Blog – Thinkers50 https://thinkers50.com Scanning, ranking, and sharing the best management ideas in the world Tue, 03 Nov 2026 11:35:41 +0000 en-US hourly 1 https://thinkers50.com/wp-content/uploads/Thinkers50_RGB_yellow-fav-2.png Blog – Thinkers50 https://thinkers50.com 32 32 Thinkers50 Announces Inaugural Leaders50 Distinguished Achievement Awards https://thinkers50.com/blog/thinkers50-announces-inaugural-leaders50-distinguished-achievement-awards/ Tue, 03 Nov 2026 11:35:41 +0000 https://thinkers50.com/?p=90783 Six new awards recognise exceptional achievements of practising leaders across transformation, innovation, technology, talent, integrity, and thought leadership.

LONDON 3 NOVEMBER 2026 – Thinkers50 has announced the six recipients of its inaugural Leaders50 Distinguished Achievement Awards, at the Leaders50 Awards Gala in London’s Guildhall.

Introduced as part of Thinkers50 25th anniversary celebrations, the Leaders50 Awards recognise practising business leaders whose achievements are making a significant impact on their organisations, industries, and wider society, and honour specific contributions across six areas: transformation, innovation, technology, talent, integrity, and thought leadership.

“For 25 years, Thinkers50 has celebrated the ideas that make business and management better. With the Leaders50 Distinguished Achievement Awards, we are also spotlighting the people who put those ideas into practice. These awards celebrate leaders who are not only achieving exceptional results, but also helping to redefine what outstanding leadership looks like.” – Des Dearlove, co-founder, Thinkers50.

“The six recipients demonstrate that exceptional leadership takes many forms. Whether transforming organisations, developing talent, harnessing technology, or setting new standards of integrity, each has made a distinctive contribution. Their achievements offer valuable lessons and inspiration for leaders everywhere.” – Mikko Leskelä, CEO, Thinkers50.

 

Leaders50 Transformational Leadership Award

In partnership with Michigan Ross Executive Education and dedicated to the legacy of CK Prahalad

The Transformational Leadership Award honours exceptional change agents who successfully lead significant organisational transformation. They mobilise and motivate people to make a lasting impact on the culture and operation of their organisations.

Recipient: KEN KELLER, CHAIR AND CEO, DAIICHI SANKYO, INC.

“The Thinkers50 inaugural Transformational Leadership Award is presented to an exceptional leader whose actions exemplify how organisations can navigate complexity and unlock human potential. This honour recognises a leader’s commitment to building dynamic learning environments characterized by psychological safety, engagement and collaboration, where teams openly share insights, challenge assumptions, and drive innovation under pressure. By championing a culture that transforms honest feedback and intelligent failure into strategic strength, Ken Keller stands as a true exemplar of high-impact leadership suited for today’s challenges.” – Amy Edmondson, Novartis Professor of Leadership and Management, Harvard Business School; Thinkers50 #1 Ranking 2021-2023.

 

Leaders50 Leadership Integrity Award

Sponsored by Deloitte

The Leadership Integrity Award honours leaders who act with honesty, consistency, and courage, particularly when facing pressure, uncertainty, or difficult choices. Their leadership builds trust and creates lasting value for organisations and society.

Recipient: MURSAL HEDAYAT, CEO, CHATTERBOX

“What I find particularly inspiring about Mursal’s leadership is the way she brings together shared values, social impact, community and entrepreneurship. Through Chatterbox, she has created meaningful opportunities for refugees and other overlooked talent while showing that business can be both commercially successful and a force for good.” – Neri Karra Sillaman, author of Pioneers: 8 Principles of Business Longevity from Immigrant Entrepreneurs; Thinkers50 Radar Award 2025.

 

Leaders50 Talent Magnet Award

In partnership with LHH

The Talent Magnet Award honours leaders who consistently attract exceptional talent and bring out the best in others. Through their personal leadership style and commitment to developing people, they turn their organisations into talent magnets – creating workplaces  and cultures that people want to join, and where they can grow, contribute, and stay.

Recipient: JURE KNEZ, CO-FOUNDER, DEWESOFT

“Jure Knez does more than offer people jobs – he creates communities where employees and entrepreneurs can build a future. Through his pioneering Katapult and City of Acrobats projects he has created an entrepreneurial collaborative and an entire city designed to nurture local talent and support entrepreneurs. The City of Acrobats takes the Thinkers50 2025 Breakthrough Idea –  the Vested business model – and creates a highly collaborative win-win ecosystem with the bold, shared vision to regenerate Slovenia’s former coal-mining Zasavje region.” – Kate Vitasek, creator of the Vested business model; Thinkers50 Breakthrough Idea Award 2025.

 

Leaders50 Technology & Human Excellence Award

In partnership with Origina

The Technology and Human Excellence Award honours leaders who exemplify the best of technology-enabled leadership, using technology not simply to drive efficiency, but to amplify human capability, creativity, and contribution.

Recipient: SURESH VENKATARAYALU, CTO, HONEYWELL

“What I appreciate about Suresh is that he puts people at the centre and leads like an entrepreneur. He gets directly involved to understand customers and figure out how to create value. You can’t delegate that responsibility. He invests personally in developing future leaders and challenges them to take the same hands-on approach. His leadership connects the potential of technology with the people who make it count.” – Alex Osterwalder, co-inventor of the Business Model Canvas; Thinkers50 Strategy Award 2015.

 

Leaders50 Thought Leadership Award

In partnership with Smith Publicity

The Thought Leadership Award honours practitioners who have made a significant addition to our understanding of contemporary leadership theory; recognising leaders’ contributions to the wider field – not just the success of their own organisation. 

Recipient: FRANCESCA MORIANI, CEO, VAR GROUP

“Francesca Moriani is not only putting innovative leadership into practice, she is contributing fresh ideas to how we think about leadership itself. Her concept of ‘braveship’ challenges traditional hierarchies and makes a compelling case for distributing responsibility, encouraging autonomy and giving people the courage to act. She is helping to redefine what it means to lead in today’s organisations.” – Mikko Leskelä, CEO, Thinkers50.

 

Leaders50 Innovation in Leadership Award

In partnership with the International Coaching Federation (ICF)

The Innovation in Leadership Award honours leaders who have introduced original approaches to the practice of leadership, harnessing innovation to influence others and deliver meaningful impact. These are individuals who challenge convention and develop new ways to empower people and achieve results.

Recipient: TARANG AMIN, CEO, e.l.f. BEAUTY

“Tarang Amin is recognised for building a culture that combines employee ownership, inclusive teams and shared accountability to unleash creativity and drive performance. The Innovation in Leadership Award celebrates innovation not simply in what a business creates, but in how its people are empowered to create it.” – Sanyin Siang, Executive Director of the Coach K Center on Leadership & Ethics (COLE) at Duke’s Fuqua School of Business; Thinkers50 Coaching & Mentoring Award 2019.

 


 

The awards were presented during the Thinkers50 London Summit & Leaders50 Awards Gala on 2 November 2026, bringing together leading management thinkers and business leaders from around the world to celebrate Thinkers50’s 25th anniversary.

ABOUT THINKERS50

Founded in 2001 by business journalists Des Dearlove and Stuart Crainer, Thinkers50 began as the first global Ranking of Management Thinkers. Over the past quarter century, it has grown into a worldwide platform and a vibrant Community. Today, Thinkers50 encompasses the Thinkers50 Distinguished Achievement Awards alongside the Hall of Fame, Radar, Coaching Legends, Leaders50, Coaches50, and curated Booklists. The Leaders50 Distinguished Achievement Awards were launched in 2026, as part of the Thinkers50 25th anniversary celebrations. The Thinkers50 London Summit & Awards Gala has been described by the Financial Times as the “Oscars of management thinking.” 

MEDIA CONTACT 

Visit Thinkers50 Press Room
To get in touch please email: enquiries@thinkers50.com
thinkers50.com

 

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The Provocateurs Episode 46 | Hamilton Mann: Rethinking AI Governance as Constitutional Design https://thinkers50.com/blog/the-provocateurs-episode-46-hamilton-mann/ Fri, 23 Oct 2026 07:45:01 +0000 https://thinkers50.com/?p=90442
Play Video about The Provocateurs Podcast: Hamilton Mann: AI Needs a Constitution

Thinkers50 in collaboration with Deloitte presents:

The Provocateurs:

podcast series

EPISODE 46

ABOUT THIS EPISODE

Hamilton Mann: Rethinking AI Governance as Constitutional Design

Hamilton Mann argues that one of the most urgent challenges posed by AI is the concentration of algorithmic power. He identifies three layers: capability, as a small number of companies develop powerful AI systems; distribution, as those systems reach millions of people; and, most consequentially, governance, where some algorithmic systems can increasingly shape decisions, allocate resources, and determine outcomes.

Conventional technology regulation is not enough, Hamilton says. AI governance should be a question of constitutional thinking – asking not simply what rules powerful systems should obey, but where their legitimacy comes from. 

Hamilton is the author of a new book, Trias Algorithmica: What Code Rules and Artificial Integrity, which was named a Thinkers50 Top 10 Best Management Book for 2025. He also received the 2025 Thinkers50 Digital Thinking Award, following his induction into the Thinkers50 Radar in 2024.

This podcast is part of an ongoing series of interviews with executives. The executives’ participation in this podcast are solely for educational purposes based on their knowledge of the subject and the views expressed by them are solely their own. This podcast should not be deemed or construed to be for the purpose of soliciting business for any of the companies mentioned, nor does Deloitte advocate or endorse the services or products provided by these companies.

Hamilton Mann, Thinkers50 Radar Class of 2024.

Hamilton Mann

Chairman and President, Artificial Integrity Institute;
Author, Trias Algorithmica and Artificial Integrity

Hosts:

Mikko Leskelä, host of the Provocateurs: Profiles in Leadership Podcast.

Mikko Leskelä

CEO, Thinkers50

Geoff Tuff, host of the Provocateurs: Profiles in Leadership Podcast.

Geoff Tuff

Global Sustainability Leader for Energy and Industrials, Deloitte

LISTEN NOW ON

Inspired by the book Provoke: How Leaders Shape the Future by Overcoming Fatal Human Flaws; Wiley, 2021.

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#TheProvocateurs

EPISODE 46

Podcast Transcript

Mikko Leskelä:

Hello, and welcome to the Provocateurs podcast. I’m Mikko Leskelä, the CEO of Thinkers50. In Provocateurs, we explore the experiences, insights, and perspectives of inspiring leaders. Our aim is to provoke you to think and act differently through conversations with some fantastic people. This is a collaboration between Thinkers50 and Deloitte, and my co-host today is Geoff Tuff, Deloitte’s global and US leader for sustainability work with energy resources and industrial plans. He’s also the co-author along with Steven Goldbach of three books, the latest one of which is Hone: How Purposeful Leaders Defy Drift. Geoff, great to see you.

Geoff Tuff:

Mikko, it’s great to be here, and I’m looking forward to doing our first podcast together, but I’m looking even more forward to today’s guest. So Hamilton Mann is with us today, and I’ll tell you as I get into this, he has one of the more impressive and complicated and French introductions that we’ve had on the show so far. So I’m going to do my best to give his bio to do it.

Mikko Leskelä:

Take a shot. Take a shot.

Geoff Tuff:

So here we go. So Hamilton Mann, he is a prominent AI researcher, a technology executive, an acclaimed author, a lecturer at INSEAD and HEC Paris, a mentor at MIT Priscilla King Gray Center, and he conducts doctoral research at École des Ponts et Chaussées – Institut Polytechnique de Paris. How did that do?

Hamilton Mann:

Very great.

Geoff Tuff:

All right, thank you. Thankfully I made it through that part. So Hamilton originated the concept of artificial integrity, which is going to be, I think, part of the focus of our conversation today. And he explained that in his 2024 book of the same name. He also serves as chairman and president of the Artificial Integrity Institute, a nonprofit organization dedicated to advancing AI systems that prioritize integrity over intelligence, which is going to be a fascinating topic to dig into. He’s also the author of the upcoming book, Trias Algorithmica: What Code Rules. I hope I got that one right. And that’s going to be released later this year by Wiley. In addition to all of that, Hamilton was inducted into the Thinkers50 Radar in 2024 as one of the 30 most prominent rising business thinkers. He received the Thinkers 50 Distinguished Achievement Award for Digital Thinking in 2025, and his book Artificial Integrity was named a top 10 best management book for 2025.

Hamilton, welcome.

Hamilton Mann:

Thank you. Thank you very much. Great to see you both, Mikko and Geoff, and very happy to be here.

Geoff Tuff:

Great. Well, then why don’t we dive in? Hopefully there are no more French accents that I’ll need to pull out for the conversation, but let’s start with your argument that AI has become a new form of power. What kind of power are we talking about here and what makes it fundamentally different from other forms of power like political power or economic power or corporate power that most of our institutions were designed to govern?

