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McKinsey & Company | Fortune

McKinsey & Company Company Profile, Stock Price, News, Rankings | Fortune

Catherina Gioino

McKinsey: AI will create more jobs than it kills — after destroying 11 million

/2026/10/03/ai-job-displacement-creation-new-careers-11-million-americans-workforce-mobility/

David Pralong

McKinsey senior partner: Why AI’s easiest wins are misleading CEOs

/2026/10/01/mckinsey-senior-partner-why-ais-easiest-wins-are-misleading-ceos/

Sheryl Estrada

McKinsey: Cheaper AI models, bigger AI bills

/2026/09/23/mckinsey-cheaper-ai-models-bigger-ai-bills-cfo/

Daniel Aminetzah

Paul Jenkins

Jorge Grieve

McKinsey partners: you need to protect successful new ventures from the core business

/2026/09/21/mckinsey-partners-you-need-to-protect-successful-new-ventures-from-the-core-business/

Matthew Bidwell

Why Apple’s next CEO shows you don’t need to ‘move out to move up’

/2026/08/28/why-apples-next-ceo-shows-you-dont-need-to-move-out-to-move-up/

https://fortune.com/2026/10/03/ai-job-displacement-creation-new-careers-11-million-americans-workforce-mobility/?itm_source=parsely-api

https://fortune.com/2026/10/01/mckinsey-senior-partner-why-ais-easiest-wins-are-misleading-ceos/?itm_source=parsely-api

Pooja Kumar

Eric Kutcher

McKinsey senior partners: America’s growth strategy demands a health reset

https://fortune.com/2026/08/15/mckinsey-senior-partners-americas-growth-strategy-demands-a-health-reset/?itm_source=parsely-api

Acha Leke

Chaitali Mukherjee

McKinsey studied 200 family business successions. The biggest problem wasn't the heir — it was the outgoing CEO

https://fortune.com/2026/05/22/family-business-ceo-succession-mckinsey-research/?itm_source=parsely-api

Nick Lichtenberg

Warren Buffett’s exit is ‘leadership at its most selfless,’ McKinsey CEO whisperer says. It’s a powerful lesson in Fortune 500 succession

https://fortune.com/2025/11/21/warren-buffett-berkshire-hathaway-legacy-joe-montana-steve-young-succession/?itm_source=parsely-api

Eva Roytburg

'The Bermuda Triangle of Talent': 27-year-old Oxford grad turned down McKinsey and Morgan Stanley to find out why Gen Z’s smartest keep selling out

https://fortune.com/article/genz-consulting-banking-oxford-harvard-golden-handcuffs-taking-risks-simon-van-teutem/?itm_source=parsely-api

Jake Angelo

McKinsey challenges graduates to master AI tools as it shifts hiring hunt toward liberal arts majors

https://fortune.com/2026/01/14/how-to-get-hired-at-mckinsey-ai-tools-liberal-arts-creativity/?itm_source=parsely-api

The great (small business) wealth transfer: McKinsey sees $5 trillion of baby boomer companies coming up for sale over the next decade

https://fortune.com/2026/02/26/great-small-business-wealth-transfer-mckinsey-5-trillion-baby-boomer-businesses-sale/?itm_source=parsely-api

Emma Burleigh

The CEO of this $2.4 billion wellness app ditched Harvard Business School and a McKinsey career to bring calm and exercise to corporate America

https://fortune.com/2025/07/23/wellhub-ceo-cesar-carvalho-billion-dollar-wellness-platform-lifestyle-mckinsey-harvard-business-school-corporate-america/?itm_source=parsely-api

The CEO of a $2 billion healthcare firm only felt rich after he paid off $100K in student loans—but that joy ‘disappeared’ in less than 3 days

https://fortune.com/2026/01/25/ceo-of-2-billion-healthcare-company-virta-health-sami-inkinen-felt-rich-100k-student-debt-joy-disappeared/?itm_source=parsely-api

Global growth has remained surprisingly robust despite a succession of shocks, including geopolitical tensions, trade fragmentation, policy uncertainty, and the most severe energy crisis since the 1970s. Asia continues to anchor global expansion, while investment in AI and strategic industries is reconfiguring markets, supply chains, and competitiveness. Yet significant downside risks persist, including escalating geopolitical rivalries, mounting fiscal pressures, and structural economic shifts. Setting the scene for the day’s conversations, this opening panel will offer a clear-eyed perspective on the economy in 2027 and beyond.

