McKinsey Insights & Publications

‘Hold course and adapt as you go’: An interview with Manon van Beek

2026-10-26T16:00:00+00:00

As she prepares to step down, the CEO of leading European grid operator TenneT reflects on scaling energy infrastructure, navigating geopolitical shocks, and building resilience amid uncertainty.

What AI can’t replace—and why it matters for value

2026-10-26T16:00:00+00:00

AI may be deepening our crisis of connection, says Stanford research psychologist Jamil Zaki. Managing change with empathy helps leaders build trust, strengthen relationships, and get the most out of their AI investments.

The rise of the agentic coworker: A banking CIO’s guide to the next wave of AI

2026-10-25T16:00:00+00:00

AI agents can increasingly make decisions, act, and collaborate across workflows. Deploying them at scale requires new approaches to governance, architecture, and accountability.

Agentic AI in financial-crime prevention: Lessons from winners

2026-10-25T16:00:00+00:00

A year of delivery has confirmed much of what we expected about agentic AI in financial-crime prevention—and inspired five key questions that can help institutions scale.

Agentic AI in transaction monitoring: How banks can scale AI in compliance

2026-10-25T16:00:00+00:00

Applying AI to transaction monitoring shows how agents can transform compliance tasks and processes, turning cost centers into drivers of advantage.

Pfizer’s Alex Condoleon on medical affairs accountability

2026-10-25T16:00:00+00:00

In this episode of Eureka!, McKinsey’s podcast on innovation in life sciences, host Navraj Nagra and McKinsey partner Yigit Yorulmaz speak with Dr. Alex Condoleon, chief medical affairs officer for global medical engagement and impact at Pfizer. Condoleon, a physician executive who has led medical, innovation, and digital health efforts across Australia, China, Europe, and the United States, argues that medical affairs’ longstanding ceiling has lifted and that the function can now be a genuine strategic partner in life sciences. He discusses what it takes to bring the right evidence to the right clinicians at the right moment, how to measure medical affairs’ impact beyond commercial metrics, and what patient-first looks like at a global scale across a wide portfolio of disease areas. An edited version of their conversation follows.

A career built anticipating the industry’s next move

Navraj Nagra: You’ve had a distinctive journey into medical affairs, including leadership roles spanning medical, innovation, and digital healthcare. What were the pivotal moments that shaped your career direction, and what ultimately pulled you into this role at Pfizer?

Alex Condoleon: Looking back at the career trajectory I’ve been on, I’ve always believed that if you want to impact global health in a meaningful way, you need to get a step ahead of where the system is already going, not follow it. That has guided each of my career moves.

I started in the industry in Australia and moved to China. That wasn’t because I had clarity on what China would become; it was because I had a conviction that China would play a significant role in healthcare. Fast-forward to today, and China’s ambition is becoming clear in biotech and in AI, and that shift is going to impact global health.

From there, I moved to Europe, into my first dedicated role focused on consumer healthcare rather than the prescription business. That was me seeking to understand how patients were starting to take control of their health and what that could mean. In that role, almost a decade ago, I was seeing early direct-to-patient models. I remember meeting the team at what today we call Ro.com when they were in their infancy, and yet there was something about what they were building toward that I thought was going to matter. Fast-forward to today and we see platforms like Hims & Hers and Ro.com playing a dominant role in how people choose to access glucagon-like peptide-1 (GLP-1) drugs in the United States.

My next pivot was moving to the United States, focused on enterprise digital transformation in medical affairs, and later building out a digital healthcare franchise. Again, it’s a similar pattern: investing ahead of a change I believed was coming, so I could be ready for the moment. I think that moment is the one we’re living through now, a moment of convergence. Many global powers are influencing what the healthcare landscape looks like; digital and AI are rising in healthcare delivery; and all of that is pushing the trend of patient empowerment and patient-guided care. When you see that kind of seismic opportunity come together, it’s great to be in an organization like Pfizer, which seeks to do bold things, because being bold in healthcare is your way of staying relevant and achieving leadership.

I’ve been very fortunate to come into a position focused on the role of data, analytics, and AI in optimizing how we think about medicine, science, and value, and how we communicate it and bring it to the forefront. I’m hopeful those things make a meaningful difference to moving healthcare forward.

Why the ceiling on medical affairs is gone

Yigit Yorulmaz: Your remit in medical affairs involves overseeing a high-caliber global platform focused on medical engagement and impact. How does this, and your past experience, help you reimagine what medical affairs can look like? What excites you about building and leading a function that doesn’t exist at any scale in many medical affairs organizations?

Alex Condoleon: I’m excited by the role I get to do every day. Part of the reason is that I’d reached a point where I genuinely felt medical affairs had hit a ceiling. What energizes me today is that I’m more convinced than ever that ceiling is gone, and that medical affairs has the potential to be a true strategic partner in life sciences. We’re in the process, as a function, of stepping into that role, and I think now is the moment.

There are a few reasons for that belief. It starts with how healthcare systems, and the experience of healthcare professionals and patients engaging with the system, are fundamentally changing. In a world where the lives of healthcare professionals are chaotic and made more complex every day, the need for accurate, relevant, timely signals to guide how they manage patients is more important than ever. But it must be approached differently than in the past. That’s exciting, but it will also challenge us: Compared to a decade ago, we need to reimagine our role, reinvent how we bring value to our target customer groups, and constantly listen to insight and feedback that helps us improve.

I say that partly because, as an industry, we’re in a period of great productivity, with lots of new products coming to market. But there’s a paradox: Every time we launch a new product and solve an unmet need, we also unintentionally add complexity to an already complex treatment landscape. That pushes the importance of thinking about what information healthcare professionals and patients need, and when. That’s where there’s an exciting role for digital platforms to enhance and magnify what we aspire to do, enriched by the AI advances we’re seeing now.

So although we’re challenged to lead through a period where the pace of external change feels like it’s outstripping our internal capacity to absorb it, that’s fundamentally a world of opportunity for medical affairs teams. But to grab that opportunity, building the right data, analytics, and AI infrastructure is critical. We’re really good with data in medical affairs, good at making sense of incomplete and imprecise data. We just need different techniques, and to get comfortable with the learning curve.

If we can build those fundamentals into our capability mix, and lead in a way that resonates with teams, I think the way the external world and technology are advancing means something important for medical, and represents the pillars on which we’ll step up as a function.

Reaching physicians in a more complex landscape

Navraj Nagra: Just to add a further deep dive here: You mentioned the use of some of these technologies, these new ways of working, to better engage physicians and medical professionals. Can you give me an example of what you’re doing at the moment to help physicians think about this more complex landscape, and how you’re engaging with them differently compared to what was being done even ten or 20 years ago?

Alex Condoleon: That’s a really important question. Given the data now available, we can, with a good degree of precision, understand the clinical realities physicians are facing, including where there are gaps in care that represent challenges and opportunities for medical teams working with the patients they oversee. Once we understand that, we have a need state we can seek to solve.

We’ve built the data and analytics capabilities that give us precision on who’s facing a clinical challenge that aligns with where we think there’s room for improvement. At the same time, we’ve been able to be precise about the digital footprint of those healthcare professionals, which lets us rethink what content we need and how to make it relevant to the channels physicians are actually on. That’s half the equation.

What’s equally important is that there’s now maturity in the healthcare landscape, letting us partner with medically focused digital channels to deploy content precisely, in the right form and at the right time, for target physicians. By bringing together the right customer segment, the right need state, and content adapted to how information is digested on the right channels, and by partnering with these platforms, we’ve seen tangible improvement in care. That improvement doesn’t replace what our field medical teams do, it’s supplemental. It increases reach and magnifies what we’re already doing through impactful field conversations.

That’s something we couldn’t have fully believed two years ago would be possible, but it is and it works. It’s an example of taking the discipline of medical affairs and matching it to the need state of the healthcare professionals you’re trying to reach. Our traditional face-to-face work can now be magnified and elevated beyond that, and we’ll only see acceleration of it.

One portfolio, many specialized environments

Navraj Nagra: One thing we recognize is that there’s such a wide portfolio across Pfizer, from vaccines to primary care to specialty medicines. When you think about patient needs, physician needs, and the scientific conversations across these categories, how does that vary? What are the differences in maturity of the approaches you’ve discussed across those categories?

Alex Condoleon: That’s a fascinating question, and one of the things I’m really fortunate about in my role. Working across a portfolio like Pfizer’s, you start to see different elements of the future of healthcare emerge, in different ways, in different specialties. One day I’m hopeful they’ll converge.

Take oncology, or rare diseases, where Pfizer has a significant focus. We’re seeing the rapid rise of a precision, science-driven form of personalization. It was only a handful of decades ago that we defined cancers by their organ of origin, but today we have hundreds of subtypes based on histology, genetics, and biomarkers, and we can envisage further precision coming. We can take the same parallel with heart failure, which has been segmented as we understand more of the genetic contributors. So there’s precision emerging in some areas, and it will only grow.

Then at another end of the spectrum, look at vaccines, or an area like GLP-1s, where the dynamic is very different: You’re fundamentally engaging with the population and shaping patient choice and behavior. Patient literacy, ease of access, and convenience matter enormously to the choices people make. And then there’s primary care and specialty care, where we’re focused on understanding healthcare professional preferences, beliefs, practice patterns, and care gaps, so we can be as targeted and relevant as possible.

I call those out because one day those three thematics will converge into a great healthcare experience, where the science of precision spans all of healthcare, where people navigate their own healthcare guided by their choices, preferences, and literacy, and where the industry has learned to be as relevant and personalized as possible to the attitudes and behaviors of the healthcare professionals we engage with.

So the breadth of our portfolio is a landscape of learning, one I’m hopeful will lead to a convergence that brings more value to patients and healthcare professionals alike. Today you can’t treat the portfolio as one-size-fits-all, but you can treat it as several test-and-learn environments, where concepts proven in one area show us when others will benefit from the same trends.

