Rewired to win: Reimagining the enterprise with tech and AI

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[00:04:58]>> [music] >> Hello and welcome to McKinsey Live. I'm Lucia Rahilly, McKinsey's editorial director and your host for today's event, Rewired to Win.

[00:05:11]We are all just saturated with headlines around AI and by and large those headlines are full of big claims. At the same time, leaders are just staring down the barrel of mixed results. It's actually been pretty hard for many companies to realize the promise of AI, at least so far.

[00:05:33]Fortunately, that is of course not universally true and in McKinsey's work and research we're seeing a a set of large established companies prove that a specific constellation behaviors including discipline focus, strong leadership and very importantly the right operating model can help leaders translate tech into real meaningful value. So today we will get into what those successful companies are doing differently and how to take action now in your organizations to start delivering on the ROI of artificial intelligence.

[00:06:14]Joining me are two McKinsey leaders and co-authors of the new hot off the presses book Rewired: The McKinsey Playbook on how leading companies win with technology and AI.

[00:06:29]This is a revised and expanded version of the best-selling first edition of Rewired and covering the rapid acceleration of AI-driven transformation and what it takes to design an enterprise that out-innovates, outruns, and outperforms in this AI era we are in. Rob Levin is a senior partner at McKinsey and the North American leader of AI transformation at Quantum Black, which is McKinsey's AI arm. Rob works with a range of clients on AI and technology strategy and implementation with a particular focus on consumer packaged goods and health care.

[00:07:15]And Kate Smaje is also a senior partner and McKinsey's global leader of tech and AI. Kate shapes McKinsey's innovation agenda and she advises CEOs and senior leaders on how to outperform by integrating tech and data with organizational, cultural, and change capabilities.

[00:07:37]Rob, welcome to McKinsey Live and Kate, welcome back. You're a McKinsey Live veteran. Thank you. Nice to see you.

[00:07:45]It's great to have you both here. Folks in our audience, as always, very quickly McKinsey Live is designed to bring you closer to our experts and to our latest research. So please ask questions along the way and we will try to make some time to answer at least a selection of them as we go. Okay, let's get rolling. First, congratulations to both of you on the book. Again, the first edition of Rewired was a best-seller and offered leaders a really thoughtful framework for how companies could empower themselves with tech and AI. Why a second edition? Talk to us about what prompted this revision of Rewired and Rob, let's start with you.

[00:08:27]Great.

[00:08:28]>> [clears throat] >> Um I think at some level, you know, we like to say that you know, the world didn't give us a choice on whether to write a second version. I think since um most of the world saw ChatGPT for the first time three and a half years ago, you know, technology has changed as AI moved from machine learning into generative AI, agentic AI, this new capability to actually automate uh end-to-end workflows. And probably the most fundamental disruption is, you know, the disruption of gen AI to software development, right? Um code writing code writing code. So so much has changed. I mean, so really wanted to contend with um you know, take a look back at the framework we had established three years ago and sort of say, you know, does this still work?

[00:09:09]Does this recipe uh for established companies to sort of um organize, align, build, adopt, and scale AI, does it work?

[00:09:19]Um and so we kept coming back to this this quote actually um you know, which is by the Greek poet Archilochus, you know, we don't let rise to the level of our expectations, we fall to the level of our training. And that feels pretty true about this moment in AI and kind of the core thesis of Rewired, which is that, you know, any given company in any given industry will align on their what of AI, where is their value, you know, where are their domains to be transformed, big business cases to be had. But in our view, those companies will perform to the level of their capabilities in identifying, organizing, building, adopting, and scaling.

[00:09:56]Um and so what are those capabilities?

[00:09:58]That's kind of what we wrote down in Rewired.

[00:10:00]And we wanted to check this time around as generative AI and agnetic AI has started to become such an important capability, do the companies with these capabilities continue to do well? And that was probably the core learning for us in writing it was the companies that have built these capabilities in sort of AI 1.0 have succeeded far, you know, far more than any of the other companies who hadn't as we've gotten into kind of AI 2.0.

[00:10:26]Freeport-McMoRan is a great example of that.