Ziad Haider, Partner, Global Director of Geopolitics , McKinsey & Company 
Louis-Vincent Gave, Founding Partner and Chief Executive Officer, Gavekal 
Andrew Staples, Co-chair, Fortune Leaders Forum; Editorial Director, Asia, Fortune 

The Global Economy: Signals for the Year Ahead

Conferences

Performance reviews have a reputation, and not a good one. For many employees and managers, they feel like a bureaucratic exercise that creates more anxiety than insight. What if the problem isn’t feedback itself, but the way organizations structure it? This conversation takes a hard look at how companies are rethinking a dreaded once-a-year obligation to focus on real conversations, clearer expectations, and continuous development. Learn what great performance management actually looks like— and what's standing in the way of getting there. 	 

David Hassell, Chief Executive Officer, 15Five
Brooke Weddle, Senior Partner, McKinsey & Company  
Moderator:  Adam Banicki, Head of Video, Fortune

Is the Performance Review Broken?

OK, I wanna start with a, a show of fans. Uh, who here thinks performance reviews are transparent, fair, and inspire better performances? Uh oh, not a lot of hands. Well, according to a recent Gallup poll, only 1 in 5 employees believe they are. So let's dissect where the disconnect is. Brooke, David, thank you for being here. You both work at companies trying to improve, improve performance. What is the most common thing you see not working with performance reviews today? Well, I, I'm happy to start. I think as the room, uh, clearly senses there are many things that are not working, and I think the key point today is that most performance systems were designed for a world that is moving more slowly than the world in which we are working in. And um I can think of a couple of failure modes. uh I would say annual performance reviews, uh, definitely a failure mode, not delivering feedback uh in a continuous way looking for measures of productivity proxies that don't actually get at outcomes and so when you shift to what we think the future is there are a couple of, um, big ideas. One is first of all. Of understanding how the outcomes are changing and the skills and capabilities required of today's workforce look differently, so McKinsey found that AI literacy has jumped in reported reported importance by employers by 70%. Um, that is just one capability. Certainly others like learning agility, uh, are also spiking. The other key. Uh, part of the future, if you will, is the performance review process itself, um, and how we are using AI and act to actually transform some of those processes, uh, and leading organizations across sectors are, um, I would say beyond experimentation at this point and actually hardwiring those processes different, uh, differently through tech, uh, technology enablement. David, what about you? You know, when I look at the, the statistics around this, um, you know, the vast majority of managers and employees, I think it was a, a, a Deloitte study that showed that they just don't trust the process. Um, I think it was Gallup that said only 2% of CHROs actually believe that their performance management practice is actually effective. I mean, that's stunning. So maybe there's 2 out of 100 in this room who think, think it's working. Um, and I think building on what Brook said, uh, you know, the fact that these, this process was designed for a different era is, is part of the issue. Um, annual reviews made sense when the world was pretty static, and we're at an accelerating pace of, of technology and change, and, uh, the, the practice just hasn't kept up. You mentioned changing annual reviews. What should change that process? Are we talking like routine check-ins with managers? Like, where do you see the best option? Yeah, look, 10 years ago there was a big movement toward, hey, we should throw out the annual review and make everything continuous, and 15.5 was at the forefront of that. We build technology for, uh, weekly check-ins and one on one. Ones and things like that and we found and our customers found that we kind of threw the baby out with the bathwater. The continuous piece is absolutely essential, but you do need a period of time where you reflect and you look at you assess performance when you can take a step back. So I think it really is the combination of those two things, but the annual cadence is part of the problem, um, you know, the, uh, the reason it's typically is done annually is because it is so onerous to, to do one of these practices. And um what needs to happen is it needs to be simplified and it can be more frequent. We have some customers doing it as many as 4 times a year. I don't recommend that for everybody, um, but at least twice a year with a lighter weight process, uh, combined with the kind of the weekly feedback is very important. And I know you're both, uh, using AI to help this