What to standardize, what to keep local

Yigit Yorulmaz: Fascinating to hear you talk about that. It’s absolutely true that different parts of healthcare are evolving in different ways, but when the portfolio is so diverse, medical affairs can’t rely on a single playbook to determine what good looks like. So you have, as a leader, the choice of deciding what must be standardized—whether platform capabilities, principles, or how you measure things—and what must be bespoke: the content you use, how you’re thinking about channel mix. How do you think about that variation across therapeutic areas or geographies, based on your experience? What’s standardized, and what’s unique?

Alex Condoleon: There’s no easy answer to that. As we build new capabilities for medical affairs—especially data, analytics, and AI capabilities—scale matters. On the flip side, being relevant to different therapeutic areas or geographies also matters. Bridging that tension is a day-to-day exercise in judgment.

There are areas where standardization and scale matter most. How we structure and govern data is best done with discipline at an enterprise level. The same goes for the analytics platforms and new professional skills we’re bringing into teams; while we’re maturing them and working out what value looks like, there’s benefit to housing that learning centrally. The same is true for how we build AI models and partner with large language model (LLM) companies: At this early-learning stage, that fits into working out the foundational capabilities that represent the workforce of the future.

But none of that matters if we can’t make it relevant to the people we’re seeking to bring value to. Central capabilities can be quicker to build in transforming moments, but ultimately they need to sit as close to the customer as possible. That means working closely with the teams you’re partnering with, not being overprescriptive about the offering, and building understanding of a capability and a best practice while giving people the freedom to innovate beyond it. That’s how we keep capabilities relevant, especially to medical stakeholders.

We’re in build mode right now. We might have ideas of a target operating model, but the key word is target, not today’s state. Organizations aren’t static, they’re living things, and it’s when we let that chemistry play out that we innovate beyond what’s apparent today. So there’s a place, especially in this digitally accelerated phase, for building capabilities that don’t yet exist. But if we can’t equally let teams apply those capabilities with freedom to operate in their own context, we’ll lose value and struggle to translate healthcare know-how and tech know-how into medical affairs impact.

Governing AI and digital initiatives

Navraj Nagra: You’ve described what sounds like a plethora of AI and digital initiatives to drive medical engagement and impact. How are you governing the decision-making around this? How are you deciding where the ROI is good enough to give your teams the freedom to operate in that space, and where you don’t believe there’s going to be impact from certain digital interventions? What’s the operating model behind these digital and AI initiatives?

Alex Condoleon: Firstly, executive sponsorship really matters when you’re going through big transformative change like this. That’s because there’s a lot we don’t know; we’re going to make mistakes and learn through failing. If the goal is worth heading toward, we’ll tolerate the learning that comes with it.

Beyond executive sponsorship, the operating model has to adapt to what you’re building, especially when capabilities in digital and AI are emergent rather than matured. That means wanting as many stakeholders vested in the journey as possible, while respecting that your priority might not be theirs. That’s why high-visibility steering groups matter; they give stakeholders a chance to stay close, understand where the transformation is heading, and be heard. At every layer of leadership, it matters, and wherever the bottleneck comes up, you need to understand it and have empathy with it.

I want to pressure-test that one layer down and talk about the fear people understandably have with AI: fear of job security, job change, reskilling, in an environment where it’s not always obvious how those things will play out. I’m more optimistic than most about what these tools can bring, but I understand why those perceptions exist. Good executive sponsors, steering committees, and project structures all mean something, but it all gets lost the moment you’re out of touch with the feeling in the organization, and whether your pace of change is landing the way it’s meant to.

One more point, specific to data and AI: We’re governing data on one hand, but we have to watch that we’re not protecting it to build new power bases of leadership. That’s one of the most important tensions we’re navigating right now. We can’t have full democratization of data—there's important governance needed to protect the reasons it was created or licensed, and how it can be used. So we need governance, but also freedom to innovate. What gets in the way is when we control data for power rather than governance. That’s a watch-out we need to keep course-correcting. But I’m sure in five years we’ll be more mature than we are now, and in ten years native to how we work. For now, these are areas that deserve double attention.

Leading change without losing trust

Yigit Yorulmaz: I want to probe on that idea of change and how you enact it. You’ve been emphasizing how technology alone isn’t enough, and clearly we need to think about creating the right governance, operating model, and winning hearts and minds as part of the journey. But I want to ask about the hardest moments you’ve reflected on. As an industry, we’re now at the cusp of another wave of innovation with even greater potential. How do you think about the relationship between the degree of ambition, leaning forward, and doing change the right way for the culture and structural ways of working? Is there a balance? What learnings are you bringing into this new chapter of your leadership?

Alex Condoleon: A friend of mine once said innovation is like a potent therapy—the dosage, the cadence, the sequencing, it all matters. I keep that top of mind. I’ve probably got more battle scars than trophies on my wall.

There’s no perfect recipe to it. But when you get the mix right, you see teams energized, you create momentum, and people lean in. When you get it wrong, there’s resistance and fatigue. Even the most ambitious vision means nothing if you don’t win hearts and minds, and ripple that change outward.

A few thoughts occur to me, in dramatic periods like the one we’re in. First, on the leader: Leading change today takes immense courage. Success is like a very fine wire that you have to walk, and it’s got failure on either side of it. And you have to be prepared to fail if you want to succeed, something organizations don’t always reward.

Second, alignment top-down matters, because organization-wide transformative change can’t make sense in isolation; it has to fit the totality of where the organization is heading. One-off bits of change don’t make sense to people if they don’t fit the collective effort. So the second thought is about organizational relevance to the level of ambition you’re pursuing.

Third is demonstrating that it’s not just a priority, but one you're willing to put resources and capabilities against. Those are the elements that actually make change happen. But all of that means nothing without trust in the system, and trust is often our afterthought rather than our first thought. We’re at our best when the change we drive builds a great result for the company, but also a great culture—a spirit of collective innovation, success, and celebration. That means staying focused on whether the change is relevant to the passion, purpose, and pride people bring to their work.

There’s a bit of political intelligence involved, which is really just understanding how to lead change relevant to multiple agendas. If you can make healthcare better and make people proud of their contribution, that’s the beginning of a change journey set up for success.

Navraj Nagra: When we think about proving the impact of medical affairs, what does success look like for you, both for the work you’re doing and for the medical affairs organization more broadly?

Alex Condoleon: I don’t know how many people hold my view, but I hope soon everyone in medical affairs does. I believe that you absolutely can measure the impact of medical affairs. And if I get a little bit provocative, maybe the more relevant question is: Are we ready to take accountability for what the results tell us? Because, if we’re going to talk about metrics and measuring impact, I think we need to be really clear on why we even want to answer those questions. And when we’re at our best, I think we want to understand what our impact is solely and purely so we can push ourselves to do better. I say that not because we shouldn’t be proud of the results we’re already achieving, but because the needs in healthcare are so high that we should always want more.

Additionally, measuring medical affairs’ impact doesn’t mean measuring everything we do. We should choose only a few things that really matter. Two lenses come to mind. First, it needs to be an externally relevant guide. We’re a healthcare professional (HCP), healthcare system, and healthcare environment facing team, and everything we do—the evidence we generate, the insight we bring, how we engage with stakeholders—is about shifting healthcare. Being grounded in something externally based matters because it represents the full value chain of medical. Second, to help stakeholders at any level understand our measures of impact, we need to keep it simple and consistent; a dashboard with a thousand data points isn’t easy to understand.

So what tells a meaningful, externally grounded story that keeps pushing us further? Care gaps are one area I’ve been a strong believer in—understanding demonstrated practice behavior improvement, with a belief that over time it leads to patient outcome improvement. That represents the full value chain of medical: Wherever you sit in that chain, you’ve contributed to something impacting how care is delivered. It’s understandable at the highest level, and externally grounded. That doesn’t mean it’s the only data point further down; we need to understand how each team contributes to that roll-up metric, which is where we get a hierarchy of metrics feeding up to something that shows impact.

That’s a tangible, actionable example, relevant to the full value chain, one that says medical understands its ultimate impact, and teams can pursue an endless goal of driving better care. We’re probably never going to reach zero care gap, but we can reach a cultural and professional endpoint, an enduring ambition to not give up. That represents the passion, precision, and value of this function, and can resonate externally and internally to elevate it.

What patient centricity actually looks like

Yigit Yorulmaz: I love that what you’re saying ties back to your clear passion for the patient, that ultimately the better outcomes are in service of patients across these different therapeutic areas. I want to probe on that a bit more. What does great look like when we’re being truly patient-centric? It’s a term everybody uses, and in some shape or form we are, but if we push ourselves to bring that to day-to-day for a global medical affairs organization, from its full remit, what could great look like in your vision for being patient-centric?

Alex Condoleon: It’s a term we’ve used for a long time, and we keep learning what it means in its full power. At its core, patient centricity starts as a cultural philosophy before it becomes an operational model. Many of us in medical affairs started as healthcare professionals, doctors, pharmacists, nurses, or allied health professionals, and we need to stay connected to that origin, ultimately impacting patient outcomes. The cultural symbols start with leaders and colleagues talking about the insights they’ve learned from and for patients, the actions they’re taking to improve patients’ lives, and how they’re measuring the contribution they’re making. That cultural ethos is the important starting point for the operations to come alive.

Operationally, we often think about engaging healthcare professionals, especially key opinion leaders, to make our science relevant and applicable to how care is delivered. We can go a step beyond that now. Data and analytics bring us closer to the patient, giving us a more precise understanding of the patient journey and where the friction points are than we might have assumed before. It gives us a louder voice for what patients want, their need state, and the endpoints that matter most in their lives. Lastly, it shows us that patients aren’t uniform; every individual is distinct. Injecting those reality checks into our strategies, priorities, and tactics can move us off the textbook approach to healthcare and into the meaningful part of it, the reason we entered this field in the first place.