[00:10:29]There was a great story about Freeport, you know, in the first book based on building a digital twin of the entire copper concentrator, creating all those efficiencies end-to-end across that process of taking copper out of the rock. And they, you know, were incredibly successful in driving value.

[00:10:48]They then turned their attention to generative AI, approached another area of the business which is leaching, the final process, chemical process to get the ore out of the rock and created a whole bunch of additional value. And that was just a good example to us of, you know, they had these capabilities they built once upon a time and they have continued to serve them incredibly well even as as AI has shifted towards gen AI and agnetic.

[00:11:11]Amazing. Yeah, agnetic is incredible.

[00:11:14]It's a new world and it's changing every day and obviously leaders need to be developing new kinds of flexibility and as you put it in Rewired a second muscle to be able to adapt continuously to these changes. It's always think of, you know, when when they put you on that destabilizing BOSU and Pilates and expect you to just carry on exercising. So on that notion of transformation and continuously adapting to what otherwise could be a destabilizing change, Kate, talk to us about the scale of what needs to happen and what's at stake here. If we accept that AI-enabled transformation is a must, what's the scale of the change and >> [snorts] >> is it worth it? Change is hard.

[00:12:01]Yeah, I think your analogy of the destabilizer is is actually a good one because it is hard, right? It is hard and it does take a big lift and I and I think, you know, you can't shy shy away from that. It's It's not a free lunch and it's not an easy thing to be able to do. But one of the things I was excited about with the second edition is just getting a lot grittier about well, what is that value capture? And exactly to your point, Miss Miller, is is it worth getting out of bed for? Right? Is it actually worth it? Am I going to see the the value? Because so much of this comes down to, you know, I've got AI everywhere AI everywhere in my organization except for the bottom line.

[00:12:35]So what we wanted to do was get granular about well, where do we see that impact?

[00:12:40]And I want to share just just sort of three three numbers with you around this. And this is looking specifically at a cohort within the research set of 20 companies that are really doing this well. They've really applied the Rewired framework soup to nuts, every part of the six, you know, boxes and so on. And we see three things here. Number one is among those 20, they have on average an EBITDA uplift of 20%. So to your point about whether or not this is worth it, I think a 20% EBITDA uplift is worth it and that is just the average, right? We see higher, we see slightly lower.

[00:13:13]But secondly, it's not a jam tomorrow thing. I'm not having to wait for years and years and years before this ever washes its face and pays back. Actually, on average it's one to two years to become cash accretive. That's probably another one to two to to really kind of fully you know, start to to to make the money flow. But it But it isn't forever, right? You can actually do this, you know, relatively quickly. And what's really interesting within that is, you know, the for most of them, two-thirds of the of the co- cohort, they were able to do this with three or fewer domains.

[00:13:47]So again, they're not papering AI everywhere across their organization.

[00:13:50]They're being incredibly focused in where they point the the resource, the money, you know, etc. And then last number I'll give you, for every dollar that they are spending of investment, they're getting $3 back on average. And again, in the grand scheme of returns that you can get, that's not too shabby. So I think to your point, do we see the value is there? Yes, we absolutely do. But that's the cohort of the 20 really really applying this versus many many many trying different elements of it, not quite building that muscle and therefore falling foul or falling short of these kind of numbers.

[00:14:24]Thanks, Kate. Okay, so those numbers make the case for why why transformation matters and the value at stake is pretty compelling.

[00:14:33]Let's turn to what I imagine is the hard part, which is the how. We know that most transformations fail. What separates the companies that get there from the ones that fall short? Rob, accepting that the two of you have just co-authored an entire book designed to answer this question, give us at least the high level on the framework for answering it and on what's new now in the agnetic context versus what has stayed the same.

[00:15:01]Great.

[00:15:02]Yeah, I think we we we landed on this concept for the book which is, you know, what's timeless, what what what remains true to the framework as we wrote it down and all of our thoughts sort of a few years ago and what is really timely, what has changed or evolved or is increased emphasis because of how technology has evolved.

[00:15:20]But again, the backdrop here is kind of this core belief that, you know, the differentiating capability is is not the what, right? You might imagine that peers in a given industry will all have a similar view as to how to create value through AI. The differentiation is the how, right? The how engine, your ability to predictably consistently turn your attention to these and know that you will be able to build, adopt and scale them to value.