Hamilton Mann:

Yeah. Okay. So where to start from there? So AIs, or I prefer to call it algorithmic systems, are quite not comparable to those traditional powers that we know as political power or economic power or corporate power. Algorithms and algorithmic power very much represent something more unusual in a way. It is increasingly becoming a power over the condition under which all those forms of power, whether it is political, economy, corporate, are operating. Political power basically and traditionally tells you what you may or may not do. And economic power is more determining what it is that you can afford and the corporate power is like determining what gets produced, or sold. But algorithmic power increasingly operates one level upstream. One level upstream. And this is the big difference with all the other kinds of power that we know. It can influence what you see before you form a political or an economic opinion, which is concerning most of us, of course.

It can also influence what kind of information is going to reach you in the first place before you make any kind of a corporate judgment or political judgments or whatsoever, which also present the form of a possibility to influence any kind of business choices that you want to make. And of course, when it comes to business choices, you can think about any kind of decisions you can take in your each and every day. So that is a very different kind of a power, algorithmic power, because it is the form of a power that designed the environment in which the decision and the actions occurs. And once you see that, there is also something quite unsettling that follows directly, which is for most of political history, we are quite worried about the person who controls the answers.

We want to make sure that there are not big concentrations of people that have a kind of monopoly in terms of controlling the answers. When it comes to algorithmic power, and this is basically the age that we are living nowadays, what we may worry about when it comes to that power is more the control of the ability to question… that algorithmic systems that, basically, will be controlling the menu of possibilities of questions we are most likely to be influenced to be asked. And even the order in which we want to, or we may encounter, the particular set of questions that we want to be asked. So algorithmic power is not simply about what you may…  let’s say reading the headlines nowadays, which is thinking about action, autonomous action, or making autonomous decisions; it is more about shaping which questions – and therefore which actions and which decisions become thinkable in the first place.

Geoff Tuff:

So I’m hearing, actually, that there’s actually two aspects to what you’re saying there. Not only does it control or influence the context in which other forms of power are exercised, but it then also helps inform or control the dialogue that ensues and the way that others engage with those other types of power.

Hamilton Mann:

Absolutely.

Geoff Tuff:

Now you’ve got me totally freaked out.

Mikko Leskelä:

Hamilton, what then, has really changed? I mean, let’s think back 15 years. The algorithmic power has been there for quite a while already. It’s been shaping what we see, what we believe and what we buy and increasingly who we become. And I see in your argument that you’re saying that actually the large scale introduction of AI is actually changing even this. And we used to be worried, weren’t we, already 10, 15 years ago about the algorithms. Is there something that has substantially changed now, Hamilton?

Hamilton Mann:

I think what is changing is that the impact of the algorithmic power is very much concerning at the civilization level because it touches something that is very much connected to our psychology as humans. Looking at the kind of a line that that algorithmic power is crossing: when the algorithm comes into your life, whether you are using it through your preferred chatbot or your preferred recommendation systems or whatsoever, it starts to begin to change you.

And I think this is something that most people don’t understand very much. And this is what needs to keep us aware and make us pay attention. For example, we call some of those algorithms or algorithmic systems recommendation engines. Although they are just learning your preferences to give you basically more of what you like. But a sufficiently powerful recommendation system, it is not just about giving you more of what you like. They do it much more quietly, silently. You don’t really acknowledge it, but this is very much what is happening.

If I show you one thing rather than another one, that is a recommendation. You see one thing, you see the other one, you make a decision and okay, we can call that recommendation. Now, if I do that very same process, showing you one thing and then another, but I do it a thousand times, deciding what is visible, deciding what I’m going to repeat, deciding what is going to disappear from your visibility, deciding what is going to be flagged as popular, deciding also what feels normal based on what other people like or say or whatever, so almost deciding, also, what becomes emotionally salient in a way, then I’m no longer simply giving you more of what you like.

Mikko Leskelä:

Yeah. So with this pace and this amount of repetitions, we actually lose sight of the algorithmic power. So we used to use products that had an algorithm baked into them. We were still sensing that this is the algorithm doing their work, but with the new powerhouse that the AI is, we kind of lose sight of that and we just start to go with the flow. Is that what you’re saying?

Hamilton Mann:

Absolutely. Because it brings some kind of new, advanced possibilities with the capability of interacting with you with natural language, and all of this based on that very process that comes with a form of a feedback loop, in terms of showing you what you basically is expect to be shown, presenting you things that are shown to us as a preference or as a recommendation. Even though it is absolutely not your preference in the first place, but it is presenting like this, it starts to change you because it starts to make you feel like, oh, this is something that I prefer…

Geoff Tuff:

It’s actually impacting your preference.

Hamilton Mann:

You just start to buy those kinds of generated AI or implemented generated AI preferences that are shown to us and that you take for granted. So this is where that algorithmic power is starting to change people at a kind of an unprecedented pace that we haven’t seen before with other forms of algorithms.

Geoff Tuff:

So I have lots of follow up questions on that because I can actually see how what you’re saying right now, Hamilton, plays out in all sorts of different spheres, not just product preference, but the type of political organizations you might align with, the groups you might affiliate with, et cetera, et cetera. But let me keep it at the more fundamental level to begin with. A lot of what we hear people being concerned about these days, a lot of the public conversation about AI is focused on what happens in the future when we have these super intelligent machines and are they actually going to at some point have the ability to take over the role of humans? I think your argument is the much more immediate concentration, immediate danger of the concentration of algorithmic power in the hands of a few. And so I’d ask you to maybe just comment on that and why you see that as the bigger risk now versus what comes in the future.

And also I’m intrigued, so I understand the concept of what you’re saying and I understand the risk of it. How much do you think those few are actually exercising that power today? How much are we being influenced without even knowing it today versus this simply being a risk that may come in the future?

Hamilton Mann:

Yeah, no, that’s a very key point. And I will take the opportunity to deep dive into the ramifications of that system. Let’s park for a second the super-intelligence topic. I’m coming back to that later. There are actually three concentrations happening at once. And I think this is something that we need to pay attention to, even more than the fact that there are just a few companies at the end of those capabilities, in our planet. So the first concentration, which is the one that is more visible, is happening at the level of capability.

So let’s put it like happening at the level of capability layers. So this is the fact that you have a small number of companies. They do have that capability to build some form of frontier algorithms, algorithmic systems, et cetera. This is the tip of the iceberg.

There is something that needs to be acknowledged even more in that point, a second concentration. The second concentration happens at the level of distribution. And the level of distribution is basically also something that we can see with naked eyes, which is the layers of the reach of those very same kinds of private institutions that happen to concentrate the first element that I was mentioning with the ability to reach millions – and sometimes billions – of people around the world. And that’s a form of concentration in itself also as well. When you think about the first concentration, which is the concentration of capabilities in the hands of a few companies, and then you combine that concentration with the reach and the form of a distribution that touches billions of people, it starts to become questionable.

But we are still at the level of what I will call a form of a submerged but still discernible body of the iceberg, let’s put it this way. I think what is very important to notice, which is something that is not that discussed nowadays, is the third level of concentration, which happens at the level of the governance layer. So you have the capability layers, you have the distribution layers. And what is very much for us to be worried about and to question and to challenge even more is what happens in the third layer, which is the governance layer. And at that governance layer, again, often captured by a few of those private institutions and companies, this is where the algorithmic power is exercised, which inherently combines functions that our political systems we have historically tried to separate.

And I’m talking about functions like legislative power, functions like executive power, and functions like judicial power. At the third layer, you have some of those algorithmic systems. They very much fuse those three forms of political power that we know very well in any kind of political discourse. Of course, we don’t name those kinds of powers with those terms – legislative, executive and judicial. But basically they have similar forms of traction, similar forms of influence and similar forms of power to societies. So we are very much at the level of the hidden base of the iceberg, the part of the iceberg that is quite invisible. And this is actually the most consequential part of the iceberg, the most consequential of those three layers of concentration of power. Why? Because the first two concentrations, they tell us who controls the algorithmic power.

This is what they tell us. This is of course a trigger for asking questions and challenging “how do we want to regulate?” But basically the information that we get with the first two concentrations is who controls that algorithmic power? The third concentration tells us something very much more interesting, which is the nature of that algorithmic power. The nature of that algorithmic power is not just about generating you the text that you like for any form of task that you have to perform or deliver for your boss or whatever. The third layer in itself tells us how it is that this capability layer, and how it is that these distribution layers, actually concentrate in terms of power? So if you have something concentrating any form of a power because they have capabilities and because they have distribution, at least you can sleep well knowing that the power that is concentrated is not something consequential.

The third layer is very much there to tell us, guys, because of the two first layers, let’s double click on the power that is very much created and let’s double click on the power that is distributed. So I think this is basically where we do have some things to question, the type of societies we want to build when it comes to what we call AI, and especially when it comes to building societies with algorithms. 

So back to the super-intelligence type of things, which is basically what you were saying in terms of, okay, we heard a lot of people saying that, okay, in the future we all are going to live on Mars and we will have super intelligence and we will have singularity, et cetera and so on and so forth.

My take is very much quite pragmatic in that I think it is worse to make a prediction. I mean, it doesn’t cost any money, so anybody can make a prediction.

The good news is that we know that when it comes to predictions, and even though those predictions are very much well-informed with some data and so on and so forth, any prediction that you make today may be wrong tomorrow because new data comes into play and change the landscape that has been used to make the prediction. So I don’t know if we will have super intelligence. I don’t know. Maybe yes, maybe no, maybe it is something different and so on and so forth. When you talk to people, everybody has their own definition of what it is. I don’t know if we will have super intelligence.

And so I think that when it comes to thinking about what we can do to build societies that we want to live in, what is it that we can do to foresee a better kind of a world where human beings can strive to continue to build on values in the world of algorithms, I think we need to also look at the very problems that we have to solve today, not be too distracted with some potential issues of problems we may have tomorrow. Because when we do that, what happens is that we displace the level of attention that we need to have in terms of what is happening now to what may happen tomorrow.

But the point is that if we don’t have today with the very situation that we need to handle, I mean, there is less chance that we will be relevant in terms of facing and, I will say, coping, with the challenges that may come tomorrow.

Mikko Leskelä:

So you are basically also saying that this is very much a burning governance issue and we can’t do the governance the way we used to. So using some kind of conventional technological regulation, which is by the way, in the EU, it’s one of the things we boast we do well, but it’s more like something deeper in the structure of society, almost like constitutional design, would you say?

Hamilton Mann:

Absolutely. And the funny thing about the EU advances that sometimes we think we have, and sometimes the US has told us that we have in terms of regulating digital space and AI systems and so on and so forth, is that paradoxically, because you have very advanced algorithmic systems that fuse that form of legislative like, executive like, judicial-like power, they are very much well advanced in terms of regulating our life more than the EU.

Mikko Leskelä:

Now let’s imagine that we were rewriting the constitution today in the age of AI and algorithmic power. What would it look like? What kind of things would it need to address? 

Hamilton Mann:

So to answer that question, first, I think it is worth looking at the difference we need to make in terms of what is regulation and what is constitutionalism. Regulation, to put it very simply, asks what rule should powerful systems obey no matter what kind of a system it is, corporation, institution, systems, right? What rules should powerful systems obey? That’s regulation. Constitutionalism, ask a very different question. It is an upstream kind of a question, which is why do these actors that we’re trying to regulate, why do these actors have all this power to begin with?

So the constitution doesn’t take for granted what it is. It just questions what it is. As opposed to regulation that tends to take for granted what it is and thinks “okay, it is what it is, so how do we put some rules of the game to cope with what it is.” And that’s the big difference. So let’s imagine that tomorrow every frontier AI company, not to name any one, complies with regulations that have been put in place. For example, requiring perfect transparency and they do, requiring them to publish excellent model cards and they do, requiring them to eliminate bias in any form to their greatest instance as possible and they do very well.

And still, even if they do everything written in the regulation, we still have a constitutional question that remains because the concentration of power does not become legitimate just simply because it is well regulated. So the question when it comes to the constitution is very much about where does the legitimacy come from? Where does the legitimacy come from? What makes an algorithmic system legitimate in the first place? And so we know how to ask such kinds of questions when it comes to voting, for example. We know that you are, for example, elected for X, Y, Z kind of a role, a public role. And so we know that that person is going to speak from that role and their legitimacy is basically won by the election.

When it comes to algorithmic systems, these are the constitutional questions we need to ask ourselves just because such – not all of them but some – algorithmic systems are so powerful that they diffuse those three functions of power that constitutionalism has been working to… So I think that we are becoming in a way accustomed to trading performance and, somehow, also compliance, as a source of legitimacy because the thing is working well and we are okay with that. We don’t question it. Oh, it generates something very great. It looks like it something that I could have been writing that, even like, maybe I’m not sure I’ve been writing as much as it did in that form of quality and so on and so forth. And so we just sometimes treat the behaviors of such kinds of systems at the surface level of performance, or sometimes compliance, and forget about questioning the legitimacy in the first place, the source of legitimacy in the first place.

If a model complies with every regulation, we should still question the legitimacy. So basically I think a concentration of power does not become legitimate simply because it is regulated, that’s one. And second, the great achievement that constitutional thinking, basically, was able to bring to society is that it is not just about regulating authority, but it is about preventing power from becoming so concentrated that meaningful contestation, meaningful ability to say no or to challenge that power becomes just impossible.