The role of AI in shaping medical affairs

Navraj Nagra: You’ve described how you’re seeing AI genuinely make a difference for medical affairs, clinicians, and patients, somewhat through a form of co-intelligence, where your teams work with AI to be more productive. Where is there still hype in the equation, though? We still see a lot of claims and counterclaims about what’s possible and what’s real. I would love your take from a global perspective.

Alex Condoleon: I genuinely believe AI is the most disruptive technological advancement of our generation, at least so far. There is hype, coming mostly from doomsdayers forecasting the downfall of society and workforces. But technologically, I don’t think we’re seeing hype. If anything, it’s the opposite: The technology is ahead of what we’re able to deploy. A decade ago, when I was pushing a digital agenda for medical affairs, the formula was overpromise, work hard, and learn from what didn’t live up to the business case. Today it’s the reverse: More is possible than we can deploy, and the external world is moving faster than we can internally absorb.

A few points to back that up: More than 10 percent of the world’s population are using ChatGPT every week today. And one in four of those frequent users is searching on healthcare every week. Microsoft Copilot recently put out its report of usage in 2025, and the number one (mobile usage) search topic was health. So it’s not only that AI is disruptive, it’s that healthcare is front and center of this AI story. That’s why I say there should be a positive call to action for stakeholders in life sciences, pharma, and medical affairs when it comes to AI.

At the same time, AI has the potential to eliminate repetitive, lower-value work, freeing up our professional contribution to be maximized elsewhere. That means thinking through how AI augments human potential, lifting the co-intelligence, judgment, creativity, and discovery we bring. AI will give us productivity, and I think the real strategic question, the counterbalance to the doomsday hype question is: What are we going to do with the capacity that we free up in organizations? If all we do is drive productivity with AI, I don’t think we promote new discovery. I don’t think we change standards of care. I don’t think we close care gaps beyond what we are doing today. All we do is achieve some productivity. I think the question for all of us is to get crisp on how we are going to reinvest the liberated resources so that we use the co-intelligence of AI to have an even bigger impact on healthcare than what we can imagine achieving today.

That’s the magic place, but getting there takes real strategic thinking. I actually think that second part—where we redeploy resources to—is as big a strategic question as any we’ll have to answer as we build up AI capabilities.

Author Talks: AI is advancing. Is our judgment keeping up?

2026-10-22T16:00:00+00:00

In this edition of Author Talks, McKinsey Global Publishing’s Adam Volk chats with Tiffany Xingyu Wang, president and cofounder of Oasis Consortium, and World Economic Forum Young Global Leader, about The Trust Code: How to Unlock AI’s Promise Without Losing Humanity (Wiley, September 2026). Wang examines why our instincts for trust can misfire when applied to increasingly capable AI systems. She argues that learning to calibrate trust can help individuals and organizations use AI with intention, preserve human judgment, and capture its benefits without sacrificing agency. An edited version of the conversation follows.

Why did you write this book?

I wrote this book because I kept watching very smart people ask a question that’s almost too big to be useful: “Do we trust AI?” AI’s already here, and we trust it when we use our credit cards, because fraud detection is running behind the scenes. We trust it when we switch on the light, because various types of software are balancing the grid. But more and more often, AI models are drafting our work, and, increasingly, AI agents are acting on our behalf. The central question of our time is no longer whether to trust AI, but how much trust does a system deserve, under which conditions, and for which tasks?

Often, we see that being overly trusting exposes us to harm, and not trusting enough traps us in paralysis when it comes to innovation. It’s only the calibrated trust, which this book advocates for, that enables us to move forward. Professionally, I spent over a decade building trust and safety systems for big enterprises.

Often in boardrooms, crisis rooms, and on conference stages, people ask me what trust means in the AI era. I never really had the time to give a holistic answer. When I began writing this book, I was pregnant with my second child, and my son was two years old. That’s when the stakes became even more personal to me.

My son talked to Alexa and rode in the Waymo with me regularly. None of this is remarkable because the next generation will not remember a time when AI was not already embedded in our daily life, making every decision with us and on our behalf.

We may not control how fast AI is advancing, but one thing we still have the time and the power to control is how deliberately we give AI that authority. I searched for a book that could teach me that missing skill, but I couldn’t find it. So I decided to write it.

Was there anything that surprised you in the research, writing, or response?

We have a machine—cognitive technology—that sounds warm, very confident, very empathetic, but without any of those signals corresponding to understanding, responsibility, or intentionality that a human would have. This creates a profound evolutionary mismatch: the Stone Age brain we have evolved from is evaluating Space Age systems.

That mismatch is one of the first surprises that I encountered during the overall process. It’s very foundational to understanding many aspects the book covers. The second surprise came from writing the book itself. The faster AI moved, the more convinced I became that the book shouldn’t be about what is happening with AI now but about how to think about whatever happens next.

As you can imagine, it’s terrifying for an author to write about what is probably the fastest-moving technology in history. I wondered whether the book would be outdated at the time it hit bookstores. I tried many ways and spent much effort to really go back to first principles when explaining any concept, any headline in this book, so that people could use it as an evergreen guide to decipher whatever comes next.

Why is it so difficult to define trust when it comes to AI?

After speaking with many people while researching and interviewing, I found the distinction that cleared this up for me.

There is trustworthiness, which is about the system itself. And we can talk about parameters that matter to AI technology. But in this context of AI, is it truthful enough? Is it competent enough? Is it also benevolently aligned with your personal goals or your enterprise goals well enough? Is it appropriate for that context? That’s about the system.

Trust is a different thing. Trust is about us. Am I willing to rely on it?

Another matter is calibration, and whether those two match. A technically very strong system can be under-trusted, and a very charming but unreliable system can be wildly over-trusted. That’s what’s happening everywhere now. Both [approaches] are actually failures.

Instead of asking, “Do I trust AI?,” what we should ask is, “What vulnerability are you accepting today for what promise in the future and in what particular situation?” Because the definition of trust is that you accept vulnerabilities today in hopes of a promise in the future. Trust remains a very vague sentiment until you can articulate and understand the vulnerabilities you accept today and the goals you want to achieve tomorrow for very specific conditions and situations.

But once you correctly articulate the question and think through it—some frameworks we talk about in the book—trust becomes a decision. It becomes a decision that you can examine, measure, and improve upon.

When it comes to AI adoption, which areas of trust do you see leaders neglecting the most?

I really focus on what trust means through the lens of AI. There are four prerequisites for trust: competence, truthfulness, benevolence, and contextual fit. I chose those four because I constantly saw people mistake competence for trustworthiness.

It’s very important to understand that AI is so capable that it will sound very competent and aligned with you, while it has no reason to be benevolent to maximize your well-being. Understanding that pitfall allows you to have the sense of guardrails you need to constantly think about when using AI. Manifestation is the quadrant that is especially important for enterprise leaders now. Does our behavior match what we say we trust? There are so many research papers now being published that say that. We started pilots, but the ROI is low.

It’s very important to understand that a pilot is a statement of belief. What really shows a state of trust when an enterprise adopts AI or not is whether we change the workflow, whether we train the staff, or whether we change the incentives. Match what you say to what you do, and think about how you use the signals of behaviors to assess the level of trust you delegate to AI.

Finally, there are the unseen factors. It’s important to understand that these technologies are meant to be warm, empathetic, and very confident. But they are not earning our trust by delivering the track record and the proven evidence, either by ROI or by reliability over time. They’re simply triggering our trust.

How can we hold AI players accountable when even they can’t predict how their systems will behave?

In today’s AI age, accountability means owning the conditions around unpredictability, and that articulation is extremely important. There is a missing actor in this conversation about AI. We usually draw a map of two posts: AI labs that build models, and regulators that write the rules.

But enterprises decide what gets bought and what’s connected to real systems. We give access to real data and deploy it at scale. Consider any mature industry, for example, aviation. Airplane builders are responsible for designing and testing it, but airlines have the responsibility for how the plane is operated and who controls it.

Of course, the regulators set a floor nobody can opt out of. AI needs the same layer. Often, the power and responsibility of enterprises as a whole are underused and underrecognized in AI governance or AI accountability. In other words, enterprise leaders have more agency than we give credit for in this race of AI.

If I’m buying AI for an enterprise, I would stop asking, “Is this model safe?” Instead, I would say, “Show me a failure and show me how you contained it. Show me how quickly I will know [about a failure], and show me what recourse I have if it costs me money or reputation.”

Enterprise leaders need to understand the strong agency you have to decide what model you buy, how you deploy it, and what protection measures you take. Hopefully, when enterprises come together and make that the default, the norm, we can see the maturity of this industry.

Labs build capabilities and regulators set the floor, but it is the enterprise that decides how much authority we give it.

Can you give an example of how AI is expanding human judgment and perception?

I interviewed Professor Stephen Quake at Stanford, a pioneer of single-cell genomics. He described biology to me as historically 90 percent experimental and 10 percent computational. The ambition is to reverse it so that it’s 90 percent computational and 10 percent experimental.

We have already seen a glimpse of this with AlphaFold, which used AI to predict more than 20 million protein structures and to understand better how biology works. A cell takes it to a more complex, different level, because every cell has 20,000 protein-coding genes.

Understanding cells better means we can better understand our body, our health, our diseases, and our therapies. But when it involves running in 20,000 dimensions, that’s beyond human brain capacity. That’s something you can’t do without AI.

But that doesn’t mean human judgment is stripped from the process. Instead, where the judgment is situated shifts. Arguably, the more AI can surface and test hypotheses for us—more cheaply, quickly, and at scale—the more important human judgment becomes. Because ultimately, humans must decide which hypotheses to pursue, which tasks and experiments to undertake, and how to apply results in ways that benefit humanity—and weed out harmful applications, such as using that same technology for bioweapons.

Biology is a very clear example of how AI expands our perception that, as humans, we operate in three dimensions. When a task involves high-dimensional data, we need AI’s help. But it makes our human judgment more important.