[00:15:47]And it's these set of capabilities aligning on a business-led roadmap that does move the needle on the business, focusing on a few domains versus peanut butter spreading use cases across the board.

[00:16:01]You know, a set of enabling capabilities around talent, operating model, the way we work, which is building technology but also the control functions and the general decision process of a company, everything about the tech stack, data which is of course the lifeblood of AI and how do we make that high quality and constantly usable.

[00:16:23]And then this focus on adoption and scaling.

[00:16:26]But what was really timely for us or changed or where was the emphasis shifted because of gen AI? A few areas.

[00:16:33]The first in strategy is, you know, end-to-end workflows.

[00:16:36]We'd always sort of had this concept that thinking about the broader change versus individual use cases had value.

[00:16:42]That's just become incredibly clear with agnetic AI and this ability to actually automate the majority of a tasks of tasks in an end-to-end workflow.

[00:16:51]Reimagining that end-to-end versus just sort of deciding on an MVP use case in a workflow is clearly the path to success.

[00:17:00]In our latest state of AI research, 3,000 companies, by far the highest correlated practice to realize value was this. We have thought end-to-end, reimagined our workflows end-to-end, not just dropped AI tools into existing workflows.

[00:17:14]>> [snorts] >> On the talent side, you know, in the last book we talked a lot about the the challenge of getting density of scarce technology talent and sort of keeping them happy and at top of their craft. This time around we know that like we have to think about the entire workforce because over time agnetic AI is coming for, you know, all white-collar workflows and and physical AI will will come for others.

[00:17:38]So we have to really think through that broad workforce transition, the skills the skills we want in our employees, what roles remain after agnetic kind of runs its course.

[00:17:51]And then I think in technology, you know, that this 20x software development productivity, I mean this incredible fundamental disruption of code writing code writing code with the likes of, you know, Cloud Code releasing in January, you know, it's it's it is really collapsing in our work with clients and in our own building for clients. It's really collapsing this model of sort of the two-pizza team, you know, eight people to two people, right? A product owner who knows the definition of what good looks like and a full-stack engineer who can, you know, work with Cloud Code, debug and work it into the architecture. So that's a huge change. But also it's never been it's never been more complex we would assert to solution technology than this moment with every vendor at every layer of the stack laying their claim to be the center of your AI gravity and many of those capabilities coming with a high level of ongoing OPEX in terms of cost. So being very thoughtful about this because it's it's easy to think about point solutions or agents in core platforms in every function of the business. But when you step back from that, that's a brittle architecture.

[00:19:00]It's maybe an inefficient architecture.

[00:19:03]Maybe a less secure architecture. And so thinking about that becomes really important. And then just lastly, adoption.

[00:19:10]You know, one thing we think about is if you think about taking a workflow and reinventing it, that means, you know, clean sheet of paper, reimagine the process, take all the roles down to tasks, automate many away, reconstruct them into new roles, train everybody. It's an incredible transformation and I think if most companies looked themselves in the mirror, they would say, "Well, actually we haven't fully transformed, reinvented an end-to-end workflow in a long time, if ever. We've been on a continuous improvement motion." And so we would say like this is the job to be done of the next several years again and again and again. So this sort of change adoption toolkit is is really critically important.

[00:19:49]Yeah, it's huge. Okay, so if I'm understanding and synthesizing correctly, the six capabilities here constitute the foundation, but the bar for what good looks like and what's required inside each one has significantly shifted. Let's look a little more closely at that picture.

[00:20:08]Kate, give us a little color on companies that have rewired successfully and what they're doing.

[00:20:15]Yeah, I mean in many ways and I get asked this question a lot about what does a rewired company actually look like? You know, how can I spot one in the wild as it were?

[00:20:23]And for me one of the biggest differentiators when you really boil these six capabilities together is they're able to operate at a different metabolic rate, right? So their their time, the latency almost from insight to decision, from decision to action starts to look really different because we were very very clear when we were writing both the first and the second edition of this that this isn't, you know, digital transformation for transformation's sake or AI transformation for its own sake.