process. Brooke, how is McKenzie implementing this and how is it being received? Yeah, well, I, I can talk about what McKinsey is doing, but I, I can also say that it is representative of what other organizations are doing as well. So one of the reasons why the annual performance review feels so heavy is because there is a lot of transactional work that goes into a performance review. Everyone in this room knows that. And so what McKinsey is specifically doing is um using AI to intake data from 360 evaluations um one of the things that McKinsey places a lot of importance on this won't surprise anyone is of course it's human capital. Without our human capital and expertise. We have no business model. So in a performance evaluation you were talking about, you know, upwards of 20 interviews, right, that you are intaking data, uh, to evaluate a candidate. It's even greater when you think about kind of milestones in someone's career like partner election. Uh, so you, you intake, uh, that, that data AI actually is used to generate, um, the, the content for the review, an initial version of course the human is, is most clearly in the loop here and that content generation is actually synthesized in the context of our leadership development model and increasingly we are using. Using um our tools to drive more predictive, uh, analysis as well so that I as an evaluator, um, will have, um, some steers right on how to interpret, uh, the content that, uh, is being teed up for me and of course, um, that helps with things like biases and my own blind spots in terms of my pattern recognition. So I'm, I'm curious, like for many employees when they're going through the, uh, performance review process, they feel like managers become robotic as they're, as we're implementing AI into this process. How is that communicated to employees in a way that makes them feel comfortable and that this is still a human conversation? Uh, you know, one of the things that we're really focused on is, is, you know, I, there's a, there is a big conversation about there, but what is the role of AI in all of our work, right? And, uh, we're, we're firm believers that AI should be, uh, used to elevate humans and to elevate our judgment and to, to, uh, building on what Brooke said, keeping humans in the loop. And so, you know, one of the things that we are, uh, focused on at 15.5 is figuring out how to, uh, use AI to streamline the process to, to, to have managers have what they need to be able to make better decisions, um, and, uh, and so, you know, right now actually, uh, we're in June we'll be releasing a product. That now integrates into about 18 different systems of work, uh, so that managers can be in real time given insights into, hey, this is an opportunity for feedback potentially over here or hey, here's, here's an area that you may wanna look at. There may be some blind spots, but really keeping the, the full onus on the manager as the, as, as the person with the judgment. Yeah. So I think we've talked about trust and, and employees want clarity and accountability. Where do you both stand on ratings at reviews time? I've always appreciated that that grade at the end of the year that sort of told me where I stand with my manager. But, uh, is, is that a good thing? Is it a bad thing? I'm curious. Yeah, well, we, we've spent some time discussing this, uh, and, um, the McKinsey data on this is, is pretty clear that, um, ratings are actually. not the centerpiece of an evaluation for an employee, uh, and in fact there is no negative stigma around, uh, ratings for most of the, um, global, uh, employees that we, uh, surveyed a couple of years ago. The negative stigma comes from perceptions of unfairness, from opaqueness, uh, in, in the process. I don't understand what is the difference between a strongly performing versus a distinctive, uh, performance, and, and. So ratings actually give people the opportunity to understand where they stand uh and I think that's quite important and and the data would back that up and in fact in my personal experience uh working with many organizations that moved away from ratings they in fact continue to use shadow ratings in their internal discussions which I think uh defeats the purpose entirely um we need to share where people. Are, uh, in terms of their evaluation but then couple that with strong focus on development I would say actually that the competitive advantage today of most leading organizations is derived by how quickly they can help develop human potential, uh, and that actually going back to your question on communication should be I think centerpiece of any kind of communication on performance based AI um that you know companies are using. Just wanna build on that, um, you know, uh. We went through a movement also probably about 10 years ago where we saw a lot of organizations say hey we should get rid of the ratings and to, you know, to