If you move from science to art, it becomes controversial. In the creative world, many designers and creatives face this question: With AI on the rise, what is the value of creatives? To answer that question, I interviewed two masterminds of creativity and design: Brian Collins and Greg Hahn, both of whom run their own design advertising shops. They are at the top of their game, and no doubt both are using AI. The workflow is becoming like an hourglass. On the top, it’s faster and cheaper to run variations and do research, and at the bottom, it is cheaper and faster to distribute it.

But the narrow waist of the hourglass is our taste, our care for the audiences, and our judgment of what would work, and our understanding of cultures. These are the many things that make one design somehow click with you, and one advertising becomes viral, versus others simply that don’t.

Human judgment continues to be a scarce and valuable asset that we need to practice and own.

How has your personal relationship with AI changed after writing this book?

My personal relationship with AI is becoming intimate but unsentimental. I use AI every day by default, and I trust almost none of it. Writing this book didn’t make me more or less cautious; it simply made me more specific. I started the process expecting to land somewhere on the optimist-to-pessimist spectrum, and I ended up rejecting the spectrum entirely.

I think binary trust is lazy trust. The question is, trust it to do what, under which conditions, and with what recourse? I don’t really have a position on AI anymore. What’s most important now is to have a practice, because trust is not practiced. It’s not well-defined, but the most important skill is being able to protect yourself and your enterprise, and unlock AI’s promise.

As a parent, that’s where trust matters most to me. My children talk to Alexa without hesitation. I’m not trying to keep AI away from them. That ship sailed before they were born.

Judgment isn’t taught to children in a lecture format. Children watch someone practice it, and then they learn it. I’m trying to make sure my children see me make decisions and hear me say, “Let’s check it out.” I’m genuinely optimistic, but not because I think the systems will get safe on their own.

We talked about accountability, the systematic thinking we need to have, and what agency enterprises and individuals need to take on. The variable that determines how this goes is not only the intelligence and capability of machines; it is also whether we, as humans, keep exercising that judgment in their presence.

Unfortunately, I see this less discussed and practiced, and that is the purpose of this book. I trust AI precisely, but not generally. I want my children to inherit the habit of deciding for themselves. That’s really the trust code.

Asia’s energy reckoning: Securing the energy system to drive growth

2026-10-21T16:00:00+00:00

Recent disruptions have shone a spotlight on the vulnerabilities of Asia’s energy system. Countries could focus on five priority investment areas to support regional growth and resilience.

The UK’s regional talent trap and how it restricts regional growth: UK Productivity Series, part 2

2026-10-21T16:00:00+00:00

The United Kingdom’s productivity gap is a management problem, and this management problem is a talent problem, and this talent problem is a geography problem.

The UK’s productivity gap is a well-documented and persistent economic challenge. The most recent OECD data shows the UK generates $86 of GDP per hour worked, a figure materially below that of Germany ($103) and the United States ($105). Recent revisions to growth rates have not changed the fact of the UK’s lagging absolute productivity levels. This gap has significant consequences, reducing the fiscal headroom available to governments to implement policy and acting as a persistent drag on living standards.

Our previous piece demonstrated a connection between national GDP per hour worked and the adoption of management systems by a nation’s businesses; it also showed that the United Kingdom is not implementing these management systems as well as comparable economies, even though the practices are well documented and widely understood. The same data exposes a split between London- and non-London-centric industries: London-centric industries are more likely to have implemented better management practices and so would likely benefit from the associated increased productivity.

According to research on behavioural change and our experience serving clients around the world, a group-level change in behaviour requires that at least 25 percent of a group advocate and behave differently. For a ten-person leadership team in an operational setting, that means a minimum of three people must have the drive, intellectual capability, knowledge, skills, and expertise to change the way an organisation runs day to day. People with this combination of capabilities are scarce. We observe that this top talent is created mainly through two routes: leaders cultivated from the shop floor up and executives with higher education who developed knowledge, skills, and expertise through varied early-career roles.

Operationally intensive firms outside London have limited access to the second pool of top talent. High-paying roles account for 40.8 percent of jobs in London, versus a national average of 23.2 percent, and their numbers are growing more than two and a half times faster in London than in the rest of the country. This concentration attracts a large share of the nation’s high-performing graduates—the top talent of tomorrow—because of the real and perceived benefit to their careers. By their late 20s and early 30s, people are often part of a dual-high-earner household, each with high-value jobs. In many cases, only London can offer both partners a high-value job in the same place. Professional identity then makes a trailing partner reluctant to move out of the London economic area even where household economics favour it. As a result, the couple stays and remains accessible only to businesses there.

Ever more of this type of talent is absorbed into London, while the regions are unable to attract enough of the kind of leaders they need to grow their business, compete globally, and generate wealth locally. Even a modest rebalancing could have a significant impact on British regions’ prosperity while having minimal negative effect on the prosperity of London. But instead, firms outside London can get stuck in a self-perpetuating loop: Unable to recruit top talent, they do not experience the benefit and so struggle to justify the business case to invest in it. This is seen in a decline in operational pay. The median production manager sat at the 86th percentile of UK earners in 1970; today the equivalent role sits at the 67th.

Germany and the United States are less exposed on both counts. Their high-value economic activity is distributed; Berlin accounts for 5 percent of German GDP, versus London’s 23 percent. And operational leadership carries higher pay and status, with premiums over median earnings of roughly 65 percent in Germany and 100 percent in the United States, versus 15 to 25 percent in the United Kingdom, once adjusted for measurement inconsistencies.

London is a high-performing economic region. It competes in industries across the world stage and generates tax revenue that funds public services across the whole country. The answer is not to restrict London’s growth but to lift other areas up so they too can more easily compete on the world stage and return wealth to those regions. McKinsey Global Institute’s Power of One research shows that national productivity growth comes from a small number of standout firms whose leaders make bold strategic moves through strong management practices which allow them to execute at scale. The United Kingdom needs such leaders to implement the management practices in businesses that would unlock growth across the whole nation.

We believe that to increase the regions’ access to top talent while maintaining London’s prosperity, businesses and policymakers would have significant impact by pursuing five approaches:

Revalue operational leadership toward US and German pay premiums.

Build prestigious graduate pathways into operations.

Formalise the route from shop floor to the top so that future leaders gain broad experience early in their careers.

Hire for critical leadership mass, rather than individual stars.

Stimulate economic activity across the country so that high-value roles are not almost exclusively limited to London.

The implication of the talent trap described in this article is uncomfortable. London’s success is a vital part of the national economy and contributes hugely, and this should be celebrated. But this has unforeseen consequences. As London absorbs a large share of the nation’s highest-potential graduates, operationally intensive businesses outside of the London economic area have limited access to this talent pool. These businesses then struggle to reach a critical mass of leaders with the ability to install management systems that will spur productivity, compete more effectively on the world stage, and thus return more wealth to those regions.

The United Kingdom’s productivity problem is, in part, a management problem

The United Kingdom’s productivity gap is a well-documented and persistent challenge. The most recent OECD data shows the country generates $86 of GDP per hour worked, a figure materially below that of Germany ($103) and the United States ($105). This gap has significant consequences, notably reducing the fiscal headroom available to governments to implement policy and acting as a persistent drag on living standards. Recent adjustments by the Office for National Statistics (ONS) to the measurement of UK hours worked have improved the United Kingdom’s position in the G7 productivity growth table, but the fundamental gap in output per hour with Germany and the United States still stands.

Our previous piece demonstrated a connection between GDP per hour worked and the adoption of management systems—the mechanisms by which organisations allocate resources, track performance, and drive corrective intervention. That connection is supported both by McKinsey’s recent survey research and by the World Management Survey, which draws on data from nearly 18,000 organisation manufacturing managers in 38 countries and finds that a one-point improvement in management score on its five-point scale is associated with the same output gain as a 40 percent expansion of the labour force or a 60 percent increase in capital.

Importantly, our research showed that the United Kingdom implements these management systems less than comparable economies, with a direct bearing on national productivity. Good management systems are not a secret; they are well documented in publicly available literature and deployed by thousands of leading companies worldwide. Something, therefore, is preventing UK businesses from implementing them at scale.

The stakes are higher than the arithmetic of average firms suggests. The McKinsey Global Institute’s Power of One study of 8,300 large companies in Germany, the United Kingdom, and the United States found that fewer than 100 standouts generated two-thirds of the productivity growth in its sample between 2011 and 2019, and that they did so through bold strategic moves executed at scale. Most were not start-ups or superstars but large incumbents that improved year after year. Productivity growth in the UK sample was close to zero, against 2.1 percent a year in the United States.

Operationally intensive firms need management systems most and are concentrated outside London

Operationally intensive businesses are those whose competitive advantage depends heavily on operational performance—through lower costs, greater agility, or more effective use of assets. The defining characteristic is cost structure, not sector. A pharmaceutical company manufacturing generic drugs at a low price point is operationally intensive; one focused on drug discovery, for which manufacturing is simply a route to market, is not. Volume manufacturing, food and drink production, and logistics are typical examples, and they are more prevalent outside London. These are the businesses that stand to gain most from the management systems we know drive productivity.

In firms where operations account for a large share of the cost base, marginal improvements in how work is planned, executed, and measured are competitively decisive. Our analysis shows that there is a “regional talent trap” that is preventing these operationally intensive businesses from tapping into the talent needed to implement strong management systems, which in turn will allow them to become more productive, better compete on the world stage, and return wealth to the region of the United Kingdom they operate in.

Two types of leaders can transform an organisation’s operations

Modern management systems are both complex and specific to the organisation in which they are implemented. They require leaders who combine intellectual capability and drive with the knowledge, skills, and expertise of modern operational management. These individuals come from all walks of life, all career backgrounds, and every region of the country. Early in their career, they go through many roles that allow them to build the knowledge, skills, and expertise required to lead modern operational business.

In our experience, organisations acquire such leaders through one of two main routes: by cultivating leaders from the shop floor up and by recruiting executives with higher education who have developed knowledge, skills, and expertise through varied early-career roles.