[00:20:50]It's about outcompeting. And to outcompete, you've got to deploy these capabilities and actually move faster than the than the peer next to you. And you know, one of the examples we we talk about in the in the book and tell the story of for a second time is is DBS. And the reason I say for a second time is, you know, it's been wonderful and Rob mentioned Freeport earlier as well to see the kind of the second chapter for some of these companies that we we you know, profiled in the in the first book. And you know, it's it's only through three, four years of really hard foundational investments on the part of DBS that when gen AI comes around and agentic comes around that they're able to move really really fast. And that's why you start to see and they're, you know, on record publicly for this, you know, around a billion of Singaporean dollars of tangible, verifiable benefits towards you from from AI. And it's only because the foundational capabilities were there that as the technologies changed, they were able to move faster. So it's both a speed of operation that feels genuinely different, but also the compounding value of these capabilities over time in that you're running faster and faster and faster and faster. That's why we describe it as a as a muscle rather than as a transformation that at some point we will be done.

[00:22:09]Okay, so there's a lot going there. The capability bar has risen. The pace of change as you just described is compounding and the window to act is narrowing. If speed is a factor. So Kate, returning to the question of scale, this magnitude of change can obviously feel daunting and so not surprisingly, one of the most asked questions we got from our audience ahead of this session was how to get started. So for leaders watching who want to be on the right side of that line, what's the most practical approach in your view to next steps?

[00:22:47]Yeah, I love it. And one of the things that we actually did in this second edition is we did what I'll affectionately call the TLDR version of the book, right? Because it's a big book. It's got a heck of a third factor to it. And we wanted to distill down, look, when you step back from all of this, what are those signature moves, those things that really make a difference here? And therefore, how do you get started against those? And that I I use these in my day-to-day of almost keeping myself in check and balance to say, look, am I really am I really doing these things? Not I'm trying to separate out the we can all agree with all 12, but when you really sort of, you know, turn the turn the screw up and say, well, hang on a minute, am I am I actually really you know, pointing my resources on the on the the points of economic leverage? Am I really? It allows you to kind of pause and and take stock of that. So I regard these as a checklist, a set of guardrails and so on for how to think about, you know, what wherever you are on your journey, if you're doing these things and really can hold the mirror up against yourself, then you you're you're on the right right path because I think very few of us now are starting with a blank sheet of paper and saying, hey, day one, how do I start? It's much more of I'm in the mix. I'm in the arena.

[00:24:02]Now, how do I make sure that I'm actually spending the money in the right way, that I'm pointing, you know, the the very precious resource to the right things and that I'm thinking about that in the, you know, in in a way that's going to be a path to value. And I'll pull out maybe a couple of these if I if I may, Lucilla, that have maybe personal personal favorites. The first is this this domain change, right? Domains, not individual use cases or end-to-end workflows. I'm not precious about the the terminology. Each company has a different way of describing that. But the reason this matters is you are pointing your resource at the points of greatest economic leverage for your industry, right? So you're actually in essence solving business problems that actually matter and will move the needle for you. And it is striking to me how many companies I I spend time with who, you know, sort of think they did that at the start and they all agreed and stacked hands, but when it really comes down to it, they sort of peanut buttered the resource amongst everybody because everybody had their own version of it.

[00:24:58]So so domains, not use cases and really pointed at three or fewer that that that move the needle for your company. So, you know, if I'm in retail, that's probably forecasting and planning, right? If I'm in insurance, it's probably claims processing.

[00:25:13]You know, you if I'm in heavy manufacturing, it might be yield or throughput. You know what they are for your for your industry. You got to point the the the time and the resource at them.

[00:25:23]Another one that I personally love is is the piece on on talent density, right? So if you look at number five here, every AI transformation is at its heart a people transformation. We wrote that in book one. That is more true today than it has ever been.

[00:25:39]And the reason, of course, is the level of change that is happening around us, right? The talent density that you have on your teams matters, particularly the technical talent density.