This is what legitimacy brings to the debate, and brings to that conversation – being able to give us, as humans in the face of algorithmic systems that are so powerful, the power to contest, to say no, to challenge, to question. If we are not able to create that source of legitimacy in which that power is exercising, then progressively we are going to lose, basically, our very power to say no, to contest, and to challenge that power because it goes at the speed of the bits. And so sometimes even though we put a human in the loop, I mean we are just human. We are imperfect as humans. And so in many cases, because we are in the loop, we are, basically being in a system where we cannot have the human possibility to contradict or to counter the power in itself.

So to me this is the reason why we need to think constitutionally about these things. So maybe let’s give a few examples in terms of, if we had to write basically that constitution, what kinds of examples could be put in the paper. First, we can think about whether there will not be no consequential algorithmic decision. I’m putting it this way, like a consequential algorithmic decision, because I don’t want to fall into a trap of one size fits all. There are different forms of algorithms and they are not all equal. So just thinking about the most consequential ones, which are the ones that have the biggest capabilities, the biggest distribution, and the biggest fusions of those three functions that we were talking about. I think that first we need to think like they should not acquire finality just because the machines have been producing something.

What I mean by that is that whatever that machine is producing, whether it is – a recommendation for a credit, a recommendation or advice for hiring somebody or someone – whatever that machine is producing, it should not be considered as a finality. 

A second point could be also from a kind of a constitutional level perspective, like a company that builds consequential algorithm systems cannot be the only institution determining whether that system is safe, fair, robust, or socially acceptable. Constitutionally speaking, that could be also an example of things that we collectively agree to write.

Because if you have one single institution with big capability, big distribution, big fishing of algorithmic power, just deciding by itself with no deliberation, no conversation, no democratic debate, deciding by itself that the very system that they have been building is safe, is fair, is okay for society because of the capability and because of the distribution at scales to millions, to billions of people, it very much creates a kind of a society where you put all your eggs in the same bucket.

Geoff Tuff:

And there are no checks and balances at all.

Hamilton Mann:

Exactly. Exactly.

Geoff Tuff:

So this is a fascinating and honestly quite frightening topic that I think a lot of what we’ve talked about so far is the subject of your upcoming book, Trias Algorithmica, and I’m probably butchering the name of it there. But let’s take a step back and get to your previous book and some of the underlying principles behind this, the whole notion of artificial integrity, because I do want to make sure that in the limited time we have left, we spend a little bit of time on that concept because you do draw a distinction between artificial intelligence and artificial integrity. And I think, and some of this has come up in our conversation so far, we’ve learned to do a good job of asking how right does the system get it? How capable is it of approximating what a human answer might be? But we’re not as good at asking, should we trust it?

And so I guess the question to you on that, and I’m doing you the disservice of asking you to summarize an entire book in a few minutes, but what does artificial integrity mean to you? How will we know when an AI system actually possesses it? And how do we get to a point where we can not just ask after a technology is deployed, should we trust it? Is it harmful? But how do we get out in front of that and actually not allow a technology to emerge in the first place until we’re sure that it is trustworthy? That’s a lot of questions built into one, but please do comment.

Hamilton Mann:

Yeah. So let me, let’s unpack a little bit on that. So first you’re completely right. We have spent most of the history of AI and algorithmic systems asking one question very obsessively: what can the machine do? And this is still the question that many people are asking. And when I say many people, I’m not thinking just about AI developers, I’m thinking very much about society in itself. What can the machine do? Artificial integrity begins with a very different question, which is what should the machine do? As simple as that, what should the machine do? So intelligence is fundamentally about capabilities, and we talked about capabilities a bit earlier. It is about, can the system reason, for example. Can it predict, can it generate some kind of outcome? Can it plan? Can it take actions autonomously?

Can it, let’s say, optimize on some very specific goals or direction that we give them? This is, basically, the type of question that people are passionate a lot about with AI nowadays. Artificial integrity is about governing the exercise of those capabilities, governing the exercise of those capabilities. So it asks, should this problem be solved? It asks, should I solve it? In being the AI system in itself it asks, who should actually make this decision? Me as a machine in a way, or maybe the human, the users. It asks, what values are the intention there? What might be harmful? What happens if I’m wrong? Those kinds of questions. And so this is basically why I describe artificial integrity as a form of a metacognition or metacognitive capability, because it is the intelligence developing the capacity to interrogate its own intelligence. Most of the AI systems nowadays lack these very simple capabilities.

So because there is an assumption embedded in much of the technological progress that we make, and especially when it comes to AI, that if a system is becoming sufficiently capable of accomplishing an objective, a task or whatsoever, then accomplishing the objective is somehow justified.

This is pushing us in a society where sometimes we put in the market things that are very impressive, that are very convincing, that are very powerful, but I mean, impression, power, and convincing is also something that can generate harm and consequential harm in a way, just because it is convincing and just because it is powerful and just because, of course, it is something that can influence as we talked about earlier. So this is basically what artificial integrity affords. It’s a way to inform better the kind of artificial intelligence that we are building, so that the system is able to know how to use that intelligence the right way, in a way. So of course we still have a gap to fill in terms of advancing such a kind of capability.

There is much research around that topic going on. This is not always called artificial integrity in the first place, but this is basically the same kind of a family. I’m thinking about some kinds of work that is done when it comes to alignment with value, for example.

I think governing intelligence, so creating artificial intelligence machines that are able to self-govern their very own intelligence is basically what makes the distinction between alignment with value and artificial integrity in a way, right? Because alignment with values is basically something that you can train the model on, a bit like once in a while, and then maybe try to adjust along the way, after the model is already deployed, along the way adjust the model to make sure that it is most likely to behave in alignment with those values.

Artificial integrity goes a step further. It’s very much about giving the ability to such systems to think ex post and ex ante. Ex ante in terms of now that I’m processing the things that the user has been asking me, what is it that I need to generate, produce; what is the kind of outcome that aligns not only with value but with context, not only with context, but with the users, whether it is someone who is five years old or someone that is 19 years old, or someone that has vulnerabilities no matter the age, and so on and so forth.

And ex post, in terms of, I am able to learn in terms of the consequences to which I have been participating by the generation of the outcome that I’ve been produced, and I am able to learn from those consequences. So it’s going to inform also the behaviors that I’m going to have for the next action. That’s artificial integrity. So of course, needless to say that we are not there yet. There is a lot of to do with a lot of work to do in terms of researching in that direction, because this is basically, again, very important. This is a form of a safeguard that ensures us that when you have autonomous systems behaving in societies, they’re not just behaving because they have an amount of intelligence like X, Y, Z degree. They are behaving also with a form of a compass that always interrogates, questions, and challenges the very own course of what is it that they are going to produce with their intelligence.

Mikko Leskelä:

Gentlemen, they say time flies when you’re having a good time. Hamilton, we won’t let you go. We wouldn’t be Provocateurs if we did. We won’t let you go without asking you to give us a bit more provocative closure for this discussion also. And we’d like to ask that if you could provoke one fundamental rethink in the way business leaders are approaching AI right now, so leaders of private organizations, what would it be?

Hamilton Mann:

To me, the point is very straightforward. I think business leaders all around the world, they need to acknowledge that when it comes to algorithmic systems, it is not just about technological systems entering the technological stack that they have in their corporation, the technological stack that we may see or have in any kind of an institution. There is much more to pay attention to. And I say much more to pay attention to at CEO level, at board level. And what needs and what deserves attention beyond the technological stack, kinds of integration, adoption and so on and so forth, or even the productivity gain that you may expect to have from it, what needs to gain attention, and especially when it comes to CEO and board level, is that the algorithmic power is a power.

So it is also entering the authority stack. Enter the authority stack within the wall of your corporation. And you know what I mean? It means that, of course, in any corporation, public or private, you have processes, you have governance, you have hierarchy. We know that in any corporation, and of course even in business, you have power into play. Dotted lines and so on and so forth.

So I think what is very key for a business leader to understand is that when it comes to algorithmic systems and inviting and hiring algorithmic systems within the force of your companies, look at it not just as a technological stack, let’s say integration and transformation, look it at a kind of authority stack that will come to be integrated with the authority stack that is very much the foundations upon which your corporation is living and ask yourself a few questions, like where the power of my authority stack, my existing one, where the power that I’m working with the governance that I’ve been putting in place and that I’ve been working for days and years to distribute and to organize, where those powers shift. Is there any form of displacement? You need to be aware of it. You need to know that, and you need to know that first before any deployment, not after. So that’s to me, something that leaders need to pay attention to. Bottom line, I think as a leader, you also have a role to play when it comes to societies beyond profit, beyond revenue. And I think this is something that can remunerate all of us in terms of thinking about the world we’re building with algorithmic systems. Personally, I don’t want a society where we begin redesigning human beings around what AI optimized best. I prefer a society where we design AI around what being human is at its best. And I think I’m going to leave there. This is, I think, something that leaders can be invited to think about when it comes to, let’s say, the decision of deploying algorithmic systems within the world of their corporation.

Geoff Tuff:

So Hamilton, I think that was fantastically said. I want to ask you a question and maybe we’ll just book another podcast to delve into it in more detail, and then we’ll go over to Mikko for the close. But recognizing that many leaders out there agree absolutely with what you just said, some are going to need to be compelled as a catalyst to believe that there’s real economic value in doing the things that you’re suggesting. Where would you suggest profit oriented companies start on the road to what you’re describing today with more of an economic value orientation in mind? What should they be asking themselves, what should they be doing, and who should be responsible for it in the company?

Hamilton Mann:

Well, I think basically, I think the question about legitimacy, I think is a good question to start with. And this is very much at the heart of any governance principle that leaders set up within their corporation. We want to make sure that any decision is based on a kind of a foundation of legitimacy, because this is what drives and what drives action the right way. This is what motivates people, right? When you are in an organization where decisions flow from the top and you don’t know what is the legitimacy of it, right? Sometimes what we call, what is the meaning of it? What is the purpose of it? You start to demobilize people, right? And I think this is at the heart of what leaders need to pay attention to when it comes to that transformation. Algorithmic systems that automate, that orchestrate, that take autonomous actions within the world of your company, it’s great.

I mean, the first day you impress and you’re like, “Wow, this is great. Let me take a picture.” But do not forget that you are basically, in the very first place, a human asset first. A human asset first. And so leaders need to continue to question, how is it that I’m going to give legitimacy to what I’m doing, to the decision that I’m taking in hiring algorithmic systems that happen to be included in my organization? Because what I want in a way is that I augment my operating models with those new capabilities, joining with the human forces and not having something that diminishes the power of humans just because the algorithm is coming. And so this is, to me, I think, something that leaders need to reflect on. There is no secret sauce and no step-by-step process to follow through.

And this is basically what makes the beauty of leading, right? Because everything is not written. Sometimes you just have to observe, know the context, feel, the ambience in a way to basically decrypt how it is that you are going to script your way to go for it. But basically, it gives an invitation to leaders to acknowledge the fact that there is an amount of leadership that is unprecedentedly needed in corporations, in institutions nowadays, just because there is that trade off that leaders need to make purposefully in terms of what is it that I’m giving to the machine? What is it that I want to keep, in the end, of the human? Why? And be very transparent and clear about that why. Beyond profit, beyond productivity, beyond numbers. Because at the end of the day, if you just look at the short term benefits, it doesn’t give you the opportunities to make the long run.

Mikko Leskelä:

Geoff, you may be right. Maybe we need to book another session after Hamilton’s new book is out. But at this point, so many thanks to our guest, Hamilton Mann.

Hamilton Mann:

Thank you. Thank you guys. Thank you, Geoff, and thank you, Mikko.

Mikko Leskelä:

Oh, it was such a pleasure. And thank you for listening to this podcast episode. This is the Provocateurs podcast with Thinkers50 and Deloitte. We’ve been Mikko Leskelä and Geoff Tuff. Please join us soon again for another episode of the Provocateurs. Thank you, everybody.

Geoff Tuff:

Thanks, Hamilton.

Hamilton Mann:

Thank you. Bye.

This podcast is part of an ongoing series of interviews with executives. The executives’ participation in this podcast are solely for educational purposes based on their knowledge of the subject and the views expressed by them are solely their own. This podcast should not be deemed or construed to be for the purpose of soliciting business for any of the companies mentioned, nor does Deloitte advocate or endorse the services or products provided by these companies.

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Ben Fanning: Peanut butter and jelly leadership | The Provocateurs: Episode 9 nonadult
Why Decentralized Organizations Struggle at Scale https://thinkers50.com/blog/why-decentralized-organizations-struggle-at-scale/ Thu, 15 Oct 2026 08:00:59 +0000 https://thinkers50.com/?p=90484 by Janka Krings-Klebe and Jörg Schreiner

Why Decentralization Disappoints

At Porsche’s 2026 annual press conference, its CEO announced a familiar set of measures: the company would simplify its management structure, remove hierarchical layers and reduce bureaucracy. The intention was straightforward. A leaner organization would accelerate decision-making, reduce costs and allow the company to respond more quickly to changing market conditions. Similar announcements have become common across industries. As markets become more volatile and complex, organizations increasingly attempt to replace hierarchical control with greater autonomy at the team level.