In cultivation from the shop floor up, an individual joins an organisation early (for example through a technical apprenticeship) and builds deep operational knowledge, skills, and expertise needed to lead through successive roles—either by promotion within the same business or by changing employers within the same industry. In operational businesses around the world, we consistently see that this route can generate very strong leaders able to drive real change in businesses and industries.

In the graduate-professional route, individuals typically hold a degree, often in a subject related to their later work. They then accumulate knowledge, skills, and expertise through multiple employment experiences during their 20s and 30s, sometimes accelerated by a formal graduate scheme, often moving rapidly through roles within the same organisation or in different ones.

The routes differ in their starting point, but the way this top talent is made is fundamentally the same: People with intellect and drive build capability through experience across a variety of roles. These are the people we see able to transform large parts of operational organisations by implementing management systems which then improve their organisation’s productivity and therefore global competitiveness. Both routes are valuable and produce world-class leaders. In contrast, where organisations fail to cultivate this critical mass of talent, even capable leaders are reduced to caretaking—holding the status quo rather than transforming it.

The leadership critical mass needed to change an organisation

Operationally intensive businesses are typically organised into operational units. As an example, they might have 500 people working within each unit. The structure varies—a manufacturing site, a cluster of retail stores, a distribution centre, a single call centre—but the scale is similar. To transform the productivity of one of these units for the long term requires implementing a management system and making it stick, usually in the face of an organisation’s natural inertia. Effective management systems are not secret; they have been well documented over the last 50-plus years. Despite this, businesses struggle to implement these approaches within their increasingly complex operational environments.

Implementing a management system is a collective act, requiring a critical mass of highly capable leadership. Putting one excellent leader into a business without a similarly capable team is unlikely to significantly improve how the organisation operates (see sidebar, “Critical mass in operational leadership: A tale of two sites”). This insight from our experience in organisations around the world appears consistent with a broad body of evidence from the social sciences. Fifty years of empirical research on organisational and community change has consistently found that a committed minority can shift group norms, but only once it reaches a threshold estimated at roughly 25 percent—a figure corroborated by more recent experimental work, including research published in Science in 2018.

Regional businesses have not implemented management systems as well as London-based industries

A striking pattern in our own survey data suggests a disparity in management system implementation between London-centric industries and others. In McKinsey’s 2025 Operational Excellence Survey, organisations in London-centric industries—such as financial services and technology—reported a score of 5.90 out of 7 on management system adoption, putting them at the 58th percentile; those in non-London-centric industries—such as manufacturing and natural gas—scored 5.09, placing them at the 19th percentile (Exhibit 1). Put plainly, against the German, US, and overall UK respondents, London-centric industries in the UK score better than more than half of all respondents, while non-London-centric industries score worse than most. The sample sizes are small, the scores are self-reported assessments, and the split is also by industry rather than location, so we infer the London link from where those industries concentrate.

While granular empirical evidence is relatively sparse, these findings are consistent with, for example, the Office for National Statistics regional data on UK management practice scores. This indicates that firms in the same sectors but located in the South East, London, and Scotland are much more likely to have structured management practices than those in, for example, Yorkshire and the Humber, the North West, or West Midlands.

These differences in the adoption of management systems between UK industries are echoed in the productivity and global competitiveness differences between London and non-London sectors. London is a top global financial centre, for example, with UK financial services contributing more than £200 billion to the economy in 2025. In contrast, UK manufacturing slipped from 11th to 15th in the UN Industrial Development Organization’s Competitive Industrial Performance Index during the first two decades of the 2000s. ONS data shows Germany to be approximately 16 percent more productive per hour worked in manufacturing, with UK output per hour roughly 10 percent below the G7 average.

The Power of One study, cited earlier, asks which talent strategies “can nurture … managerial leaders who can make bold strategic moves”—the kinds of leaders who create standout firms. Our answer is that the United Kingdom already has such leaders, but the regional talent trap keeps them away from the operationally intensive businesses in the regions that could become the next standouts.

The regional talent trap

Our analysis shows that the structure of the UK economy is incentivizing its graduate talent (which originates from the whole nation) to gravitate to and then remain in London and the surrounding economic area. For organisations outside London, this removes one of the two routes to generate top talent, making it harder to form the critical mass of leadership needed to implement the management systems they need to become more productive, compete better on the world stage, and provide these regions with better-paying jobs.

Dissecting the regional talent trap

The regional talent trap can be broken down into five interrelated parts:

High-value roles in the United Kingdom are heavily concentrated in London. High-value roles, defined by the Economics Observatory as highly skilled, well-paid roles, are concentrated in London and the South East. ONS data shows that 40.8 percent of jobs in London are high-paying—nearly double the national average of 23.2 percent. And the gap is widening. Institute for Fiscal Studies analysis shows that employment in high-paying occupations grew 240 percent in Inner London between 1993 and 2022, versus 95 percent across the country as a whole.

High-value roles are a magnet for high-performing graduates. The concentration of these roles in London and the South East exerts a strong pull on academic high performers at the point of graduation. At the start of a career, it is a rare individual in this group who can transform a business, but some can later become the top talent described earlier who can. Research by the Centre for Cities found that 22 percent of all graduates who moved to a new city after university chose London, and the likelihood of choosing London increased along with the prestige of the graduate’s university (Exhibit 2). Thirty-eight percent of graduates with a first-class or upper second-class degree from a Russell Group university who moved within six months of graduation chose London, alongside 52 percent of equivalent Oxbridge (Oxford or Cambridge) graduates.

This is not a claim that selective universities make better leaders; it is a description of the natural graduate flow. Once graduates are in London, job concentration enables them to jump to different roles to develop professional experience early in their career with little personal disruption. Someone in London can have six different roles in the first decade of their career without having to move or undergo any geographic relocation. Outside London, this is much rarer, as fewer areas offer that density of transfer opportunities in a similar sector.

Assortative mating creates ‘power couples’ where both are in high-value roles. People build friendship groups and professional networks with others who have similar jobs, earning power, and educational backgrounds. Also, they often find long-term romantic partners within those same groups. As a result, many high-performing graduates (regardless of sexual orientation or gender) partner with other high-performing graduates, becoming a “power couple,” a term coined by American sociologists, and both of them work in London in high-value roles during the early years of their careers.

The United Kingdom’s economic structure makes it hard for such a couple to maintain their careers outside London and its surroundings. Once someone in this group has a similar partner, the two of them become “trapped” in the London economic zone. It is well documented that professional power couples gravitate to metropolitan areas economically large enough to support two high-value jobs.

Many may want to live outside London but are unwilling to move due to the perceived career impact. Ipsos polling from July 2026 captures this precisely: Asked whether they would rather live in London or Manchester, Britons more often selected Manchester (26 percent, versus 21 percent for London), due to cost of living, friendliness, and community. However, London decisively wins on job opportunities and as the place where “more successful people” live and work.

For this group, a career is more than a source of income. Professional identity is formed in one’s 20s and 30s. For some, a career is not just what they do but who they are. Someone working at a prestigious law firm does not view themselves as “someone who works at a law firm”; they are “a lawyer.” If they choose to leave London as a trailing partner, they are unlikely to find a high-value role in the same location as their partner and so would have to stop, pause, or move backward in their career. The effect on their sense of self can outweigh a net improvement in living standards for the couple. Put simply, a couple may have a better standard of living outside of London (the reduction in cost of living may be more than offsetting the reduction in income of the trailing partner), yet they still would choose not to move, due to the psychological impact of slowing their career.

A significant part of the nation’s graduate top talent, therefore, choose not to leave London, even if they might have a better standard of living elsewhere. Large numbers of people move out of London in the 25-to-35 age bracket, but most move to adjacent regions and are still economically active within London itself (referred to in this article as the London economic area). The pull of London and its stickiness act as a brake on the mobility of talent into other parts of the country. That in turn could prevent operationally intensive firms from accessing the talent they need to implement effective management systems and increase their productivity.

Firms outside of London can face a talent challenge

Industries outside of London feel this impact and often struggle to recruit top talent that can transform their business’s productivity. Half of manufacturers say they cannot source the talent their business needs locally, for example, and 97.5 percent say hiring and retaining skilled people poses a significant challenge to their business. Note that although this talent issue affects many different sectors outside London, manufacturing has some of the cleanest data on the issue. The risk is clear: Organisations outside London then do not see the benefits of productivity improvements and therefore do not see the investment case to acquire top talent at higher levels of remuneration. This perpetuates lower adoption of the management practices, which undermines their ability to compete on the world stage.

The pay and status of operational roles have fallen in relative terms over the last 50 years. In 1970, for example, the median production manager earned enough to sit at the 86th percentile of the national income distribution—a salary equivalent to around £68,000 a year in 2026. Today the median production manager has slipped to the 67th percentile, with an annual income of around £48,000.

The change is even starker at the top of the profession. In 1970, the best-paid 20 percent of production managers sat roughly at the 98th percentile of all earners, a position equivalent to about £133,000 a year in 2026. Today, the equivalent top-earning production managers sit at the 89th percentile, with an annual income of around £74,000. The equivalent pay commanded by the top of the profession has fallen by almost half.

A decline of this kind is both a symptom and a cause of falling status. When a career’s pay ceiling drops away from the top of society’s income scale, the role loses its pull on people choosing a career, making it harder (but not impossible) for those roles to attract the ambitious future leaders who could drive the industry forward. Unlike in 1970, few people who command the highest salaries in the United Kingdom are in operational leadership roles.

Some firms break this pattern by offering prestigious formal operational leadership programs that capture talent at the point of graduation. However, these seem largely restricted to foreign-owned multinationals and a small number of global UK firms. This appears consistent with the World Management Survey finding that US-owned firms in the United Kingdom tend to import stronger operational management practices.

Within the United Kingdom, it seems that the London-centric industries are better at developing formal graduate schemes. Our informal review of UK leadership graduate schemes shows a ratio of approximately six to one in favour of London-centric industries over non-London-centric operational ones.