[00:25:49]And so, you know, the level of real kind of open heart surgery that you're doing to make sure that your your people proposition is right matters. And at the same time as we move into, you know, agents taking more of the kind of coordination type roles, more of the routine execution and decision-making, then then actually the human role shifts up that value stack as well. And starting to think about what having, you know, both carbon and silicon employees together in one organization, how that works, the level of people change around that becomes really consequential. So if you're not thinking about your AI transformation as a set of people changes, then you're probably off track somewhere in what you're you're doing.

[00:26:31]So those are those are two two favorites of mine. I won't go through all 12. I don't know, Rob, if you have a a particular favorite as well.

[00:26:38]Um a couple I like in my day-to-day. First of all, I think that the, you know, speed is a defining organizational advantage is interesting.

[00:26:45]You mentioned DBS and I think that the $1 billion of realized value is impressive. The other North Star metric I love about DBS is, you know, when they started out with AI, it took them 18 months to get their first model to production. Now they put a model into production every two months, right? That speed, that that time, that is that's the capability. That's that's the differentiated capability that allows them to just keep going doing more and going faster. So I think that's interesting.

[00:27:13]And then I think probably, you know, you know I'm a fan of agentic engineering. You know, I think this is the fundamental disruption that we're only beginning to see the potential of.

[00:27:24]A great example in the book actually is LATAM Airlines which, you know, I'm not sure all of us would would sort of think about airlines as, you know, the bleeding edge of technology, but LATAM Airlines has is probably a year ahead of most companies in terms of fully adopting and embedding agentic engineering, not just for coding, but for the entire software development life cycle. And they're going so fast as a result. And also related to this point, it's changed the talent mix, right? We really need, you know, great engineers, but we need those who have retooled themselves with agentic software.

[00:27:59]And also, it just just really emphasized again the importance of domain expertise, you know, in addition to technology talent. With these capabilities, if we can define done to incredible precision, cloud code can write that for us in a couple of hours or a day. So this paradigm is shifting and it's really interesting to see where this will go.

[00:28:21]It's amazing. Thank you both. Okay, these points converge on a on a single unavoidable question, which is who owns this?

[00:28:32]A transformation this consequential touching every capability, every domain, every layer of the business, even the most techno-optimistic among us have to ex ex concede that it's not going to run itself. So Kate, where does accountability sit here?

[00:28:51]Yeah.

[00:28:52]It's both top-down and it's distributed, right? So in all the hundreds of transformations that we've now now studied in our in our misspent youth, there isn't a single one that is successful that does not have this as a number one, number two priority by the CEO, right? That's a given. I've not found it.

[00:29:10]At the same time, the ownership of how to actually do this has to be distributed across the the full leadership team. So I describe it as it's it's a corporate team sport, right?

[00:29:21]It's actually got to be everybody's job.

[00:29:23]of my telltale signs, you know, walking into a you know, management team or whatever is when someone asks that question in the room about technology, let's say, and you watch everybody in the room sort of turn and face the one person that's got it in their job title, right? That's when you know that this is not going to work because you need your CHRO to wake up in the in the morning and say, actually, what is an agentic organization going to look like? Your CFO is, you know, rewiring the funding mechanisms that are going to allow you to actually reinvest and and you know, invest and reinvest in this over time.

[00:29:55]Your your business owners, you know, the domain owners, the real heart of the transformation, you know, need to actually, you know, own this, right? So, it is both top-down and it is distributed as well. I think long are the the days now where you can delegate this to the technology function and and and hope for a for a for a good outcome. It's just not enough anymore.

[00:30:16]Okay. Thanks, both. I am mindful of time and we've been getting a slew of questions. I'd like to turn to Q&A, at least try to squeeze in one or two.

[00:30:26]Let's start with missteps to be aware of since you covered those in the book, but we haven't spoken about them yet here.

[00:30:31]Rob, what are the gaps most businesses miss in their AI maturity journey?

[00:30:39]Sure. I mean, I think there's gaps at different stages of sort of maturity and transformation.

[00:30:45]I mean, one of the first Kate kind of touched on, which is just this notion that you know, these AI transformations, we we call it the ultimate, you know, corporate top team sport, right? It is it these need to be entirely business-led because we need to think about the technology, but the the more important thought in our minds is if we take an end-to-end workflow, you know, that's a core operational aspect of the business.

