do in an environment where you have no pattern recognition and you just have to go figure things out. So those are some of the skills now we know we're moving to. We've launched more of an exploratory look now going forward where you say, okay, but if everyone gets superhuman with these AI tools, what do you want to add on top of this resilience, teamwork, ability to learn new stuff?

15:27Diana Wu David:That part I just told you I feel pretty solid about. This part I'm going to tell you is places we're more in exploration mode. One of them is, well, what do the models not do well? They don't aspire. they're not good at setting the right aspiration level. When you think about what great leaders do, they help an organization set the right, ask what should we aspire to? And so how do we start to look for and develop the skills of leadership? Leadership is going to be durable in a post-AI world because one of the great things leaders do is they help set an aspiration and get people to stretch. The second is judgment.

16:04Diana Wu David:And you've seen time and time again, these models, there's not truth in the model. There's not judgment in the model. Humans need to impose those parameters. So how do you build judgment and how do you build that capability? And then maybe the last one we're spending a lot of time on is the models are inference models. They're great at a linear approach to problem solving, which is kind of what we've been teaching for the last hundred years. What they're not great as discontinuous leaps, truly novel thinking. And so we're starting to figure out where are backgrounds that are going to be more creative to come up with things that aren't about the next logical step, but are about a discontinuity.

16:47Diana Wu David:And so we're going back to liberal arts degrees and kind of saying, hey, let's come back to some of the things that might have been deprioritized in the past to see if we can get a little bit more creativity.

16:57Bob Sternfels:Yeah. We had a piece from the chief technology officer of Goldman Sachs who had wanted his child to be a coder and then thought, nope, I think she should be a philosophy major, that that's kind of what this world calls for.

17:11Diana Wu David:Maybe, right?

17:16Bob Sternfels:Legal teams are under more pressure than ever. More data, more complexity, more scrutiny. They need AI built for the realities of legal work. For more than a decade, Relativity has invested in AI built specifically for legal teams, designed to meet legal standards and support defensible decisions. The result is explainable AI that handles tedious tasks, so judgment and critical decisions stay where they belong with you. Relativity is the AI platform for legal work. Learn more at relativity.com slash ideacast.

17:52Bob Sternfels:We've sort of looked back, we've looked forward, you've talked about some of the celebratory stuff that's going on. I need to ask, though, you've also had your share of unwelcome publicity in recent years, and I'm talking about OxyContin, bribery charges in South Africa, conflict of interest accusations in the U.S. and elsewhere. How do you account for all of that? How do you look at what happened and why?

18:14Diana Wu David:Well, I'm glad you asked. I think it's been a real soul-searching for us, starting about four or five years ago, of really two questions, Ayi. On the one hand, where should we be more humble and learn from our mistakes? And at the same time, where should we be more courageous and just say, look, we disagree with you. And even though we'll face criticism, we're going to push back because that's what we believe in. And let me kind of parse each of those. You know, some of the ones that you mentioned fall clearly in the humble camp. And I think some of the great learnings that we had, if I just take two in particular, opioids, the work with opioids and our partnerships in South Africa.

19:02Diana Wu David:You know, what we learned is that we have to have a higher diligence around client selection. And we put in place a framework that is a really robust assessment now that looks across every aspect from the country, the topic, the institution, the individuals, and the operating environment to say, is this a client that we actually want to bring in to the firm? And so one of the languages that I've used with our partners, and it's hard to drive change in a partnership, is you all grew up with the idea that one of the privileges of a partner is to commit the firm. And I'm not saying that's not the case anymore, but you don't do it alone.