Why conditions in Germany and the United States seem more favourable

The structural forces described here appear less pronounced in Germany and the United States, the comparison economies we are using in this series. This is significantly influenced by differences in the geographic concentration of economic activity and by the relative pay and status of operational roles.

German and US economies are less concentrated

In Germany, economic activity is less geographically concentrated than in the United Kingdom. Berlin, the largest city, contributes only 5 percent of the country’s GDP, compared with London’s 23 percent share. With a distributed industrial base and thousands of hidden champion small and medium-size enterprises distributed across the country, a highly educated couple in Germany have more options to build dual careers outside the capital city. Although Germany’s industrial economy has challenges of its own today, the distribution of its talent is not one of them—and the productivity gap between the United Kingdom and Germany remains significant.

The United States has a genuine multi-hub economy. A dual-career household can build two high-value careers not only in the coastal centres of Boston, New York, San Francisco, and Washington, DC, but also in Atlanta, Austin, Chicago, Denver, Houston, Seattle, and beyond. Furthermore, the interior metros such as Columbus, Nashville, and St. Louis have all grown their shares of graduates and knowledge workers in recent years. Collectively, US cities offer ambitious graduates a range of viable options that the United Kingdom cannot match.

Operational roles have higher status and pay

Operational roles in Germany and the United States often command higher pay and social status. In healthcare, for example, a UK healthcare general manager earns approximately £58,000, while a medical and health services manager in the United States earns a median $117,960 (£87,000). Pay is an imperfect proxy for status, but the gap is consistent with operational roles retaining a standing abroad that they have lost in the United Kingdom.

Production managers are paid a premium—the extra pay versus national median that a role commands for its responsibility and the scarcity of the skills needed to do it. Germany pays a premium of 65 percent, the United States 100 percent, and according to official statistics, the United Kingdom pays 50 percent. But the UK figure is inflated: The ONS category includes highly paid engineer and managing directors and other senior leaders whom the American and German statistics classify elsewhere. When we adjust the UK premium using advertised salaries for production managers, it falls to between 15 and 25 percent—less than half of Germany’s premium and a quarter of the US premium.

The entry requirements for a production manager position tell a similar story. The ONS says of the UK role that “there are no pre-set entry standards” and that entry is possible “with either a degree or equivalent qualification, and/or relevant experience.” The US Bureau of Labor Statistics lists a bachelor’s degree and five or more years of related experience as the typical route in. Germany classifies the role at its highest skill level, which normally means a university degree, usually in engineering. Britain treats as an occupation what the United States and Germany treat as a profession.

In Germany this premium may reflect the high cultural status of operations management and engineering. Germany’s leading engineering universities—TU Munich (TUM) and RWTH Aachen among them—have institutionalised relationships with the country’s most prestigious industrial employers. TUM’s formal Partners of Excellence include BMW, Bosch, Siemens, and Volkswagen, and RWTH Aachen alumni consistently fill some of Germany’s most senior DAX executive positions.

A similar dynamic exists in the United States, where Boeing and the GE companies rank among the top dozen employers of Georgia Tech mechanical engineers. Caterpillar and John Deere recruit there, too.

In the United Kingdom, by contrast, perceptions of operational roles lag those of other sectors. A Nestlé study of 2,000 young people found that just 4 percent would consider a career in manufacturing.

Graduate schemes develop and attract potential future top talent at the point of graduation

Both countries also have more established graduate pathways into operational leadership and treat these pathways as a credible route to the very top. In Germany, the chief executives of all three major carmakers started on their companies’ graduate programs. Oliver Blume joined Audi’s trainee program in 1994 and has led Volkswagen Group since 2022. Ola Källenius joined Daimler-Benz as a trainee in 1993 and has led Mercedes-Benz Group since 2019. Oliver Zipse joined BMW as a trainee in 1991 and led the company from 2019 until May 2026, when he handed over to Milan Nedeljković, who joined BMW as a trainee in 1993.

In the United States, Procter & Gamble hires at entry level and builds from within. The company reports that more than 99 percent of its senior leaders began their careers there. David Taylor, who served as the chairman, president, and CEO from 2015 to 2021, joined in 1980 as a production manager. GE Aerospace’s two-year leadership programs follow the same logic: more than 25 percent of its top leaders began their careers on one.

What company leaders and policymakers could do

London is a high-performing economic region. Its industries compete across the world stage and generate tax revenue that funds public services across the whole country. London and the South East (broadly, the London economic area) are the only two regions that pay more into the Treasury than they receive in public spending. The answer is not to restrict London’s economic activity but to lift other areas up so they too can better compete on the world stage and return wealth to those regions.

Reversing the structural forces described in this article would entail deliberate actions by company leaders and policymakers.

Company leaders

Revalue operational leadership. Raise the pay, profile, and prestige of operational leadership roles to shift their perceived status. A premium comparable to the 65 percent seen in Germany or the 100 percent in the United States could begin to change the career calculus for high-potential graduates.

Build prestigious graduate pathways. Foster more industrial graduate programs—structured, high-status routes into operational leadership that are explicitly positioned as a pathway to senior corporate leadership.

Formalise the route from the shop floor to the top. Make technical apprenticeships and progression from the front line into site and operations leadership an explicit pathway, so potential future leaders can gain a wide variety of experience early in their career. This can maximise the ability of operational businesses to generate top talent from within.

Target critical leadership mass within organisations. Focus on building the collective operational capability of leadership teams, not just on individual hires, to create the conditions and momentum for management systems to take root.

Policymakers

Stimulate high-value economic activity. Growing high-value jobs in regional cities through sustained investment in those cities and their transport could increase the effective density of roles to encourage the top-talent couples to pursue careers away from London. The mechanism described in this article would indicate that the lack of implementation of strong management systems is caused by regional businesses’ restricted access to national talent, which is in turn caused by the regional talent trap. Therefore, improving UK productivity will depend on not just creating high-value jobs outside London but also ensuring that the talent needed to embed these systems is distributed more evenly across the economy.

To get access to this talent, regions outside London need to offer top jobs and attract top talent to fill them. Just moving the job will not fix the problem. These leaders are created over decades, building knowledge, skills, and expertise by working in a wide variety of roles within a specific industry. Transforming regions into attractive talent hubs will mean reversing a talent trap trend that has developed over 50 years and has been reinforced by economic, psychological, and social factors.

The implication is an uncomfortable one. London and the South East are remarkable economic assets, competing on the world stage and contributing hugely to the UK’s prosperity. But if leaders with the drive and capability to transform British industry come from two main routes—the shop floor and the graduate route—then one region is absorbing a very large share of the second group. Every year, graduates who are the possible leaders of tomorrow head to the capital and its surroundings. Then, for social and economic reasons, many never leave.

Rebalancing this even slightly would give more operationally intensive businesses of the Midlands, the North, South West, East, Wales, Scotland, and Northern Ireland the opportunity to reach the critical mass of leaders they need to install the management systems that would make them more productive, more globally competitive, and more profitable. If just some of the talent London attracts could be deployed to lead businesses elsewhere, the overall effect on the country could be significant—to the postcodes in which these businesses operate and to the United Kingdom as a whole.

The State of Grocery Retail Europe: Implications for packaging players

2026-10-21T16:00:00+00:00

Continued growth pressure on European grocery retailers limits opportunities for packaging players. In this environment, cost competitiveness remains imperative.

Focus, scale, fund: The AI playbook CPG leaders are missing

2026-10-21T16:00:00+00:00

Small brands are out-innovating giants, buying patterns are changing, and scale isn’t the shield it used to be. Here’s how AI could help fund the next wave of growth in food and beverage.

Federal permitting’s uneven economic impact, and what companies can do about it

2026-10-21T16:00:00+00:00

Permitting delays ripple unevenly across states depending on their industry mix. Companies with projects in the queue can take steps to manage delays.

Three horizons of quantum: A business road map to the future computing economy

2026-10-19T16:00:00+00:00

Many business leaders now accept that quantum technology is coming sooner than they first expected, and they’re taking steps today to capture a competitive advantage. We have found that over 300 organizations worldwide are already working with quantum technology providers, with many more experimenting. What many leaders lack is a sense of how much to invest in which technologies, and over which timeline.

Faced with quantum technology’s rapid pace of development and the scale of its implications, leaders are now forced to reconsider their action plans. Across all three of its pillars—quantum computing, quantum communication, and quantum sensing—quantum technology is advancing rapidly. Quantum technology companies are hitting milestones more quickly than they initially predicted, and many have ambitious road maps that put the expected arrival of fault-tolerant quantum computing (FTQC) within the next few years. FTQC will enable reliable quantum computing at scale, opening new possibilities in everything from drug discovery to materials design to financial modeling. But FTQC is a double-edged sword; when fully scaled, it will also bring about Q-Day, the moment when quantum computers can break today’s public-key cryptography. As a result, even companies that don’t directly deploy quantum computing will need to update their security platforms to contend with it.

To help business leaders understand the road ahead, we mapped three horizons of quantum technology development (Exhibit 1). This article will also highlight the roadblocks that could slow the quantum timeline—and the emerging developments that could speed it up.

The financial stakes justify immediate action. Our latest Quantum Technology Monitor finds that quantum computing alone could create $1.3 trillion to $2.7 trillion of economic value worldwide by 2035. To capture as much of this value as possible, business leaders need to understand how quantum technology is developing across the three horizons and map their companies’ pathways through them. Waiting to invest is risky. Those that do could soon find that the partnerships, talent, and intellectual property needed to succeed with quantum technology were claimed years earlier by faster-moving competitors. They could also face severe security issues when Q-Day arrives.

Horizon 1: Early commercialization

The clearest signal that quantum technology has crossed from research into commercial deployment is that the sector now has an exit market. In 2026, companies such as Horizon Quantum, Infleqtion, Pasqal, Quantinuum, and Xanadu went public. Meanwhile, IonQ acquired SkyWater Technology in a deal that pairs SkyWater’s chip manufacturing with IonQ’s quantum hardware and software. Governments are also supplying a stream of development capital, including, for example, recent commitments of $2 billion in the United States and more than €1 billion in France. Many of these investments are aimed at increasing manufacturing capacity rather than just research output, and they show that governments see economic opportunity in quantum technology.