what Brick shared about the, the shadow ratings we saw that's true. I mean companies need to assess their people and make talent decisions. So whether that's a rating or some other methodology as an organization as an HR leader, you have to do that. And so to not share that with the person I think does create a lot of, uh, um, challenge in, in the employee-employer relationship. Because there is this lack of transparency we found, um, one of the challenges we've seen with ratings is just uh it is a communication problem because sometimes people say, you know, on a 5 point scale a 3 is actually pretty good that's where you should be and then there's, you know, kind of deviations aside from that we all grow up thinking like we should get A's and you know we should all be a 5 and so I think the, the challenge is in the perception of the conversation, not necessarily the rating itself. I, I once had a manager that said you should get maybe 1 or 25 ratings and you're nearer and, and a 3 it means you're performing really great and you're, you're showing up every day so I understand that. Uh, I'm curious if companies remove ratings, how should they handle compensation, performance, promotion, those sort of things? Well, even if a company chooses to remove ratings, I still think the centerpiece needs to be around this mismanagement. dialogue and um I've seen increased importance on this notion of dialogue or interlocution right um whether you're interacting with a robot robot uh an agent or a human this is a new capability that's emerging. And I don't think managers are um equipped in all cases to be able to engage in a thoughtful dialogue. So certainly things like pushing pushing transactional activities first and foremost to things like AI will help. Second, um, there is typically some managerial upskilling that needs to happen and I, what I've seen work really well is positioning that in the. The context of broader cultural transformation, so you don't get managers to emphasize and role model continuous development if that's not a cultural priority led and role modeled by the CEO and then finally there needs to be accountability. So if we're gonna ask managers to do continuous feedback better, we better hold them accountable in their own performance reviews and talk about what great looks like. I, I'm curious, like when we think about asking managers to do more, uh, are we asking too much of them if, if it's going to be a continuous feedback loop? Is that more work being put on, on management to make sure that they're delivering these results? David, what do you see, you know, I, I read, I read some statistics that showed that, uh, in, in many organizations managers spend upwards of 210 hours a year doing performance management like the formal practice, and it's mostly backward looking, and they spend only about 13% coaching. Uh, in, in real time, which I think those numbers really need to get flipped, and so I do think that. Uh, we put too much on managers when we have these big onerous practices that don't actually deliver the value which is more forward looking than backward looking. So how do we create systems and, and, and, and a culture where managers have the appropriate training because every manager that comes into your organization has, you know, baggage of training from whatever other organizations they have. So you have to get them aligned around your ethos, uh, and then, and then how do we, um, you know. Really empower them with, with the, the data and the intelligence and what they need to make, uh, to, to have those coaching conversations ongoing in the flow of work and not as some additional thing that they need to do. Yeah, at a place like McKinsey, tens of thousands of employees, how important is it to create a model that's scalable to all of those employees and how does AI help with that? Yeah, I mean, it, it, you know, we, we do do continuous feedback. We have, um. You know biannual, uh, reviews as well that serve as kind of anchor points, uh, but, but like I shared we we are using AI to again, you know, kind of shift some of that transactional work and holding people accountable, um, to, to really spending time, uh, with their people and their teams, uh, to try to. Drive development. I also think there's a mindset shift here which is when you think about the manager irrespective of the company, it, I, I think the mindset shift is viewing development as performance, right? So we're development is not separate from managing someone's performance. If you're managing their development, you are. Actually managing their performance and I think that kind of shift is important if you're really going to embrace the notion of adaptability, uh, continuous learning, learning quotient. There's all sorts of buzzwords out there to get at this notion, um, but that, that would be a core kind of cultural or mindset underpinning that I think is