Yet, the results often follow a recognizable pattern. After the first layers of management disappear, decision-making does not become faster. The number of meetings multiplies. Teams spend increasing amounts of time coordinating their contributions across the organization. Instead of greater agility, organizations experience more meetings, increased friction between teams, delays in resolving dependencies, and growing effort spent on alignment. These outcomes are often attributed to cultural resistance or insufficient commitment to new ways of working. A closer examination suggests a different explanation. Hierarchy is not primarily about authority. It is a mechanism for concentrating coordination. The hierarchical layers being removed performed important coordination work across teams. Their removal eliminates authority, but also the mechanisms that performed coordination across the organization. Hierarchy concentrates the coordination tasks required to align teams, resolve dependencies, and translate intent across the organization into defined roles. When hierarchical layers are removed, the coordination functions they carried must be allocated elsewhere. This requires a clearer understanding of what hierarchical structures actually coordinate.

Three Layers of Organizational Coordination

Starting from first principles, organizations exist to coordinate collaboration. Coordination refers to the mechanisms through which contributions are aligned, ranging from direct interaction to structured interfaces and system-level governance. Whenever value creation is sufficiently large or complex to require the involvement of more than one person or team, those contributions must be aligned. In small settings, people communicate directly, adjust their contributions, and resolve issues as they arise. Coordination remains local, informal, and largely invisible. As organizations grow, these conditions change. Once multiple teams contribute to the same outcome, coordination becomes unavoidable, regardless of structure. Relying on individual, ad-hoc communication patterns is still possible, but as the number of participants increases, it becomes excessively time-consuming. Moreover, agreements between individuals do not automatically create cohesion at the team level. Taken together, these bilateral agreements may still contradict one another or work at cross purposes.

Intra-Team Coordination

The first qualitative step in scaling coordination occurs within teams. Moving from individual, ad-hoc alignment to intra-team coordination requires a shared understanding of how work is coordinated and the willingness to accept binding rules within the team. As many self-managed teams experience, establishing this level of coordination can already be challenging, especially when rules are unclear and no one is able to claim authority.

Three layers of organizational coordination
Figure 1. Three layers of organizational coordination

Ideally, intra-team coordination takes place within the team itself. Individuals align their activities and interact directly around shared work. Ownership is clear, and decisions can be made quickly because participants remain close to both the task and to one another. At this scale, coordination relies primarily on social mechanisms: shared understanding, trust, and frequent communication. Teams function as small adaptive systems capable of responding rapidly to local signals. This form of coordination is highly effective, but only within the limited scope of work that a single team can perform. Agile methods originated at this intra-team level, focusing on improving collaboration, adaptability, and delivery within small, cross-functional teams. Approaches such as Scrum and Extreme Programming enabled teams to become highly effective units by emphasizing fast feedback, shared ownership, and continuous improvement. At this stage, agility was largely synonymous with team-level excellence.

Inter-Team Coordination

Whenever multiple teams are required for shared value creation, coordination must extend beyond the boundaries of individual teams. Organizations therefore need mechanisms that enable inter-team collaboration: coordinate how specific teams interact, how their conflicts are resolved, and how their contributions fit together and evolve over time. The coordination task thus expands from local optimization to larger-scale productivity. Inter-team coordination aligns those teams whose contributions depend on one another. Unlike intra-team coordination, which relies primarily on direct interaction, this level increasingly depends on establishing how contributions connect through defined interfaces, agreements, and shared structures. At this point, agile methods begin to show their limitations. While they optimize feedback and alignment within teams, they provide only limited mechanisms for coordinating dependencies across multiple teams. Yet, in larger settings, such coordination is essential, as teams cannot operate independently from one another. In response, organizations introduce additional scaffolding, such as Large-Scale Scrum or the Scaled Agile Framework, to preserve short-term agile value-creation cycles across several teams. These are necessary to coordinate and synchronize the work of several teams. Ironically, it leads to the emergence of yet another caste of managers, albeit with more “agile” sounding titles. Of course, organizations need not adopt these frameworks. They can take inspiration and invent their own way of coordinating large-scale efforts. In non-agile organizations, inter-team coordination often emerges within product divisions through specific operations management and processes. Over time, most organizations develop effective solutions to handle recurring coordination tasks for fixed constellations of teams.

As organizations begin to systematically coordinate the value creation of multiple teams in this manner, the focus shifts from optimizing individual team performance to enabling recurring collaboration between teams. But this is not sufficient in a dynamic environment, where new challenges and opportunities emerge all the time. Such environments call for a more fluid structure of teams: teams need to increasingly work in dynamic constellations, not fixed ones. Coordination therefore has to extend beyond the existing constellations of teams. It must operate at the scale of the organization, establishing the conditions under which teams can connect and collaborate, regardless of prior relationships. This trans-team layer of coordination maintains coherence across the organization while allowing teams to recombine as needed. In doing so, it makes the organization more adaptable and effective.

Trans-Team Coordination

When constellations change and teams need to recombine quickly to respond to new customer needs, then coordination cannot rely on historical precedent or accumulated agreements. Teams then do not have the luxury to establish new collaboration contracts from scratch in a time-consuming process. Nor do they necessarily share the same assumptions about collaboration and alignment mechanisms. Each team may be accustomed to its own set of alignment rules, which do not easily transfer to new constellations. Establishing a new rule set for collaboration then recreates a familiar problem from intra-team coordination: who has the authority to define a new rule set for the new inter-team collaboration? If time is of the essence, leaving this to ad-hoc negotiations risks delaying or undermining the collaboration itself. This is where trans-team coordination becomes necessary. It establishes a shared and accepted basis for collaboration that applies independently of which teams are involved. The basic conditions of collaboration are shared standards that exist across the organization. These standards provide a common basis for teams to quickly connect and collaborate. In large corporations, these standards take the form of HR policies and governance practices. However, they are not designed for a dynamic environment. In a dynamic environment, these standards create barriers to collaboration because they rely on the very things that need to be overcome: assumptions of stability and fixed hierarchies for coordination.

To see how trans-team coordination operates at scale, consider Alibaba’s Singles’ Day, the world’s largest shopping event. Every year, in just 24 hours, Alibaba processes tens of billions of dollars in sales. But the real story is not the volume. It’s the way it happens. Behind the scenes, thousands of independent teams, from logistics, payments, product design, to customer service, are making live adjustments in real time. They’re responding to signals from the market as they happen, coordinating through shared data and platforms for instant cooperation. Singles’ Day has become one of the world’s most dynamic launchpads for new products. In the weeks leading up to the event, brands use Alibaba’s systems to design and test items specifically for that day. During the event itself, successful products can be rolled out and scaled rapidly. No pyramid structure could orchestrate that complexity. No alignment through control could respond that fast. It’s coherence through shared intent and live information. It works because every team knows what matters, sees the same context, and has the autonomy to act on it. They also have a common framework and mindset that facilitates connecting and collaborating in real time across thousands of dynamically forming teams.

That is what an entrepreneurial operating system looks like. One that senses, responds, launches, and learns at the speed of its environment. And that is what modern organizations might aim for when they restructure for the purpose of “decentralization.” The extent to which they can be successful depends primarily on the dynamic needs of their environment, and the interdependencies of structures they try to implement.

The three layers of coordination exist to manage these interdependencies so that the organization as a whole can create value in a dynamic environment, and evolve in sync with new challenges and opportunities. The three layers also differ in their temporal dynamics. Coordination within teams unfolds continuously around the value creation itself, coordination between teams occurs in recurring cycles of alignment, and trans-team coordination operates on longer time horizons, shaping the conditions under which the collaboration potential develops. The question, then, is how to make the three layers of coordination more effective without necessarily resorting to the old hierarchical structures.

The Coordination Challenge at Scale

A closer look at existing solutions to coordination reveals the two main dimensions of the challenge. First, the size and number of teams that are required to co-create value. Second, the stability of their inter-team cooperation needs. The number of teams largely depends on the complexity and size of their shared effort: you cannot build a spacecraft with a two-pizza team. The stability of their cooperation needs depends on the stability of demand for their product: your demand for food remains relatively stable, but your diet and the means of delivering it to you can change rapidly.

This means that for any collaboration challenge, the number of teams required for value creation and the stability of their coordination needs determine the required solution. Different combinations lead to fundamentally different coordination requirements:

Coordination requirements across scale and environmental dynamism
Figure 2. Coordination requirements across scale and environmental dynamism

When only a few teams are involved, coordination can remain largely local, even under changing conditions. As the number of teams increases, coordination must be structured more explicitly. In stable environments, this structuring can be achieved with traditional management, fixed reporting lines and procedures that align the different contributions in the value stream. In dynamic environments, however, coordination cannot rely on these fixed arrangements to continually create value. When customers or markets demand new solutions, then the different contributions and the ways to create value need to adapt fast. This is where agile methods and self-management frameworks show their strengths and also their limitations. They begin to fail when the number of teams required for value creation passes certain thresholds. It is in this context that hierarchy emerged as a historical solution to large-scale coordination.

When Hierarchy Becomes a Constraint

Hierarchies can effectively structure and scale coordination across large numbers of contributing teams. But they mostly do it through fixed relationships: predefined roles, reporting lines, and escalation paths. In stable environments, this works well. In dynamic environments, it becomes a constraint. The fixed coordinating structures cannot adapt fast enough to changing coordination needs. As this adaptation gap grows, slowly but surely, it increasingly disconnects the governing structure from the operational needs of teams, leading to bad coordination priorities and decision lag. The governing structure then becomes a burden, not the support and enabling function it should be. It is no longer able to effectively balance short- and long-term goals. The organizational structure becomes fragmented, competing for resources and aligning on half-hearted compromises that serve neither productivity nor innovation. Many organizations respond by removing hierarchical layers. But this does not remove the underlying coordination demand. It only removes the structures that previously contained it. The operational dependencies between teams remain. What disappears are the mechanisms that made them manageable. Coordination then shifts from structured roles into the informal space between teams, where it becomes a continuous distraction: negotiating conditions for support, alignment meetings, and the intransparent forming of brittle alliances to win internal, and often personalized, power struggles. Power that was once clearly allocated becomes diffused, and harder to invoke for the long-term viability of the organization. Paradoxically, as the number of “autonomous” teams increases, so do their internal coordination costs. This is the structural challenge at the heart of decentralization.

To summarize, organizations struggle when the autonomy of teams is not matched by equally well-designed coordination mechanisms. Decentralization removes the coordination mechanisms of hierarchy, but until a replacement is found, organizations lose the ability to effectively align the contributions of large numbers of teams.

Reducing Coordination Complexity

One way to solve this challenge without relying on hierarchy is to reduce the need for inter-team coordination altogether by organizing work into self-contained value streams, as exemplified by Buurtzorg, a pioneering Dutch home healthcare organization. Teams are structured so that each unit can deliver value to customers largely independently. Most coordination occurs within the team itself, while inter-team interaction needs remain limited to a small number of topics and occasions. This approach is particularly effective when the number of teams required for value creation is small and their coordination needs are relatively stable. At Buurtzorg, small teams care for their own group of patients. Care planning, scheduling, and treatment decisions are handled within the team itself. Although interaction with central services exists, the delivery of care does not depend on continuous coordination with other teams.

A related logic appears in organizations where value creation requires multiple teams, but the number of interactions between them is deliberately kept small. Here, coordination is not eliminated but contained. W. L. Gore & Associates, for example, deliberately limits the size of its organizational units. The company’s founder, Bill Gore, observed that once groups grew beyond roughly 150 to 200 people, interpersonal communication and shared ownership began to deteriorate. To preserve effective and fast coordination, the company caps the size of individual units and establishes new ones as soon as this threshold is reached. By constraining the number of interactions, the organization limits the growth of coordination complexity while keeping its highly effective social coordination mechanisms intact.

Both approaches reduce coordination complexity by limiting either the number of teams involved or the number of dependencies between them. Both are effective as long as coordination needs remain relatively stable and bounded.

Building Coordination Infrastructure

However, not all forms of value creation can be organized this way. Many products and services require the coordinated contribution of multiple specialized teams, often under dynamically changing conditions. In these cases, coordination mechanisms need to continually evolve to fulfill their purpose, but without overburdening the operational system. This means that the total volume of coordination tasks rises, but overall coordination effort must not. Coordination efficiency needs a large boost. Only a few companies have mastered this challenge. They design their whole organizational system around ultra-efficient coordination infrastructure and highly adaptive value creation. This infrastructure defines how contributions connect: through shared interfaces, transparent commitments, and common standards for collaboration that apply across teams. Specialized tech and interaction protocols make dependencies visible, establish how value flows between teams, and act as the single source of truth for commitments, resources, and results. It allows coordination to scale across large numbers of teams and interactions. In the hyper-dynamic environment of the Chinese market, more such organizations can be found. The Chinese appliance manufacturing giant Haier provides another showcase of coordination infrastructure. Thousands of decentralized micro-enterprises operate as autonomous units responsible for specific customer solutions. A shared digital platform makes capabilities, resources, and contractual relationships visible across the organization, allowing micro-enterprises to discover partners and rapidly form collaborations as needed. Both inter-team and trans-team coordination are fully structured through shared mechanisms. In its recently announced five-year strategy for the coming years, Haier repeatedly calls for more improvements in coordination infrastructure, explicitly prioritizing cross-ecosystem collaboration, shared platforms and common standards as the foundation for autonomy and coherence. Not because Haier is lagging behind in this regard, quite the contrary: they know how much more they can gain through progress in this field.