The demand side has changed just as much. Proof-of-concept projects are shifting to paid codevelopment. Our research finds that companies including Airbus, Boehringer Ingelheim, E.ON, JPMorgan Chase, and Liberty Mutual are actively developing applications in collaboration with quantum technology players. Cloud access and quantum-as-a-service models are also lowering the barrier to entry, turning what was once a large capital project into a strategic partnership decision. Companies are showing a strong interest in quantum. Our recent Technology Trends Outlook 2026 report shows that searches on quantum-related keywords and the number of research publications on quantum technology rose steadily between 2022 and 2025, while equity investment surged from $2.5 billion in 2024 to $13.3 billion in 2025.

None of this means that quantum computers are ready today, but the market around them is clearly forming. Supplier relationships and intellectual property are being cemented, internal skills are being built, and companies are making investment decisions even with imperfect information about when reliable fault-tolerant hardware will arrive.

The three quantum pillars are commercializing at different speeds. Quantum computing is currently the largest market of the three pillars. It draws its advantage from the quantum principles of superposition and entanglement, which allow a processor to evaluate an enormous number of possible states simultaneously. The unit doing that work is the qubit, the quantum equivalent of a classical bit. But while a classical bit must be either zero or one, a qubit can hold both values at once and behave as a single system.

Our research shows that quantum computing companies surpassed $1 billion in combined revenue in 2025—and that revenue could grow to as much as $4.4 billion by 2028. Near-term deployment of quantum computing is overwhelmingly hybrid, with quantum processors attached to existing high-performance computing environments rather than replacing them. Several such systems are already operating. The US Department of Energy’s Oak Ridge National Laboratory has connected IQM’s Pathfinder system with its Frontier supercomputer; Europe’s 54-qubit Euro-Q-Exa went live at the Leibniz Supercomputing Centre in early 2026; and Aramco and Pasqal operate a 200-qubit neutral-atom machine as the Middle East’s first commercial quantum-computing-as-a-service platform.

Quantum communication is driven by the looming Q-Day deadline. To address upcoming security risks at the physical layer, quantum key distribution (QKD) uses quantum mechanics to make interception detectable in real time on protected links, and it’s starting to move out of the laboratory. Meanwhile, postquantum cryptography (PQC)—new algorithms designed to resist quantum attacks while running on existing hardware—is here now and will be critical for companies migrating their cryptography systems. We expect the overall quantum communication market to reach $11 billion to $15 billion worldwide by 2035.

Quantum sensing is the most mature of the three pillars. It uses the same quantum properties that make qubits work but applies them to measurement rather than computation. Q-CTRL’s quantum sensors, which navigate by reading Earth’s magnetic field rather than satellite signals, have completed safety-of-flight testing, for example. Atomic clocks, gravimeters, and magnetometers that leverage quantum sensing are also finding commercial traction in sectors such as navigation, medical diagnostics, and mineral exploration. We expect this market to reach $7 billion to $10 billion worldwide by 2035.

For business leaders, positioning their companies as front-runners in Horizon 1 is about building the foundations of success. This requires securing talent, intellectual property, and partnerships today. Those that wait risk entering the next phase dependent on technologies and ecosystems shaped by their faster-moving competitors.

Horizon 2: Establishing a competitive position

Hardware road maps from leading quantum computing companies are converging on the possibility of delivering a fault-tolerant quantum computer within five years. IBM has published a potential path to large-scale fault tolerance by 2029, while IonQ is targeting two million physical qubits by 2030—enough to yield up to 80,000 logical qubits, the error-corrected units of computation that fault tolerance depends on.

Fault tolerance will bring a step change for quantum computing: machines that can sustain long computations reliably enough to be embedded into production workflows. If quantum technology companies achieve their development road maps, we expect the quantum computing market alone to capture between $43 billion and $71 billion of the worldwide “internal quantum market” by 2035. (The internal quantum market represents the total global spending expected by companies on quantum hardware, software, and services, and we estimate this market will reach $60 billion to $100 billion in 2035.)

But fault tolerance at scale is also a trigger for Q-Day, which could invoke a whole slew of new security challenges. For many large enterprises, getting ready for Q-Day is a multiyear, multibillion-dollar transformation—but one that’s necessary. Without PQC systems in place, companies’ virtual private networks, public-key infrastructure, connected devices, and identity systems could all become exposed on Q-Day. Plus, sensitive data is already exposed to “harvest now, decrypt later” attacks, in which encrypted traffic is collected by hackers today so it can be read by quantum computers on Q-Day. That means acting today is imperative to avoid severe security flaws in the near future.

However, the FTQC timeline faces several roadblocks. The case for fault tolerance by the early 2030s rests on road maps, and every technology developer—and buyer—knows that road maps often slip. Three key constraints deserve attention from leaders who are building their quantum investment plans around these dates.

The first is that scaling is now largely a manufacturing problem. Quantum technology is advancing quickly, but building quantum machines with millions of physical qubits requires lasers, cryogenic infrastructure, control electronics, and manufacturing processes that don’t yet exist in the capacity needed for true scaling.

The second is that talent is scarce. The pool of researchers and engineers who can design and program quantum computers remains limited. To scale quantum computing, companies also need software developers and solution architects to translate quantum capabilities into business applications, as well as technicians to build manufacturing systems.

The third is that there is no consensus on which quantum computing hardware modality—superconducting, trapped ion, neutral atom, photonic, spin, topological, or an entirely new one—will scale most effectively. A company choosing a single quantum technology provider is making a future-looking bet, not merely a procurement decision.

Despite these uncertainties, many companies are pushing ahead with plans for quantum technology adoption; they can’t afford to be laggards. When we interviewed business leaders already working with quantum technology providers, one-third said their companies spent $10 million or more on quantum computing in 2025, with 7 percent of those companies spending $50 million or more (Exhibit 2). These are capability-building budgets rather than experimental ones, set by buyers that have determined that today’s road maps, while not set in stone, are reliable enough to plan against.

Horizon 2 is when companies lock in their quantum strategies—and real value starts to accrue. Fault-tolerant machines will be scarce and expensive when they first arrive, so the customers that worked with providers on earlier pilots and partnerships in Horizon 1 could get first dibs on this compute capacity. Thus, during Horizon 2, the most competitive companies will be those that continue to codevelop the quantum applications they started before the hardware even existed.

Horizon 3: Leading the quantum-enabled economy

In Horizon 3, entire economic arenas could reorganize around the future capabilities that quantum technology will make possible. The McKinsey Global Institute (MGI) has identified 18 future arenas of competition that will shape the global economy over the next 15 years, collectively generating $29 trillion to $48 trillion in revenues by 2040. Quantum technology intersects with several of these arenas. This means that some of the most competitive companies in Horizon 3 will be those that redesign their operating models around quantum technology.

Quantum technology could create outsize value in four of the arenas:

Cybersecurity. Quantum technology could soon upend the public-key cryptography that underpins most enterprise security systems. Business leaders need to act now to build the PQC systems that will ensure their data remains safe after Q-Day. This is a multiyear program, not a patch cycle. We expect PQC to emerge as one of the largest quantum communication verticals, representing a $2.4 billion to $3.4 billion global market by 2035.

But quantum technology could also help companies design new, more robust security systems—which is especially crucial in an era when AI has dramatically sped up attacks. QKD is a hardware-based security approach that uses physics to distribute keys and offers a level of protection that classical cryptography cannot. QKD is distance limited and cannot provide authentication or signatures on its own, so it remains a complement to PQC rather than a substitute in the near term. But we predict that by 2040, QKD could be a standard networking option. Financial exchanges, power grids, hospital systems, and government networks could at that point run over shared, QKD-protected corridors as the default. By 2035, we expect the QKD market to be worth $2 billion to $2.8 billion worldwide.

Drugs for obesity and related conditions. Quantum computing could lead to a new world of personalized medicine, especially for chronic conditions. The body’s biological systems are far too complex for classical computers to model, so medical research is done on simplified subsets of data. In the near term, quantum computing could sharpen molecular simulation enough to design therapies for narrow patient subgroups rather than broad populations. For example, it could enable pharmaceutical companies to develop more effective therapeutics for obesity and its related chronic diseases. By 2035, we predict that quantum computing could deliver up to $400 billion in value to the pharmaceutical industry.

Further out, quantum computing could support patient-level digital twins: computational replicas of an individual, detailed enough to simulate how a disease could progress and how a treatment would work. By 2040, diagnosis and treatment could be optimized continuously for one person at a time rather than for a population they resemble.

Industrial and consumer biotechnology. Quantum technology could transform the discovery of biomaterials and biochemicals. We estimate that quantum computing could unlock up to $800 billion in value in the chemicals industry alone by 2035. A material’s useful properties are a result of how its electrons interact. Those interactions are quantum mechanical, and the possible configurations multiply so fast that classical computers cannot always calculate them directly. Thus, researchers often fall back on trial and error: synthesize a candidate, test it, adjust, repeat.

Quantum processors don’t rely on approximation, because they represent electron behavior using the same physics that governs it. In May 2026, Q-CTRL demonstrated a 3,000-fold speedup in modeling how materials behave, running on 120 qubits. That capability points toward designing materials that don’t yet exist, such as superconductors that move electricity without loss and alloys that survive inside next-generation reactors.

Robotics. Our analysis shows that the global market for general-purpose robots could reach $370 billion by 2040. Quantum computing could have a big impact on the development of general-purpose robots, including humanoid, quadruped, and other form factors, because to operate effectively in the real world, these robots must continuously process information from cameras, microphones, sensors, and other inputs. They must assess their surroundings to move safely through unpredictable environments and coordinate with cloud infrastructure, enterprise software, and fleets of other robots. In other words, general-purpose robots require huge amounts of sensing, communication, and computational processing power—all of which quantum technology could provide.