quite important. What separates a useful performance conversation from a bureaucratic one when you're going through the motions and doing this continuously? You know, I think the first thing is, you know, and, and, and this actually I think really highlights the difference between the, the performance review process which is, you know, this big conversation, it happens once a year, typically very, you know, very much subject to recency bias and, and, and whatnot. Uh, versus, uh, a conversation that's happening in the moment about something that's happening in real time. Most of that growth, I mean, if you're gonna have a conversation once a year about something that happened 3 or 4 months ago, um, you're not really going to be able to help that person develop around that one thing. But if you're noticing it and calling it out and have a conversation in the moment, you have this opportunity to, to, to shift the behavior. Uh, what it takes, however, is the managers, right, having a good practice of behavior around that, and you know, we tried to solve that problem with our. 15.5 check-ins and one on ones 10 years ago, but, uh, it was really hard for the, the, the, the software was great, but the, the change of management was the hard piece and so, um, so now I think AI now makes it even uh more possible than ever to provide the right kind of insights and nudges and things to managers to be for them to be able to have that conversation. That's the problem we're solving now, um, and I think what again it comes back to making sure they're, uh, addressing something that's happening in real time, yeah. So I mean you could go in so many different directions with that question. I think we've all had a review where you're kind of like wow, that's, uh, that was not what I had anticipated. I think, um, where when I think of a bad review I think of kind of something that you know is awash in platitudes and niceties where you walk away not actually knowing what the thing is that you need to be working on and um. Interestingly enough, we're we're also seeing AI be used to try to help managers and leaders be more specific, be more intentional about the feedback that they are giving, um, so AI giving feedback to humans on how to deliver feedback that's essentially what I'm saying, and that is happening. Um, I think the other thing where, um, you, you see kind of the failure mode is having the feedback feel disconnected from, from the outcome or from the, the flow of work, um, and going back to our research again, one of the key things that we found is that over 70% of employees wanna have clear goals as a part of their continuous dialogue and process so that would be the other kind of, you know, failure I would say that. Often happens and again connecting that back to specific outcomes is going to help drive specificity. Yeah, I just want to build on that. I think the, I think having, like you said, the clear goals and clear expectations are the foundation and, and what needs to be in place in order to have effective feedback, right, because otherwise, um, if someone doesn't know what's expected of them and where they stand, it's challenging to deliver, uh, something that's going to be useful for them to understand how can they grow and, and, and meet those expectations better over time. OK, I wanna end with um one last question, uh. What is one thing leaders should stop doing and start doing as they think about their review process this year, David? Uh, let's see, stop and start. I would say, um, uh, well, let's start with start. I think that, uh, companies should really think about, um, lightening up the processes as much as possible, so the annual process, uh, making it as lightweight as possible, and then really focusing on that continuous, uh, feedback, uh, throughout the year. There should never be a surprise at review time, right? So, uh, really at that point, you should, you should be documenting the past, but organ organizing the entire process around the future. How can we grow? How can we be better in the future? And, uh, you know, yes, you have to do assessments, but, but, but, but limiting the, the time investment on that. I would say uh stop creating kind of endless steps in the process fundamentally take a clean sheet to your performance evaluation process starting back back looking back from the outcomes that you want to achieve, um, you know, taking a page from our our work on reimagining work flows that's what I. I would, I would stop creating the process. I would start reimagining and working back from those specific outcomes you want to achieve. And I would bet that some of the outcomes you want to achieve are actually new skills and capabilities that you want your workforce to embrace and then designing systems to help actually those employees live into those capabilities and skills. Brooke, David, thank you so much. Thank you. Thank you.