Decentralization Does Not Eliminate Coordination

Behind the current trend towards decentralization lies the assumption that removing hierarchy and shifting to smaller autonomous teams will make organizations more adaptive or more productive. In doing so, they inadvertently also remove the mechanisms of efficient coordination. But large-scale decentralization does not eliminate the need for coordination. It shifts it. When hierarchical layers are removed, coordination has to move out of fixed roles and reporting lines and into the structures and rules that connect operational teams. The long-term viability of organizations requires that these new coordination mechanisms are made transparent, clearly governed and able to effectively balance short- and long-term needs. Without such coordination infrastructure, decentralization cannot succeed at scale and will likely be reversed. Embedding highly effective coordination routines, training and skill development into the interaction infrastructure of organizations makes large-scale decentralization viable. Without it, decentralization does not scale but collapses back into hierarchy.

 

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Trias Algorithmica: How Leaders Can Make Algorithmic Power Legitimate https://thinkers50.com/blog/trias-algorithmica-how-leaders-can-make-algorithmic-power-legitimate/ Wed, 07 Oct 2026 08:00:44 +0000 https://thinkers50.com/?p=90362 by Hamilton Mann

As algorithms move from advising organizations to acting through them, leaders need to separate algorithmic powers. No single system should be able to concentrate the power to set the rules, execute them, and judge its own results. Independent counterpower capable of constraining the authority delegated to algorithmic systems is a condition of the legitimate exercise of algorithmic power.

Algorithms Acquiring Power Are Not New

In 2019, Deanna Amato discovered that the Australian government believed she owed it thousands of dollars when it seized her tax refund.

The debt had been generated through what became known as Robodebt, an automated welfare-compliance program that compared tax-office income data with social-security records. Rather than establishing what recipients had actually earned during individual two-week benefit periods, the system could average annual income across those periods and use that figure to calculate an alleged overpayment.

For Amato, the assumption was wrong. The resulting debt was nevertheless treated as real. The Federal Court declared the demand for payment invalid.

Once software can encode rules, operationalize them at scale, and produce conclusions that institutions treat as authoritative, the relevant question is no longer simply whether the algorithm is accurate. It is how much power has accumulated around it and who possesses the power to contradict it.

What’s New Is Algorithmic Power Becoming Normalized Authority

Fast-forward. In May, Senbin Hou walked into an Immigration and Customs Enforcement office near Seattle for a scheduled check-in. He left in custody.

Hou, a Chinese national whose immigration proceedings were still pending, had previously been released and enrolled in ICE’s Alternatives to Detention program. Part of that program required him to use a mobile application to complete biometric check-ins through facial recognition.

According to records maintained by ICE’s contractor, Hou had failed to complete 15 of those check-ins. Hou told a different story. He said that technical problems with the application’s facial-recognition function repeatedly prevented him from completing them, and that each time this happened, he contacted ICE to report the problem.

The system recorded noncompliance. Hou described technology failure. ICE acted on the former.

After what it described as a routine review of his record, the agency revoked Hou’s release and arrested him when he appeared voluntarily for an in-person appointment. For weeks, a database entry had become a deprivation of liberty.

In July 2026, a federal judge ordered Hou’s immediate release. The court found that his detention violated due process and emphasized the substantial risk of erroneous deprivation when the government could act on recorded biometric failures without first allowing the person affected to contest them.

Both Robodebt and the system governing Hou were algorithmic systems, but they represented very different generations of algorithmic technology. Yet while the technological sophistication had changed considerably, the governance structure around how algorithmic authority was exercised had not.

In both cases, a rule defined what counted as compliance; an algorithmic system translated messy real-world circumstances into a standardized administrative determination; that determination could trigger state action; and the individual subject to it lacked a meaningful opportunity to challenge the system-generated premise before serious consequences followed.

In effect, this concentrates algorithmic power across the chain from rule-making to execution and judgment.

Algorithmic Power Is Scaling Faster Than Counterpower

If we think this concentration of algorithmic power can have life-altering consequences only in the public sector, we are wrong. The private sector may demand even greater attention.

A 2026 Grant Thornton survey of 950 senior executives from midmarket and large organizations across ten industries, including banking, construction and real estate, technology and telecommunications, insurance, and energy, all sectors in which algorithmic power can materially affect people’s lives, found that 74% of organizations were already giving agentic AI access to their data and business processes. Five percent of organizations were already allowing AI agents to execute high-stakes decisions without human review. Yet 78% of senior executives lacked strong confidence that their organizations could pass an independent AI-governance audit within 90 days, and only 20% had a tested AI incident-response plan for an AI failure.

What those numbers imply when combined is striking. If 74% of organizations are giving AI agents access to data and business processes, while 80% lack a tested incident-response plan, then even under the most favorable possible distribution, and allowing for rounding in the reported percentages, at least about 53% of respondents must fall into both categories, potentially rising to about 74%. In a sample of 950 senior executives, that represents roughly 504 to 703 respondents whose organizations give AI agents access to data and business processes while lacking a tested failure-response plan.

Apply the same arithmetic to governance readiness: with 74% granting agentic access and 78% lacking strong confidence that they could pass an independent AI-governance audit, and similarly taking the reported percentages conservatively, at least about 51% of respondents must again fall into both categories, potentially rising to about 74%. In a sample of 950 senior executives, that represents roughly 486 to 703 respondents. These are not correlations inferred from the data, but the range of overlap mathematically implied by the reported percentages.

And the 5% permitting AI agents to make high-stakes decisions autonomously should not be dismissed as a small number. Applied to a sample of 950 senior executives, that represents roughly 48 respondents whose organizations allow algorithmic decisions to acquire a significant degree of operational finality. The greater that finality, the greater the risk that algorithmic power becomes difficult, costly, or even impossible to reverse. In the high-stakes contexts represented across these industries, that can mean decisions affecting whether someone receives credit, gains access to housing, retains access to a digital or communications service, qualifies for insurance, or receives or can afford energy.

The picture is clear.

The governance gap lies in the mismatch between the power organizations delegate to algorithmic systems and the governance infrastructure available to constrain that power.

The direction of travel is clear.

The central challenge is to govern the power acquired by algorithmic outputs, which should be inseparable from ensuring that meaningful counterpower exists before those outputs become power exercised over people’s lives.

The Powers of Algorithm We Mistake for Technical Functions

Every consequential algorithmic system sits within three functional forms of power.

The first is algorithmic legislative power: the ability to encode the rules. A threshold, objective function, ranking criterion, or system prompt may look technical. But these choices determine how attention, suspicion, opportunity, workload, capital, and risk are distributed. A corporate rule does not cease to be a rule because it is expressed as a parameter.

The second is algorithmic executive power: the authority to translate rules into action. Whether an algorithm recommends an intervention or is permitted to execute it directly by freezing an account, changing a price, advancing or rejecting a candidate, committing a payment, or triggering another operational process, it is the operational arm of algorithmic power. The critical resource is not computing power. It is permission.

The third is algorithmic judicial power: the authority surrounding the determination that an output, action, or person satisfies the relevant rules. It determines whether a fraud alert was justified, reviews rejected applicants, decides whether a model failure warrants suspension, hears appeals, and ultimately determines whether a prior decision is genuinely reconsidered or rubber-stamped.

Fused in algorithmic systems, these three powers can produce absolute authority over both people and the organizations that deploy them.

At a time where we are delegating even greater authority to algorithmic systems whose capabilities are becoming more sophisticated at a pace measured in weeks, extending the reach of concentrated algorithmic power into an ever-wider range of decisions, governing that power is not an option.

If history has taught us anything, it is that the separation of powers and the establishment of countervailing power are instrumental in preserving rights of appeal, protecting human agency, and ensuring the legitimate exercise of power.

Oversight Should Not Be Confused With Algorithmic Counterpower

Corporate governance already understands the principle of counterpower through separation of power.

Traders do not unilaterally set their own risk limits. Companies do not prepare their financial statements and independently audit them. Credit officers operate within authorities established elsewhere.

Yet, the algorithmic concentration of power often takes root in organizational concentration of power.

This occurs, for example, when a technical team defines an algorithmic system’s purpose, builds it, deploys it, evaluates its performance, interprets its failures, and decides whether its outputs should stand; in effect, cumulating functions analogous to policy-setting, risk management, safety, compliance, validation, incident review, and appeal can accumulate within the same chain of command.

Governance failures do not remain outside the technology; they compound within the algorithmic system itself. The organization concentrates authority over the system, while the system concentrates the algorithmic functions inadvertently delegated to it, through which its power is ultimately exercised.

Separation of algorithmic powers is therefore not simply a matter of inserting a human somewhere in the process or assigning AI oversight to another committee. It requires identifying where power has accumulated, organizationally and technically, ensuring that no actor controls the entire chain from rule-making to execution to judgment, while also subjecting each exercise of authority within that chain to productive friction and independent counterpower.

That means separating goal-setting from model building; data stewardship from model optimization; optimization from the limits within which optimization may occur; system design from deployment authorization; product success from independent monitoring; automated decisions from meaningful appeal; and technical capacity to scale from authority to create enterprise-wide exposure.

And the greater the power delegated to an algorithm, the stronger the counterpower organized around it must be.

The Leader Test For Governing Algorithmic Power

Boards and executive meetings do not need to become AI engineering committees. But they need to acknowledge that algorithmic systems do not simply enter the technology stack; they enter the organization’s authority stack. Leaders must therefore keep sight of how power is redistributed, including the authority granted to the algorithmic systems they hire. They must build their map on how algorithmic power reconfigures authority across the organization.
Three questions help provide one.

  • What power is actually being delegated to the algorithmic system, and how far does that authority extend within the governance of the firm or institution?
  • How is that power distributed around the system, and what prevents rule-setting, execution, and judgment from collapsing into the same hands?
  • What independent counterpower can challenge, constrain, or stop algorithmic power when it exceeds its mandate or causes, or risks causing, harm?

That is the premise of Trias Algorithmica.

Ultimately, governing algorithmic power means acknowledging the social necessity of continually revisiting a simple question: what makes the power it exercises legitimate?

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Lifetime Achievement Award 2026: Marshall Goldsmith https://thinkers50.com/blog/lifetime-achievement-award-2026-marshall-goldsmith/ Tue, 29 Sep 2026 07:45:17 +0000 https://thinkers50.com/?p=90100 Thinkers50 has announced Marshall Goldsmith as the recipient of the prestigious Lifetime Achievement Award, to be presented at the Thinkers50 London Summit & Leaders50 Awards Gala in November.

The Lifetime Achievement Award is given to someone who has had a long-term impact on the way people think about and practice management. 

A pioneer in the field of business education, Marshall is the global authority in helping successful leaders achieve positive, lasting change in behaviour: for themselves, their people and their teams.  He is the creator of the Stakeholder-Centered Coaching method and the inspiration behind the 100 Coaches global network and has authored or edited over 60 books including four New York Times and Wall Street Journal bestsellers:

  • What Got You Here Won’t Get You There (with Mark Reiter, Grand Central Publishing, 2007)
  • MOJO: How To Get It, How To Keep It, How To Get It Back If You Lose It (with Mark Reiter, Grand Central Publishing, 2010)
  • Triggers (with Mark Reiter, Crown Currency, 2015)
  • The Earned Life (with Mark Reiter, Crown Currency, 2022)

“Hyperactive and relentlessly positive, Marshall Goldsmith’s reputation is based on results. His contribution to leadership excellence is second-to-none.”– Des Dearlove, Thinkers50 co-founder.

“Marshall’s impact can be seen in the hundreds of leaders he has mentored, and the thousands they in turn lead. At the heart of his approach is a deep understanding of the human side of leadership. His own humility and openness encourage others to recognise both their extraordinary strengths as well as their vulnerabilities – and, importantly, to ask for help.” – Sanyin Siang, Executive Director of the Coach K Center on Leadership & Ethics (COLE) at Duke’s Fuqua School of Business; Thinkers50 Coaching & Mentoring Award 2019, Coaching Legends 2023, Thinkers50 Ranking.

Marshall featured in the Thinkers50 Ranking between 2007 and 2017 and was inducted into the Thinkers50 Hall of Fame in 2018, upon which the Marshall Goldsmith Distinguished Achievement Award for Coaching & Mentoring was created. He will be presented with the Thinkers50 Lifetime Achievement Award at an exclusive reception at London’s Guildhall on Sunday 1 November, the evening prior to the Thinkers50 London Summit & Leaders50 Awards Gala. Previous recipients of the Lifetime Achievement Award include Lynda Gratton, Rosabeth Moss Kanter, Tom Peters, and Charles Handy.