[00:31:06]We're going to entirely reinvent that, right? We need to be incredibly thoughtful about how we do that.

[00:31:12]Um so, many needs to be business-backed and needs to be very aligned. And one of the first missteps is is kind of I think this mindset that the job of an ELT is to listen to proposals around AI, resource them, and then turn to sort of the CDIO capability to sort of get it done. And we would say, "No, it's it's if you if the if your mindset is I I run a call center or I run supply chain planning and I am going to totally reinvent how this is done, I think, you know, you you're it it's much more than a technology thought, right? It needs to be entirely business-led. So, that's one thing I think folks fall back on an old paradigm of working with IT and that just doesn't work in this day and age.

[00:31:52]Um that's one I think obstacle.

[00:31:56]I think another obstacle we see is just is just the lack sort of a lack of preparation for adoption.

[00:32:02]In at least two two way two three ways. One is, you know, very often we we resource things to MVP. We have an idea, let's go invest some money and see if we can prove it out. I think we're less interested in the number of successful MVPs. We're more interested in how many of these end-to-end workflows have fully scaled across the entire footprint of your of of a company where it's relevant. Um and so, from adoption, I have you resourced that?

[00:32:26]Have we thought about the fact that when the MVP works, we then need to get this to production and rinse and repeat it and scale it across our business.

[00:32:33]Have we thought about the fact that as we rinse and repeat that, we need an efficient way of, you know, kidding this technology so that when I bring it to the next country or the next product line, I don't need to fully reinvent the wheel. I'm just sort of tailoring from there.

[00:32:47]Have we thought about the adoption that is not technical? We like the example of a big automotive company in the book that fully reinvented their supply chain.

[00:32:55]But the harder as hard as that was, they then had to work with hundreds of suppliers to sort of get them to work in the way that, you know, the digital twin of the supply chain said it should.

[00:33:04]That's incredible change. These aspects of adoption are things that they're often afterthoughts and therefore they get become stumbling blocks and slow our progress.

[00:33:13]Okay, that's super helpful. I'm going to squeeze in one more. Kate, very quickly, what does it take to build AI conviction both yourself as a leader and across your organization before that window to lead actually closes?

[00:33:28]Yeah. Let me say two very quick things maybe on this one. I love the question.

[00:33:32]The first is the best way to build conviction is is focus on the value, right? Follow where the money actually is and solve real business problems. You want to build conviction, you know, that's the that's the heart. That's the place where you you want to be able to do it.

[00:33:46]And then the second thing I will say is also cut yourself a little bit of slack on this. This stuff is hard and one of the maybe, you know, benefits of of Agentech and so on is that the cost of iteration has come down, right? If you like the cost of a wrong turn has got or it's easier to make a wrong turn and and pivot. And, you know, I think it's becoming less and less about generating the or coming up with the perfect answer, much more about, you know, stress testing it, owning it, building the conviction around it, building the, you know, the right to to deliver that that change. And I think there's beauty in the messiness of the process sometimes and what ends up on the cutting room floor and why maybe where the real value value sits.

[00:34:29]So, I would cut some slack as well.

[00:34:31]Inspiring. Okay, that brings us to time.

[00:34:33]Fantastic discussion, Rob and Kate.

[00:34:35]Thanks for joining us today and again, big congratulations on the launch of Rewired.

[00:34:41]Thank you so much.

[00:34:42]>> much.

[00:34:43]And as always, many thanks to all of you watching. Folks in the audience, if you want to see where you stand vis-a-vis your own rewiring efforts, please use that QR code you can see on screen to take the Rewired assessment.

[00:34:57]Super cool. You can also visit mckinsey.com to find out more about this second edition of Rewired. It's really a must-read in this age of constant change. And as always, you'll be able to find a replay of this conversation and all our previous McKinsey lives on our website at mckinsey.com/live.

[00:35:17]Finally, be sure to join us for our next live event a few weeks from now on May 12th. We are slated to talk about another massive headline issue, geopolitics, and specifically the way that geopolitical dynamics are affecting global trade. You can scan the QR code on screen to register. See you there and have a great rest of your day. Be well.

[00:36:03]>> [music]