Eric Kutcher, Senior Partner and North America Chair, McKinsey & Company, Zig Serafin, Chief Executive Officer, Qualtrics
In conversation with: Diane Brady, Fortune

Fortune Global Forum 2024: Powering the AI Revolution

Photograph by Michael O’Shea for Fortune

Eric Kutcher, Senior Partner and North America Chair, McKinsey & Company
Zig Serafin, Chief Executive Officer, Qualtrics
In conversation with: Diane Brady, Fortune

Thank you very much, everybody. So we're gonna be talking about um infrastructure and so much more. Nice to see you both. Uh By the way, um Zig is a former musician, so accordion and Saxe just to kind of level set here we are here, we are, right. So, you know, I wanna start actually, you get into rooms, we don't get to go talking to CEO. So before we get into the, the deeper dives on what it takes to get, you know, to the next stage of the A I of revolution. Um What are CEO S talking about? Where are we right now? And, and, you know, give us some sense of just where we are in this evolution, us and globally, I'm gonna start with you 60 seconds or less. I think CEO S woke up 18 months ago to, this is real, they woke up to 18 months ago. This is real. At the same time, I think they're extraordinarily frustrated by the lack of return on investment. They've gotten so far. But I think they're starting to see these autonomous agents as a mechanism to begin to rewire and rethink and reimagine their business. So the business model transformation as opposed to some of the productivity gains and such. Yeah, I think it'll be both. I mean, I think, I think what you're gonna see right now is the opportunity to completely reimagine the way customer service is done, right? Or, I mean, we can go through examples but you only give me 60 seconds. So, so, so I, I think they're starting to say, wait a minute, there is entire bits of what I do today that I can do completely differently with a much better experience. I'm gonna pull on that thread. So, you know, I get the, the privilege to be able to work with CEO S CMO S coo sc hr OS all around the world, different industries, many of which many of you here representing and uh there's a new framework that's underway for um getting out of the boxes that we've been living in. If you think about the app based boxes that we live in today, we're the ones that are providing the input. We're the ones that are actually the producers of the output that takes place, you know. And so they've been neatly built as CRM applications, website, applications in the future will be one where that entire framework goes away. And there's a new framework where you will have an agent based world where the speed at which you actually end up leveraging an autonomous engine to get something completed will make things very different. And so this isn't just about adding windows dressing to the, to the, to the existing applications that we have today, right? It's not about adding an A I feature to the world that we live in today. It's really stepping back and saying over the next 234 years, how will someone engage with my airline? How do you understand that customer better? How do you actually create a faster way of enabling them in a more personal way as a result of that? That's a huge theme. Now, along with that is responsible A I is a major theme just like we have measures in most companies and organizations today where you have security controls and, and, and and a policy around how you run security. You will do the same thing for how A I uses the data, how you actually create trust. How do you actually engage with people in an honest way, get permission to be able to create increasingly closer personal connections at scale across the way that your business shows up working with the customers and employees that ultimately end up enabling your business. Can I pick up you, you talked about kind of this? I I said re imagine, you know, you talked a bit about the way people are gonna think through customer experience. You made a huge announcement earlier in the year that I that I happened to to get to see a little bit, which if you think about introducing a new product, I, I'm not promoting, I'm just, it really, it just really threw it really, it was really quite, quite remarkable because today, if you're going to, you, you would go do a whole bunch of customer research, right? Unless you're, you know, unless you're Steve jobs, you would go do a bunch of customer research. You figure out what now you literally can type into your A I engine. And because you have this incredible history of every interaction point, you know, what individual customers that very micro segments. And so that information was never existed before. Yeah. Yeah. So that, that capability essentially is called edge and it actually helps companies to be able to tap into knowledge about what's happening in their industry, cost industry uh and get instant answers to some of the biggest questions that might be on people's minds. Uh but also be able to get instant answers to the micro questions and decisions have to be made at a store level. Let's say that your KFC um brands, for example, and you're operating in a far region, we have KFC here in the audience um or if you're in the restaurant industry, uh what is it that your store manager needs to know about that took place yesterday across your competition and what customers care about in order to be able to inform the way that you end up showing up even the next day and of course at a bigger picture level is what trends do I need to pay attention to? And so being able to synthesize that type of data prior to the emergence of where A I is taking things would have been very expensive and very time consumptive, right? And now you're moving into a world where the speed of getting that type of decision making improves and accelerates. So I think what we want to achieve in this panel and I'm glad we're addressing that because we will take a question or two is, is both the potential of A I and the limitations. And of course, one of the potential limitations is that infrastructure problem or issue. So let's let's let's just sort of do a bit of a lightning round here because I'd I like to know how you'd prioritize addressing these challenges. Let's talk about the powering part, the infrastructure data centers. How big an issue is that it's a huge issue, right? If you actually look at the amount of you heard the number from us