Celebrating 25 years of Thinkers50, this year’s London Summit brings together the world’s foremost management thinkers and business leaders to challenge assumptions, spark fresh ideas, and shape the future of leadership. Discussions and keynotes will follow three key themes at the heart of management and leadership: purpose, technology, and talent. Networking breaks will facilitate the all-important encounters and conversations over coffee, lunch, or a glass of wine. 

Speakers at this year’s London Summit include Liz Wiseman, author of Multipliers; Jesper Brodin, former CEO of IKEA; Rahaf Harfoush, author of Hustle & Float; Erin Meyer, author of The Culture Map; Ndidi Okonkwo Nwuneli, executive chair, Sahel Group and co-founder, Wealth4Impact; and Kevin Nolan, President/CEO of GE Appliances. VIEW FULL LINEUP

We hope you can join us in London!

Thinkers50 London Summit & Leaders50 Awards Gala 2026

Tickets for the London Summit are upon application, via the link below. 

APPLY FOR LONDON SUMMIT TICKETS

London Summit & Leaders50 Awards Gala tickets (including Gala Dinner and presentation of the Leaders50 Distinguished Achievement Awards) may be requested by email to events@thinkers50.com – note availability is limited.

 

Notable Marshall Goldsmith Content

What Got You Here Won't Get You There by Marshall Goldsmith and Mark Reiter

What Got You Here Won’t Get You There (with Mark Reiter, 2007)

Triggers book cover by Marshall Goldsmith and Mark Reiter

Triggers (with Mark Reiter, 2015)

How Women Rise by Sally Hegelson and Marshall Goldsmith

How Women Rise (with Sally Helgesen, 2018)

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World’s Top Management Thinkers Name 50 Exceptional Leaders for 2026 https://thinkers50.com/blog/worlds-top-management-thinkers-name-50-exceptional-leaders-for-2026/ Mon, 14 Sep 2026 10:45:39 +0000 https://thinkers50.com/?p=89907 Leaders50 combines recognised business titans with hidden gems, including DEWESoft CEO Jure Knez, Chatterbox founder Mursal Hedayat, and former Build-a-Bear CEO, Sharon Price John

LONDON 14 SEPTEMBER 2026 – Thinkers50 today unveils its Leaders50 2026, a biennial list of 50 exceptional leaders from around the world.

What makes Leaders50 unique is who chooses it. Nominations come exclusively from Thinkers50 Ranked Thinkers and recipients of the Thinkers50 Distinguished Achievement Awards – the experts, scholars and authors who study leadership for a living and help define contemporary management thinking. Rather than measuring company size, revenue or media profile, they nominate leaders on the evidence of their impact, contribution, purpose, and commitment to developing others.

That collective perspective gives Leaders50 a depth that conventional rankings cannot replicate. It connects scholarship and practice, allowing nominators to recognise not only visible performance, but also the ideas, behaviours, and purposeful choices that are changing how organisations and teams are led – including work that may not yet command broad public attention.

This expert-led approach enables Leaders50 to recognise outstanding leadership wherever it occurs. The 2026 list includes internationally recognised figures, such as Satya Nadella, CEO of Microsoft, Jensen Huang, CEO of NVIDIA, and Leena Nair, CEO of Chanel, but it also brings important, less familiar stories to a global audience. Among these hidden gems is Jure Knez, founder of DEWESoft and Acrobat City, an entrepreneurial hub in Slovenia; former refugee Mursal Hedayat, founder of the UK-based social enterprise Chatterbox; and Sharon Price John, former CEO of Build-a-Bear and now CEO of children’s apparel company Carter’s. Their inclusion exemplifies the list’s ability to look beyond the most visible and obvious names and identify leaders that the world’s foremost management thinkers believe deserve wider attention.

“The challenges facing the world, organisations, and individuals are enormous. We do not know all the elements needed to tackle these challenges, but we do know that it will require exceptional leadership. Leaders50 shines a light on remarkable leaders who are making a real impact in the world, so that others may learn from them, be inspired by them, and take on the mantle of purposeful leadership.” – Des Dearlove, co-founder, Thinkers50

Those named to the Leaders50 will be honoured at the Thinkers50 London Summit & Leaders50 Awards Gala on 2 November 2026. The inaugural Leaders50 Distinguished Achievement Awards will also be announced on the night. Apply for accreditation.

2026 Leaders50

The full Leaders50 listing (in alphabetical order) is:

Tarang Amin
CEO, e.l.f. Beauty

Bill Anderson
CEO, Bayer

Ana Paula Assis
Senior VP and Chair for IBM Europe, Middle East, North Africa and Asia Pacific

Mary Barra
CEO, General Motors

Subha Barry
President, Seramount

Ed Bastian
CEO, Delta Airlines

Cécile Béliot
CEO, Bel Group

Olivier Blum
CEO, Schneider Electric

Jesper Brodin
Senior advisor to the IKEA Foundation; chair of The Earthshot Prize

Niels Christiansen
CEO, LEGO Group

Ken Denman
Managing Director, Sway Ventures

Saori Dubourg
CEO, Greiner AG

Aicha Evans
CEO, Zoox

Margo Georgiadis
CEO-partner, Flagship Pioneering

Mursal Hedayat
CEO and founder, Chatterbox

Jensen Huang
Co-founder, President and CEO, Nvidia

Merete Hverven
CEO, Visma

Andrea Illy
Chair, illycaffè

Sharon Price John
President and CEO, Carters

Francine Katsoudas
Executive Vice President and Chief People, Policy & Purpose Officer, Cisco

Ken Keller
Chair and CEO, Daiichi Sankyo, Inc.

Jure Knez
President and co-founder, DEWESoft

Li Bin
Founder, Chairman, and CEO, NIO

Mike Mahoney
Chair and CEO, Boston Scientific

Raja Al Mazrouei
CEO, Etihad Credit Insurance

Frédéric Mazzella
Founder of BlaBlaCar and Dift

Kenneth McGrath
CEO, Lidl

Doug McMillon
Former President and CEO, Walmart

Florent Menegaux
CEO, Michelin Group

Michael Miebach
CEO, Mastercard

Francesca Moriani
CEO, Var Group

Satya Nadella
CEO, Microsoft

Leena Nair
CEO, Chanel

Vas Narasimhan
CEO, Novartis

Kevin Nolan
President and CEO, GE Appliances 

Jacqueline Novogratz
Founder and CEO, Acumen

Ndidi Okonkwo Nwuneli
Executive Chair, Sahel Group; co-founder, Wealth4Impact

Miranda Qu
President and co-founder, Xiaohongshu

Cindy Rose
CEO, WPP

Leigh-Ann Russell
CTO and global head of engineering, BNY

Etienne Salborn
Founder, Social Innovation Academy

Dilhan Pillay Sandrasegara
CEO, Temasek

Ted Sarandos
Co-CEO, Netflix

Jim Hagemann Snabe
Chairman, Siemens AG

John Stankey
Chair and CEO, AT&T

Nick Studer
President and CEO, Marsh Risk

Lisa Su
Chair and CEO, AMD

Jessica Tan
Executive Vice-President and President, Sun Life Canada

Warner Thomas
CEO, Sutter Health

Suresh Venkatarayalu
SVP and CTO, Honeywell; President of Honeywell Connected Enterprise

VIEW THE 2026 LEADERS50

 

ABOUT THINKERS50

Founded in 2001 by business journalists Des Dearlove and Stuart Crainer, Thinkers50 began as the first global Ranking of Management Thinkers. Over the past quarter century, it has grown into a worldwide platform and a vibrant Community. Today, Thinkers50 encompasses the Thinkers50 Distinguished Achievement Awards alongside the Hall of Fame, Radar, Coaching Legends, Leaders50, Coaches50, and curated Booklists. The Thinkers50 London Summit & Awards Gala has been described by the Financial Times as the “Oscars of management thinking.”

 

MEDIA CONTACT

Visit Thinkers50 Press Room

To get in touch please email: enquiries@thinkers50.com
thinkers50.com

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The Half-Life of Board Expertise https://thinkers50.com/blog/the-half-life-of-board-expertise/ Thu, 03 Sep 2026 07:45:25 +0000 https://thinkers50.com/?p=89658 by Marianna Zangrillo and Thomas Keil

For decades, boards have treated experience as a competitive advantage. The underlying assumption has been simple: the more seasoned the directors, the better the board’s judgement. But what if that assumption no longer holds? In today’s business environment, knowledge has a much shorter shelf life than it once did. Artificial intelligence, geopolitical shifts, new regulation and rapidly evolving business models mean that expertise acquired over a successful career can become outdated surprisingly quickly. Experience still matters, but unlike financial capital, its value is no longer steadily compounding. In many areas it is quietly depreciating, as the world changes faster than even the most accomplished directors can keep pace with. Rod Adkins, board chair at Avnet and board member at United Parcel Service (UPS), PayPal, and W. W. Grainger summarised this idea:

“Going forward, boards must think about a dynamic versus a static model. In the past, board members may have been on the board for as much as twenty or thirty years… High‑performance boards will always have to focus on the types of skills, backgrounds, and experiences of the directors needed at any time. It must be consistent with the pace of change in the marketplace.”

We call this the half-life of board expertise: the time it takes for the strategic relevance of a board’s collective knowledge to diminish. The implication for how boards should be staffed are significant. Adapting to a shorter half-life of expertise does not simply mean refreshing boards more often. It means rethinking the role that different directors play. Some provide continuity and institutional memory. Others contribute specialist expertise that is particularly valuable during specific phases of a company’s development.

Balancing stability with renewal

The challenge is therefore not to choose between stability and renewal, but to combine them deliberately. Boards still need directors who understand the company’s history, culture and strategic context. That perspective can only be built over time and remains essential for effective oversight. At the same time, boards increasingly need directors who bring fresh perspectives and deep expertise in emerging technologies, new business models or geopolitical developments. The future belongs neither to static boards nor to constantly changing ones, but to boards designed to balance institutional memory with intellectual renewal. The balance between stability and change does not mean regular replacement of all the board’s members. Instead, boards should maintain a stable core deliberately creating room for directors whose contribution is tied to a particular strategic challenge. Not every board seat needs to serve the same purpose. Some directors provide continuity. Others contribute expertise that is valuable precisely because it is time-bound. Christian Trümpler of Partners Group described how this works in practice:

“In one of our investments, we knew that we had to reposition the brand within the first two years because although it was successful in Europe, it was not known in the US. So, rebranding was important. We needed an American with brand experience on the board to support this project because we had to reposition it globally. But from the outset, we also knew that the best person to help us with rebranding would not be the same person who would continuously drive our go‑to‑market efforts in the US. We communicated this to the board members right from the start: we need one person on this board to help us rebrand, then in two years’ time, we will need somebody else to get us into Walmart and Target. That was the plan from the beginning, and it’s now being executed one‑to‑one without any bad blood because it was made clear to everyone from the start.”

Curiosity as a governance capability

The priority is no longer simply to recruit accomplished directors. It is to ensure that the board’s collective expertise evolves as quickly as the environment it is expected to govern. This changes the questions boards should ask themselves. Rather than focusing only on whether they have the right mix of skills and experience, they should also ask how quickly they update their assumptions, expose themselves to new ideas and recognise when long-held beliefs no longer fit reality. As Debbie Hewitt, chair of England’s Football Association put it: “For board development to work, you need that the board openly accepts it does not know everything, that it is curious about finding out different ways of learning, and that it is open to acknowledging where the weaknesses might be.” In a world where the half-life of expertise is shrinking, curiosity becomes as important a governance capability as experience itself. The boards that remain effective will not be those that assume they already have the answers, but those that deliberately seek out new perspectives and continually renew their collective understanding of the world around them.

The most effective boards of the future may not be those with the deepest reservoir of experience, but those that recognise when experience is no longer enough. As the half-life of board expertise continues to shrink, the real competitive advantage will lie in a board’s ability to renew its collective knowledge while preserving the judgement that comes from understanding the business. The next frontier in governance is therefore not simply board refreshment, but designing boards that evolve as deliberately as the companies they oversee.

 

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The Road to Theory R. https://thinkers50.com/blog/the-road-to-theory-r/ Mon, 31 Aug 2026 07:45:29 +0000 https://thinkers50.com/?p=89581 by Stuart Crainer

In 2015 Thinkers50 presented an award for putting ideas into practice to Zhang Ruimin, then the CEO of the Chinese white goods company, the Haier Group. The work of Haier first came to our attention in 2005 when it deconstructed itself into a series of self-managing business units. As part of this, Haier established the inverted-pyramid organisation, a way of putting users first and reorganising the structure of the company.