before you look at all the growth in the data centers around A I, you don't actually have two issues. One is you got to build the data centers or actually don't have enough people to build them. Second, you got to power the data centers, we don't have enough power. I actually think the power will come from frankly the big hyper scalers and others making massive investments. Um But that still takes time. It'll, it'll be surmountable town. How about you? How, how do you think about it? I think about it in terms of 222 major lenses. Number one is you have the hyper scalar, uh you know, A G I, the people that are building these massive models that will require an immense amount of compute power. They have to be close to the energy sources. They've got to find new forms of sources of energy. And then there is task specific A I where you actually start to build fine tune models. Uh ones that, you know, probably half of you in this audience will end up building and, and, and, and enriching and using a variety of techniques to be able to say it's purpose built, it's specific to this particular task. Uh and that will be highly efficient, it'll be focused on a data set that is specific to what you do in your business. Uh And it would be cost effective as well. And so there's idea of actually building sustainable scalable A I is really a combination of those two major lenses and we have the possibility of a trade war. I know we just had the China panel and obviously that's going to impact what happens with A I. Let's talk about the regulatory front since we're at a global event. Obviously, we've got what's going to happen on the US front with regulation, which we've got some indication of, and then globally, how much is that going to determine what this next stage looks like? Look, the, um, I think every, every geography is gonna do it differently. I, I actually think the US will be much more about a framework for which to operate on and I'll actually put more of the responsibility back on the companies themselves. And I actually think that is not a bad model. I think Europe runs the risk frankly, of over regulating. Um and if they over regulate, will they ST you know, will they ST me the innovation? If you sign the innovation, what happens from the economic benefits? What do you anticipate in terms of policy or regulation? Two things? Um Number one is, um I think in the background, we have to really realize that we are entering into a golden age of A I. OK. Um You know, the, the nature of where A I is headed is probably one of the most um amazing technology innovation moments on the history, you know, of, of, of humanity, you know, and, and if you over regulate it, if you over um policies it to begin with, uh you'll end up stymie the innovation opportunity. So it's more important, Eric and I were talking about this earlier uh that you create frameworks guiding points, right? But ones that are aimed towards uh catalyzing the innovation that can take place, right? And the second thing that's interesting is in that same time frame, I think around the world, we're seeing uh a time where governments are looking to get closer to understanding what's truly on the minds of the citizens and the residents that actually are part of the communities that they live in. Right? And we're seeing this at the micro level in the United States most recently, if you look at the county level statistics that that came about in the most recent election. Um And so what we're finding is that experience is essentially the currency of trust. And if you want to build trust, you have to get closer and closer to the people that you serve, this is happening in the private sector, right? If you think about the companies or the brands that you love and the ones you get closest to that, you feel understood, you feel like you trust them, they're consistent. The same is true for governments. And so A I actually creates an opportunity to use the technology to actually be able to at speed understand how to get much closer to the people that are looking to participate in different programs. Are those programs showing up in the way that you want them to show up. Uh once you deploy the funding, right? So, so I'm gonna ask you if you have any questions and just raise your hand. We've got mic runners here and I'm not sure if we do have any questions, but as we um before I turn to that. I want to ask you who the net beneficiaries are going to be. We've seen the big tech companies obviously do. Well, you are a tech player and a lot of people want are concerned about the, the start up ecosystem and how that innovation flows from here. What do you see? I, I think we will be, I think individuals will be the great beneficiary. I just again, one of the big opportunities is gonna be around customer experience. And if you think about things that are miserable experiences today, we can completely solve that going forward. I mentioned earlier, this sales force announced that at their big conference, this, you know how many we've all this age board, but we all go to the experience where you dial a customer service number and it goes through 18 different ways to make sure you never get an answer. And now what they have is a voice at the other end, that's completely done through agents and it walks you through to get you, you know what you need in a matter of seconds. And I just think that this is early days of this, but you start to go through all these friction points that I don't think will be friction points. 