Then in 2012 Haier effectively eliminated 12,000 middle manager roles and transformed the company’s original pyramid into a flat structure. The headlines screamed “Debureaucratisation! The smashing of middle management!” And for once they were entirely accurate. From Haier’s 80,000 employees, those with entrepreneurial willingness and capability were screened to form more than 4,000 autonomously operating micro-enterprises (MEs). The former middle managers had two choices: either seek opportunities elsewhere or become entrepreneurs within the Haier system, becoming ME owners or core members, serving as resource providers for the MEs.

Over the years since then we have witnessed at first hand Mr Zhang’s relentless quest to create an organisation truly fit for the twenty-first century and his similarly relentless curiosity.

Haier came from near bankruptcy in a semiderelict factory to become a modern international corporation. In 2025 it achieved a record high 426.8 billion RMB ($63 billion) in global revenue and 32.2 billion RMB ($4.75 billion) in total profit. But the issues that Haier has encountered and approached in its unique way are truly universal. Inefficient and indifferent organisations, and unhappy people within them, exist throughout the world. And at the same time, wonderfully imaginative, progressive, and successful organisations flourish. 

The Haier philosophy was first articulated in 2005 and is called RenDanHeYi. This Mandarin word is a compelling distillation of what is important in organisations. In RenDanHeYi Ren refers to the value of employees, Dan refers to the value of users, and HeYi means that the value of employees is realized through the value they create for users. The fusing of the creativity of employees and the needs of users lies at the heart of RenDanHeYi. In the language of Haier, the objective must be to continually insist on Zero Distance between employees and users, to continuously iterate and upgrade that relationship.

The spirit of continual improvement and iteration lies at the heart of Zhang Ruimin’s worldview. He is now the company’s chairman emeritus and remains intent on continually pushing the ideas and practice forward. We have spent the last few months working on a book together which combines the insights and experience of Haier with other inspiring examples of progressive management in organisations worldwide.

Working together we have sought to truly understand how ideas and best practices fit together. The result is Theory R: Rethinking How People and Organizations Work which will be published by Amplify later in 2026. The concept of Theory R takes the ideas and practices of RenDanHeYi as its foundation—as well as its first letter. Theory R is a distillation and translation of the original philosophy, intended to facilitate understanding and the application of the ideas in practice globally. It reflects the encouraging reality that organisations worldwide are taking these ideas and using them to reconfigure their own contexts.

Theory R aims to enable organisations to move toward a state of self-organisation, with autonomous individuals and lifelong users in an infinite loop of connection and collaboration to fuel constant change, learning, refinement, and performance. More pithily, Theory R is focused on how we can maximise human value in organisations.

And while we label it a theory and think of it as a philosophy, Theory R is practical. It is working today throughout the world. 

The primary distinction between Theory R and classical management models lies in their ultimate purpose. The classical model is material centric, reflecting what may be called a product-based economic paradigm. This paradigm pursues the appreciation of capital and wealth, benefiting only shareholders. Because capital belongs to shareholders, the model is inherently oriented toward shareholder value. The flaw is clear: shareholders can only share value, not create it. This explains why the classical model lacks an intrinsic engine for sustainable growth.

Though both are business models, Theory R and classical approaches differ radically in their development philosophies. We cannot comprehend Theory R through the lens of classical thinking, just as we cannot understand mobile phones through the logic of landlines. They represent two fundamentally distinct concepts.

Theory R is people centric, seeking the appreciation of human value—and that necessarily depends on human awakening or, more precisely, on self-enlightenment. By dismantling bureaucracy, Theory R enables every individual to move from being a mere executor to being a self-driven entrepreneur—an act of self-enlightenment. 

Stuart Crainer is cofounder of Thinkers50.

Theory R Book Cover

For two decades, Chinese white goods giant Haier has been rethinking the fundamentals of how organisations work – dismantling bureaucracy, replacing hierarchy with thousands of autonomous micro-enterprises, and putting employees directly in touch with the users they serve.

Now Haier chairman emeritus Zhang Ruimin and Thinkers50 co-founder Stuart Crainer are taking those ideas a step further. Their forthcoming book, Theory R: Rethinking How People and Organizations Work, published by Amplify later this year, distils the philosophy and practice of Haier’s RenDanHeYi model into a new framework for organisations everywhere – one centred not on maximising shareholder value, but human value.

 

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Thinkers50 Announces New Coaches50 https://thinkers50.com/blog/thinkers50-announces-new-coaches50/ Mon, 17 Aug 2026 10:45:09 +0000 https://thinkers50.com/?p=89265 LONDON 17 AUGUST – Thinkers50, the global authority on management thinking, has unveiled its 2026 Coaches50, supported by Coaching.com. 

Announced every two years, the Coaches50 list identifies and celebrates 50 executive coaches who are making a significant contribution to leadership, management, and organisations across the world.

“We see the coaching community as playing a vital part in our ecosystem and having a big impact on thought leadership,” explains Thinkers50 co-founder, Des Dearlove.  

“As well as being originators of ideas, coaches are the honeybees of best practice: cross pollinating the world of practitioners with the freshest and best ideas wherever they come from.”

“Coaches50 celebrates some of the people doing the most thoughtful and consequential work in coaching today,” adds Charlotte Jordan, CEO of Coaching.com.

“They are not simply contributing ideas; they are challenging the profession to think differently, raising the quality of practice and expanding what coaching can make possible. We’re incredibly proud to support Thinkers50 in recognising their contribution.”

2026 Coaches50

In alphabetical order, the 2026 Coaches50 are:

  1. Jennifer Garvey Berger
  2. Ann Betz & Ursula Pottinga
  3. Robert Biswas-Diener
  4. Peter Bregman
  5. Valorie Burton
  6. Ron Carucci
  7. Shirzad Chamine
  8. Peter Chee
  9. Nihar Chhaya
  10. Alisa Cohn
  11. Gena Cox
  12. Bruna De Palo
  13. David Drake
  14. Maja Djikic
  15. Scott Eblin
  16. Chester Elton
  17. Tasha Eurich
  18. Hortense le Gentil
  19. David Goldsmith
  20. Adrian Gostick
  21. Ruth Gotian & Andy Lopata
  22. Janet M. Harvey
  23. Peter Hawkins
  24. Jesse Hsu
  25. Jeffrey Hull & Margaret Moore 
  26. Amy Jen Su
  27. Shweta Jhajharia
  28. Atchara Juicharern 
  29. Ben Koh
  30. David Lancefield
  31. Jacquelyn Lane & Scott Osman
  32. John Mattone
  33. Sharon Melnick
  34. Julia Milner
  35. Haesun Moon
  36. Reeta Nathwani
  37. David Noble
  38. Lisa Orbé-Austin
  39. Divya Parekh
  40. Jonathan Reitz
  41. Marcia Reynolds
  42. Neha Sangwan 
  43. Liz Simpson
  44. Tracy Sinclair
  45. Nicky Terblanche
  46. Bonita Thompson
  47. Brian O. Underhill
  48. Hitendra Wadhwa
  49. Caroline Webb
  50. Andrew White

Members of the Coaches50 are selected according to three key levels of criteria:

  • experience and professional reputation
  • influence and impact
  • activity and engagement

Within these three levels are considered key factors such as client impact, peer recognition, media presence, published content, contribution to the coaching profession, and engagement within the global coaching community. The coach’s qualifications, depth of experience, professional development, and expertise in a specific area of coaching are also taken into consideration.

MORE ABOUT COACHES50

 

ABOUT THINKERS50

Founded in 2001 by business journalists Des Dearlove and Stuart Crainer, Thinkers50 began as the first global Ranking of Management Thinkers. Over the past quarter century, it has grown into a worldwide platform and a vibrant Community. Today, Thinkers50 encompasses the Thinkers50 Distinguished Achievement Awards alongside the Hall of Fame, Radar, Coaching Legends, Leaders50, Coaches50, and curated Booklists. The Thinkers50 London Summit & Awards Gala has been described by the Financial Times as the “Oscars of management thinking.”  

 

MEDIA CONTACT

Visit Thinkers50 Press Room
To get in touch please email: enquiries@thinkers50.com
thinkers50.com

APPLY FOR ACCREDITATION

 

Thinkers50 London Summit & Leaders50 Awards Gala 2026

2 November 2026
Guildhall, London 

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Mike Wade: Why AI Is a Leadership Challenge, Not Just a Technology Challenge https://thinkers50.com/blog/mike-wade-why-ai-is-a-leadership-challenge-not-just-a-technology-challenge/ Tue, 11 Aug 2026 08:00:03 +0000 https://thinkers50.com/?p=89070 Digital transformation has been on the leadership agenda for years, but generative AI has raised the stakes dramatically. 

IMD Professor Mike Wade argues that the biggest challenge facing organisations is not adopting AI, but leading the cultural and organisational change that comes with it. In this interview he explains why so many transformation initiatives fail, how leaders can use AI to improve their own decision-making, and why organisations must focus on value creation rather than technology for technology’s sake. He also explores the ethical challenges surrounding AI and why resilience may prove just as important as agility in an increasingly volatile world.

Mike is Professor of Strategy and Digital at IMD and Director of the Global Center for Digital and AI Transformation. He is also the author of Digital Vortex, Hacking Digital, Alien Thinking, Gain: Demystifying GenAI for office and home, and Twin Transformation.

Watch the full interview or read the transcript below. 

This interview took place at IMD’s signature Orchestrating Winning Performance program in Lausanne, 2026.

Transcript

Des Dearlove:

Hello, I’m Des Dearlove, co-founder of Thinkers50. My guest today is Mike Wade. Mike is professor of strategy and digital at IMD and director of the Global Center for Digital and AI Transformation. A leading thinker on digital disruption and transformation, his books include Digital Vortex, an award-winning bestseller on disruptive competition, Hacking Digital, a practical guide to accelerating business transformation, and Alien Thinking on turning breakthrough ideas into reality. In 2025, Mike published Gain: Demystifying GenAI for office and home, exploring generative AI’s impact on personal and professional life. And in the same year, Twin Transformation, examining how AI and sustainability can be combined to create long-term competitive advantage. Mike, welcome.

Mike Wade:

Thank you. It’s a pleasure to be here, Des.

Des Dearlove:

You’ve been writing about digital disruption and transformation for many years.

Mike Wade:

Yes.

Des Dearlove:

What has changed most in the way leaders understand disruption and what have they still not really got to grips with?

Mike Wade:

I think when we wrote Digital Vortex 10 years ago now, disruption was still something that most executives thought happened to other people. And so they were still coming to terms with the idea of disruption, so we unpacked it in that book. I think today that’s no longer the case. I think disruption has become very much a factor of everyday life for executives so they’re more used to it. What hasn’t changed, I think, as we’re focused very much on the digital side and now AI, of course, is that still today, most executives and most organisations treat this as a technology challenge rather than what it really is, which is a change management challenge and often a cultural transformation as well.

Des Dearlove:

So I think you’ve said 87% of digital transformations actually fail, so why are the statistics so scary and so bad?

Mike Wade:

Yeah, we keep track of those statistics as many organisations do. We’ve given up trying to focus on a precise number, but it’s over 80% and it’s really hard. These transformations are really tough. The technology part is difficult, but even if you get the technology part right, you can still fail. Part of the issue is with expectations being unrealistically high. Part of it is that the organisational change doesn’t go as well as organisations like. Part of it is because in the rush to adopt the latest technology like AI today, organisations end up with a really fragmented mess of different things going on rather than what they should be doing, which is prioritising based on value creation.

Des Dearlove:

Now, I’ve been sort of analysing this sort of research for a long time. I mean, 80% of change management… We used to hear 80% of change management transformations didn’t work or failed. The number doesn’t seem… If anything, it’s getting worse, you’re putting it into the high 80s. Does the digital thing make it more precarious or is it… I mean, why aren’t we moving the needle on this?

Mike Wade:

Why are we not learning from past failures? And it is surprising to me constantly that organisations don’t learn. They do make the mistakes over and over again. I think what technology does is it accelerates the process. So the change, maybe you had a little bit more time in the past to adapt to a changing world and do what you need to do. That’s been very much compressed. And I think the stakes are higher because the opportunities are larger, but the threats are also larger, so the whole game is getting harder to play. And even if organisations are getting better at playing the game, because it’s getting harder to play, the success rate doesn’t necessarily improve.

Des Dearlove:

Obviously, everyone’s talking about AI and gen AI and what do leaders need to understand about it beyond the kind of the hype? What is it that they really need to get their heads around?

Mike Wade:

I think they need to get their heads around the fact that unlike other waves of technology disruption, this one is a multipurpose disruptor. It’s a general purpose. It has the ability to disrupt by itself, but also to disrupt many other areas, unlike let’s say blockchain, which can be disruptive within very specific niche areas, or 3D printing, or augmented reality. They can be disruptive but I think AI and particularly generative AI has this broad scale ability to shift and change the way things work and how organisations operate. And that’s new and it’s really important for leaders to get their heads around that.

Des Dearlove:

I think the other thing is that if you’re the CEO of an organisation, perhaps you understand that everybody else needs to change the way they work because this gen AI is going to make a big difference, but how does it actually… I mean, what are CEOs themselves doing? Because it can be like everybody else should be taking the medicine except me, I carry on doing what I was doing before, but how do the best CEOs that you’ve seen use AI in their everyday leadership role?

Mike Wade:

Yeah, I think you’re right. I think that the best CEOs, the best leaders are practitioners. They’re using it. They’re engaging with it. They’re not outsourcing it or delegating it to others. In fact, this week we’re running a stream here focused exactly on that. So what can executives get personally from these technologies? How can they use these technologies to become more efficient, to become more productive, to make better decisions, to get more time back into their week? There’s so many practical ways that generative AI and related technologies can help these executives do what they do better. And the ones that leverage that and explore that, I think are the ones that see the potential that then can be leveraged more in a bigger way throughout the organisation.

Des Dearlove:

So what sort of things are you talking about? Can you give me an example of how a CEO might deploy AI in their own life, in their own world, to perhaps give themselves more time, perhaps make better strategic decisions or be better at analysing data?

Mike Wade:

I think that the forward-looking leaders are using AI as a kind of a virtual chief of staff. So a chief of staff, there’s an organisational part of that. So helping to simplify the calendars, to prepare better for meetings, to organise the inboxes, things like that, but also to stress test decisions. So you can create agents now with certain personas and certain roles. What would a competitor think of this challenge or this decision I’m thinking of making? What would a customer feel? What would somebody who’s a finance executive or technology executive or somebody who’s an extreme in this area or that area? So I think these executives are essentially placing AI between them and all the things that they need to do and getting a little bit of additional insight to prepare them better for all the things they need to do on a daily basis.

Des Dearlove:

Can you see a situation where someone would have an AI board member?

Mike Wade:

There are some examples today of boards that are experimenting with AI board members. I think the time when you have an independent board member with fiduciary responsibility who’s an AI, that’s probably still a little bit into the future. But I do think that AI can help board members to be more effective by anticipating questions to ask management or that management may ask them, to summarise documents, to stress test decisions. Again, as I mentioned before, to play the role of different personas that could improve the discussion that boards have. So I think AI has a very, very strong role to play for boards. And yet, Des, it’s interesting, we’re doing some research now into this question, how boards are using AI. And there’s quite a strong sense within many boards today that if you use AI, you’re cheating, that boards are paying a lot for board members and for their brains and they shouldn’t be using AI because that’s not what they’re there for, which to me is a missed opportunity, not to replace a board member, but to augment what the board members do for boards and for organisations.

Des Dearlove:

I mean, this is bringing us nicely to another one of your sort of topics, which is AI and ethics, but I’m going to come to that. But I mean, I think you’re right. There is this sense almost that either you have AI in your life and you’re forward-looking or that there’s a kind of a pushback, that there’s an element of you’re cheating if you’re using AI. We seem to be hung up on this a little bit. How do we get through that particular kind of forest and then we’ll talk about the ethics of AI and how we make sure there’s some guardrails.

Mike Wade:

Yeah. I think there is a real thing, this AI shaming.

Des Dearlove:

AI shaming. Yeah.

Mike Wade:

We see it in organisations where people will say, “I don’t think it’s right that if an email is sent with my name on it that is written by AI, it’s just not ethically right.” Or they’ll say, “My kids are not allowed to use it in school. Why is it okay for me to use it at work? Don’t these things hallucinate all the time?” And I think a lot of that comes from just a lack of understanding of what the technology is, the strengths, the weaknesses, how it works. I think a lot of that can be addressed through training, helping people, executives or frontline workers, whoever, really understand what the technology is, how it works, how it’s different than traditional AI or other technologies, what are the benefits, what are the risks? And I think armed with that knowledge, they’re much better able to go out and use the technology in the most effective way.

Des Dearlove:

So let’s talk about guardrails, let’s talk about ethics. Where are we in your view in that whole debate? How are we going to ensure some of the nightmare scenarios that obviously are being rolled out all the time? How do we protect ourselves and how real are the risks, I guess?

Mike Wade:

How real are the risks is something that’s mostly down to a matter of opinions and when it comes to opinions, you have them all over the place. So it’s very, very difficult to separate the signal from the noise and there’s a lot of noise around this. This is actually something we looked at two years ago. We launched something we call the AI Safety Clock, which is based on the Doomsday Clock, which as you all know, was something set up after the Second World War to measure the likelihood of nuclear war. And so we did something similar for AI safety and for us, midnight, so it goes from 11:00 to midnight, is uncontrolled AGI. So AI that is as intelligent as we are generally, which is out of our control. And we tried to take a very, very fact-based approach. So collecting a lot of data, looking through the legislation, looking through the advances in the technology, monitoring things like agentic AI, which basically gives AI more autonomy, physical AI and all the rest of it.

And when we launched the clock a little over two years ago, we placed it at 31 minutes after 11:00, so 29 minutes to midnight. So going from kind of medium to getting into that higher risk, since then we’ve moved it 11 minutes closer to midnight. So we actually see the risk going up and unfortunately the checks and balances that you would normally have with a very powerful technology are not very advanced. In fact, most countries, when they’re enacting legislation around AI, many times they’re taking away checks and balances as safeguards because they want to compete.

Des Dearlove:

Yeah. No, I think this is one of the great challenges, that technology moves much faster than the speed of legislation.

Mike Wade:

Yes.

Des Dearlove:

And particularly with AI, there is this sense that if government gets in the way, if regulations get in the way, we’re going to fall behind to China or whatever. So it’s a real conundrum because on the one hand, we’re racing as fast as we can go in order to be the ones out front, but at the same time, we don’t really know quite what we’re heading towards.

Mike Wade:

Yeah, that’s right. And I think the danger, as you point out, is that we’re advancing this technology faster than our ability to control it. And at some point we can’t put the horse back in the barn and so we should be, I think, thinking a little bit more than we are about the risks and our ability to control those risks. But as you say, it’s a geopolitical race and we’ve got the two big powers when it comes to AI, the US and China, taking different approaches. And then we have Europe and the approach of Europe, which often is the case, let’s put the protections in place first and then let’s see what we can do with the technology. The US saying, “Let’s just go. We’ll worry about the future later. We don’t want to constrain innovation.” And then China taking a somewhat kind of in the middle approach, let it happen and then when they’re ready, come in with legislation. So the results are that the US and China are far, far ahead today when it comes to AI.

Des Dearlove:

That’s the technology side. Let’s talk a bit about the human side. I mean, I think for some reason, AI has this different sort of persona in terms of I think we understood that technology is a tool and enabler, but something about AI has spooked us or some parts of humanity and there’s a fear that it’s going to push out what makes us human. How can leaders… Well, presumably it’s desirable to keep the humanity, let’s debate that first. But then secondly, how does one do that in a way that you’ve, one, that the company, the organisation remains competitive?

Mike Wade:

I think a lot of it comes down to communication and messaging. As we spoke earlier, digital transformation is very much about change management and AI driven transformation is the same, but with any change management, there’s resistance, always resistance. Many people don’t like to change. But the thing when it comes to AI, there’s-

Des Dearlove:

Sorry, many people are frightened of change.

Mike Wade:

They’re frightened of it, of course.

Des Dearlove:

I mean, that’s what… Because what does it mean for me? Often it’s the case of how do you create some reassurance or a sense that there is a future?

Mike Wade:

They’re worried about the future that they can’t control, they don’t understand. But when it comes to AI, there’s this extra element, this kind of existential, it’s not just resistance because they don’t like change. It’s resistance because they may not have a job. And this is kind of an extra layer of resistance that organisations have to overcome and how do they overcome it? They overcome it in the same way, we’ve always done good change management with solid communications, reinforcing of messages, making sure that people understand that it’s not there to replace them, it’s there to help them do better work than they’re doing today. So it’s not just about replacement, it’s about augmentation.

I think that’s a really, really strong message that organisations need to push and help train them because most people, they’re just really scratching the surface when it comes to AI. For most people today, it’s still a chatbot. That’s it. It’s just a chat window. And when they realise there’s so much more you can do than just type in a chat window, possibilities open up and they realise that they can do their jobs not only faster, which is fine, but often that’s taken by the company anyway, but doing it better, doing their jobs better. And then that helps to overcome some of that resistance.

Des Dearlove:

Where do you see the pockets of best practice? I mean, I know this is a very fast moving picture, but I mean, obviously the technology companies are, that’s what they do, but some of the more, perhaps the more traditional companies, do you see examples where they’re managing… Because there’s a tension. Do you deploy AI everywhere and invest squillions of dollars? Which is one way of doing it. Do you wait and see a little bit, but you don’t want to wait too long because the competitor will have leapt over you and be disappearing into the distance. So there’s a tension between, do we go now? Do we wait? But who’s doing this well? Who’s managing this process well?

Mike Wade:

So I think there are organisations that are doing this well. I think the best organisations are taking upskilling AI literacy and training seriously. So they’re equipping their people with the skills and knowledge to leverage the technology. I think the best organisations are avoiding exactly what you said, the temptation to just do a bunch of things, just run ahead all kinds of projects that individually may make sense, but as a portfolio make no sense at all and focusing instead on the value that can be created and being rigorous about that and then working backwards. Because whatever the value is created, maybe they want to increase the top line, maybe it’s about efficiency, maybe it’s about market share, maybe it’s about patient care, whatever it is, but being rigorous about that and then working back. And what they may find is that the best way to get there doesn’t have to be generative AI or agentic AI.

It could be something else. And what we see a lot of companies today, it’s like they have the gen AI or the agentic AI hammer in their hands and they’re looking around. Everything looks like a nail, so they’re using it in all kinds of places where it’s not optimal. They could probably do it cheaper, more accurately, with less impact on the environment using some other type of tool. It’s very important, AI, they have to use it, but moving, shifting the focus toward the value that can be created rather than just focusing on the technology itself. I mean, I think that’s what the forward-thinking organisations are doing very well today.

Des Dearlove:

Do you anticipate fallout? I mean, it feels like we’re in the AI bubble where people are… Organisations are throwing money at it. Do you see any signs that some air’s going to come out of the bubble at least?

Mike Wade:

I mean, it has to, right? It has to. There’s so much buzz around it. I mean, today as we’re recording this, we’re seeing a massive selloff in AI stocks now. Will that continue? It’s hard to say. I think when we look at the financial markets, the stock markets, there’s a disconnect between the underlying performance of these companies and the valuation of the companies. So I think there’s going to be probably a retreat there, but where there’s a lot of smoke, there is fire. So these technologies do have the ability to create value in so many different ways. So organisations that can look past the buzz and the hype and really zero in on the concrete use cases where they can show improvement, I think those are the ones, the rigorous prioritisation, and prioritisation is less about what you do, more about what you don’t do. They rigorously prioritise, I think they have the best chance of capturing value from AI today.

Des Dearlove:

And we have to be aware of our context. We’re sitting here in Switzerland, right in the centre of Europe. And as you indicated earlier, the US is charging ahead, the Chinese, charging ahead. Is there any hope for Europe catching up?

Mike Wade:

Europe has all the raw materials it needs. Europe has a smart people, has access to capital, has great universities, it has great companies, it has everything it needs. It’s just got to get its act together. And I think what we need is a really coordinated effort within Europe, which as we know, Des, has been difficult historically to achieve, but there are some examples, right? CERN in Geneva is a good example where for that type of fundamental research, Europe got together and created a place where best scientists come and all of this stuff, like a Silicon Valley.

After the first Gulf War when the US decided to restrict the GPS, Global Positioning System, I think a lot of European countries and companies thought, “Wow, we’re building our business on this technology.” And so Europe developed its own version of the GPS system. So I think there’s hope, but I mean, we need to move much more quickly, much more aggressively than we have done and make some tough choices, like there’s going to have to be a physical location somewhere in Europe that aggregates all this talent. Where’s it going to be? This is one of the-

Des Dearlove:

Our own cluster, our own Silicon Valley or equivalent.

Mike Wade:

We need that. Yes.

Des Dearlove:

Yeah. Okay. Final question. We – the Thinkers50 London Summit & Leaders50 Awards Gala – we’re launching the new Leaders50 list this year. So, 2nd of November, we’re going to have 700 of the leading management thinkers of the world mingling with the most inspiring business leaders, particularly to the leaders in the room, what would be your advice? What do they need to be aware of and what should they do when they go back to the office on the 3rd of November? What would your advice be to them? What do they need to do?

Mike Wade:

I think they need to be laser-focused on where the value is going to be created in the future and how they can capture that value with AI but not just with AI. I think they also, there’s a lot of talk about agility, rightly so. I mean, organisations need to be fast, but I think today there’s almost too much focus on speed and not enough focus on robustness because I don’t think the changes that happened, the disruptions, the threats we talked about earlier are going to slow down. There’s going to be more and more of those. And agile companies that can move quickly won’t be able to avoid them all. They’re going to take damage. They’re going to take hits so I think organisations that blend and match agility and speed with strength and robustness, I think are the ones that are going to survive and thrive in an increasingly unpredictable environment going forward.

Des Dearlove:

Mike Wade, thank you very much.

Mike Wade:

It’s my pleasure.

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