18 months from now, two years from now. What do you think, you know a couple of things. One, the analogy that comes to mind is, is the interaction that we have today. With maps, you pull out your phone, you pull up a map, you want to go to a location, we're still responsible for the interpretation of that, right, and make sure the inputs right in the near future. Um You know, the map will be there in the background because the, the, the, the car, the device is actually taking over and it autonomously gets you to, you know, where you need to go from point A to point B. That is the magnitude of, of, of opportunity and change that's underway across every single industry um that, that will take place. And so you reimagine the way that we've done things right as a result. Now, that said, we also don't see a world where there's going to be just a few behemoths that ultimately end up creating these systems. And in fact, what we envision is an ecosystem where um um people will innovate, they'll build fine tune models, they'll build agents that are specific for a certain set of business tasks or outcomes. Uh And, and that will create a whole new world of opportunity that will take place, you know, much like what took place in the industrial revolution. We'll see a similar effect. I see a question from Bob Nardelli here if you could just go in the middle. Um Thanks, Di thanks Diane. Uh This is a great panel. Let, let me ask you a question. Uh Do you see any similarities between a I and electric vehicles because there was the same excitement and euphoria about EVs the mandate that came down. It was range anxiety and it ended up turning charging anxiety, right? No infrastructure. And you mentioned the point, we gotta build a lot of data centers and then we got to be able to supply energy to that. Now in Atlanta, you know, we just built the last nuclear plant, Vogel years over overdue and billions of dollars. So I didn't hear any of you mention about small mobile nuclear reactors where you actually control your own destiny at the data center. I know some of Microsoft and some of the other are, are still out looking for a place to plant a data center. Uh How do you, how do you generally think about is, is energy gonna gonna throttle the rate with which you introduce A I former Ceo of Chrysler I should mention, by the way, I'll, I'll respond real quick and then, and you can talk specifically to nuclear and get what you're what you're seeing. I the, the the old saying that says we often end up uh overestimating what happens in the short run and we end up significantly underestimating what happens in the long run I think is extremely true here. I think that the capacity and level of innovation that A I will bring, it will take a long time for what those new scenarios can be, start to come alive. And if you look back at the industrial revolution, back to the 18 fifties, 18 sixties, when you know the Bessemer steel process was invented and how long it took for people to realize that steel could now actually help you build skyscrapers, took many, many decades later, right? It was initially used in railroads. And I think we are in a very similar stage at this point where people will see, see A I improve substantially, but the timelines will be significant compressed. You go from five year inflection points to three year inflection points. What do you think? So, I agree with that. I mean, in some ways, the internet was, was a very real example of this, right, the first internet boom, you know, happened but didn't really ever deliver the promise and then it took time until we got there. I think we're in that. But I don't think it'll take as long just by virtue of what we can do today and the availability, this question of power is a very real issue. Like it's a very real issue. The question of nuclear, the reality is we can't build even the small nuclear reactors fast enough, right? It's going to take. So I do think that is part of the solution, but I don't think that's part of the solution in the next 5 to 7 years. I think we've got to think about alternatives and frankly, I think that means we are going to end up with kind of more natural gas and more solar and more, you're gonna have more of the available technologies that are out there today that we're gonna have to use as we start building out these data centers, you know, in the near term. But it also presses the need for more efficient ways of using A I and how you end up innovating that. So building smaller models specific to specific tasks, if you don't need all that data, you can be a little more depending upon what you're trying to do. And that's already happening. I think that it's starting to happen. I see we're at the two minute point and I want to give each of you a chance to give some closing thoughts in terms of um rus in the moment, but also give us a sense of both what's exciting concerning, I mean, help us see around the corner bit as to what we'll be talking about this time next year. I'm going to start with you Z Sure. Um I think first off, uh we're at a stage where um companies broadly is not one ceo I've met with over the last year who hasn't said I want to use A I to get closer to my customers and to be able to scale the rate at which we end up differentiating ourselves and being, becoming more personal, more connected, more convenient in time, right? And that's a major theme that said um half of you here in this audience are probably building some kind of an A I capability one way or the other half of you will be consumers and you'll be using it right with that. And in most recent research that we had is, is only about 20 to 25% of consumers that actually trust the companies that are building the A I to actually make the A I perform as it's actually being promised or, and, or you'd be able to trust with how the A I is being used to be able to facilitate them, right? So being able to approach uh the problem with um building trust where experience is the new currency that actually ends up driving how people will engage with your company and your application is going to be core, you know, a core essence in this. And, and I think that is in the context of the fact that a lot of people are just wary of all the hype. A lot of there'll be a big hype cycle that will take place and that we're in the midst of it, right? Lots of claims promises the timing for that. What that horizon actually looks like. I wanna give also a chance for you to give some, I think you're down to a Haiku or a limerick. At this point. We discussed this, I'm just not that talented. I I don't, I think the technology will not surprise us or disappoint us. I think we're gonna see the technology continue. I think the thing that most organizations are gonna struggle with is talent. I mean, this is gonna be the biggest change that talent has to begin to understand and think about how they do their jobs differently each and every day and to reskill generations of individuals. I think that's the challenge we're gonna face. Please join me in thanking our panels. I know we'll discuss today. Thank you again.

Daniel Swan, Senior Partner, McKinsey & Company, Zach Thomann, Chief Operating Officer, Americas & APAC, GXO Logistics Moderator: Ruth Umoh, Next To Lead Editor, Fortune; Co-chair, Fortune COO Summit

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