General-purpose robots that harness quantum computing from near-bot and cloud-computing platforms will come first, with quantum networking and sensing later becoming on-bot quantum enhancements. We estimate that qubots, or robots that use quantum technology in at least some capacity, could represent up to a $67 billion global industry by 2040, with humanoid qubots alone representing up to $55 billion of that total.

By 2040, quantum computing could become ordinary in many high-innovation sectors. Most organizations will likely access quantum computing through quantum-as-a-service providers. Quantum communication could feasibly scale from a handful of connections to a network layer. Quantum sensing could become embedded in common industry tools: magnetometers that read brain activity, gravimeters that map what lies underground for mineral exploration, and navigation systems that track positions without satellite signals.

Horizon 3 is not just about adopting quantum technology for its own sake. If quantum becomes as foundational as AI is today, the question facing business leaders will not be whether to use quantum technology at all, but how to do so in ways that materially change their operating models. Every leader also needs to consider how quantum technology and AI will intersect—and the value that intersection could create for their businesses, the economy, and society.

MGI’s latest research on the AI economy describes an interconnected system moving at mismatched speeds: Model capabilities are expanding exponentially, while the physical inputs they run on, such as chips, power, and data centers, are growing linearly and take years to build. Quantum computing could help unblock that bottleneck, dramatically expanding compute capacity to accelerate AI capabilities. And the AI–quantum relationship is symbiotic. AI is speeding up quantum algorithm design and error correction in ways that could hasten the arrival of FTQC. As MGI notes, changes in even one part of the AI ecosystem can ripple through the other parts to cause change to the whole system. The combination of AI and quantum computing is an example of such an interconnected shift, and together they could accelerate the arrival of Horizon 3. This would leave leaders less time to prepare for its implications.

A way forward

Over the next 15 years, quantum computing will drive deep structural shifts in how companies do business. As fault-tolerant systems scale, first movers could gain access to computational capabilities that deliver hundreds of billions of dollars in value. Preparing for this future requires action today. To do so, leaders need to understand how quantum will develop across the three horizons.

To ensure their organizations take leading positions during each horizon, leaders can focus on four strategic priorities: secure ecosystem partnerships and access to frontier systems; pilot applications ahead of commercial readiness; build organizational familiarity through talent and IP; and explore how quantum technology could ultimately alleviate compute and energy bottlenecks in AI scaling. Quantum technology will not wait for organizations to feel ready—and the window to build a leading position will narrow with each horizon that passes.

Switch tracks: Rewiring rolling stock for a new era

2026-10-19T16:00:00+00:00

Fast-growing markets in Asia are difficult for global rail OEMs to access. Value could shift from new builds toward servicing and renewals—while OEMs prepare for future opportunities.

Securing software in the age of AI: Red Hat CTO Chris Wright

2026-10-19T16:00:00+00:00

Over his roughly three decades in the open-source software industry, including the last two at Red Hat, Chris Wright has watched the sector evolve from a once niche technical field to a mainstream building block of enterprise infrastructure. As Red Hat’s chief technology officer, Wright now oversees engineering across the company, from emerging-technology research to product development and ongoing support of products already in the market. In a recent conversation with McKinsey Senior Partner Martin Harrysson and Associate Partner Shilpa Khatana, Wright discussed how a recent wave of AI-driven discoveries of code vulnerabilities is reshaping trust in the software industry and why he believes the industry will emerge from this moment more secure.

This interview has been edited for length and clarity.

Martin Harrysson: You’ve been at Red Hat for more than two decades, and in the open-source community, including early years involved with Linux, for around three. What strikes you about where open source finds itself today?

Chris Wright: It’s a fascinating time for open source. Over 30 years, it went from a fringe, hobbyist activity to something pervasive across every industry—consumer, enterprise, small business, data centers, devices. Open source is foundational to the global economy today. The vast majority of enterprise codebases—well over 80 percent, typically over 90 percent—contain open-source components. Every hyperscaler and every large consumer platform runs on open-source infrastructure. It’s entrenched, and it’s fundamental to our future.

Now it’s back at the center of the conversation, this time because of cybersecurity concerns and AI’s role in scanning code and even building models with open weights. There’s a full-circle aspect to it: Many of the same debates we had decades ago about trusting open source are resurfacing in a new technology era.

One of open source’s greatest strengths is also what’s driving today’s concerns: It’s open. Anyone can access the code, learn from it, and build with it; that’s real democratization of technology. Now that AI models have been trained on the internet, including a huge amount of open-source code, they’ve become remarkably capable at understanding, writing, documenting, and finding issues in code.

Martin Harrysson: Given those real concerns, how do you see AI reshaping open source’s role and importance?

Chris Wright: Starting around the middle of this year, a new class of cybersecurity-focused models—beginning with Mythos and the Glasswing project, and followed by efforts like Daybreak—began uncovering vulnerabilities that had been sitting latent in source code for years, in some cases upward of two decades. Some are individually exploitable; others are chains of smaller vulnerabilities that together give attackers a path to escalate privileges or break into systems. Because open-source code is visible and accessible, it has been the natural target for this kind of scanning.

That’s produced a narrative that open source is somehow uniquely insecure. I think that’s the wrong conclusion. Open source has always made a virtue of transparency—sunlight is a disinfectant. You can see exactly how a cryptographic algorithm is implemented rather than being told to just trust a vendor’s word for it. And the community development model brings a much broader set of eyes to any given piece of code than a small proprietary team ever could. That means faster discovery and faster remediation—there’s no hiding behind security through obscurity.

Martin Harrysson: So you see this having a positive overall impact?

Chris Wright: What’s happening now is that AI has made it dramatically cheaper to scan all of that code at once, so we’re seeing a concentrated burst of vulnerability discovery. It’s a genuinely uncomfortable moment: Customers are worried, and rightly so, because open source is so pervasive that they often lack full visibility into what’s actually deployed, especially when it arrives bundled inside third-party or commercial software. But I’m optimistic about where this ends. We’ll come out the other side having raised the bar for security across the board—more scanning, faster patching, and security testing built directly into project pipelines. Efforts like Daybreak, which has talked about “patching the planet,” are genuinely global in scope in a way that would be much harder to achieve if we were only securing smaller, more fragmented, proprietary codebases. And this is exactly what Red Hat and IBM are doing with our Lightwell initiative: making open source safer for enterprises.

Martin Harrysson: In the near term, what should a CIO, CTO, or CEO be doing about this?

Chris Wright: I’d point to three priorities—two are basic hygiene, and the third is more about where this is all heading.

First: Know what software you have and where it’s running. If you can’t produce something like a software bill of materials or a dependency graph across your enterprise, you have no real way to assess your risk. That visibility also helps you prioritize—not every system carries the same value or the same exposure. A database full of sensitive information behind several layers of firewalls is a very different risk profile than a public-facing service.

Second: Build the automation to actually act on what you find. Imagine every vulnerability in your environment has already been discovered. Now you need to patch potentially hundreds of thousands of dependencies across your infrastructure. If your patching process still involves months of manual review and a spreadsheet, you will never catch up—the backlog will always outrun your capacity. Mature CI/CD and DevSecOps practices, which the industry has been building toward for years but hasn’t fully adopted everywhere, are what make addressing that kind of scale possible.

Third, and this is more forward-looking: The industry needs to keep building the tools such as scanning, exploit research, and mitigation techniques that let defenders keep pace with attackers. Right now, I think attackers have the upper hand. We’re going to discover entirely new classes of vulnerabilities, not just new instances of existing ones, and each new class will trigger a fresh wave of activity. I think we’re heading toward a world where both attack and defense increasingly happen at machine speed—agents defending against agents, rather than relying on human-speed response.

Shilpa Khatana: Who should own this? In most enterprises, we see engineering taking on more responsibility for security, but there’s also a dedicated cybersecurity function, risk, governance, and now often an AI council. How should those groups divide the work?

Chris Wright: It’s a shared responsibility, but with real specialization by role.

This builds on the shift we made with DevOps and then DevSecOps—bringing security earlier into the development process, on the theory that a vulnerability caught early is far cheaper to fix than one caught in production. But developers aren’t security specialists, so DevSecOps also means building automation and gates into the pipeline that give developers visibility into security issues without requiring them to become experts themselves.

The application development team owns what’s deployed and where—they’re best positioned to know what dependencies went into their software. One thing I don’t think is well understood: When you write code, you take responsibility for it, but the moment you pull in a dependency from the internet, you’ve taken on responsibility for that code as well, even a small, stable utility you’ve never had reason to question.

At the same time, other teams need to specialize in things individual developers shouldn’t be expected to do alone: “red-teaming” at a systems level, setting compliance requirements as a CISO function, and translating policy into automated checks embedded in the pipeline. So you end up with a CISO setting requirements, teams turning that into policy as code, and development teams having responsibility for what they build and what they pull in. It’s shared, but each group brings a different kind of expertise.

Martin Harrysson: The rise of agentic AI adds another layer to this—agents are now writing code and can themselves introduce dependencies, sometimes through subagents. Doesn’t that complicate accountability?

Chris Wright: You’re still ultimately accountable if you initiated the work—that doesn’t change. But I agree these layers of abstraction can obscure what’s actually happening. We’ll likely see more issues introduced by people who weren’t being careless, just less aware of what an agent a few steps removed did on their behalf.

At Red Hat, we see a real spectrum in how teams approach AI-assisted development—similar to the broader industry. At one extreme, some teams genuinely don’t look at the code an agent produces. At the other, everything is handcrafted and reviewed by humans. Most teams land somewhere in the middle.

What gives the most AI-forward teams confidence that they’re not building something that will collapse under its own weight is that they’ve built a serious harness around the entire development and deployment process, effectively a system of checks at every stage. You define what “good” looks like for your codebase, simple principles like preferring simplicity over complexity or refactoring instead of letting the same rough code multiply. Then you build an adversarial reviewer (agent) that checks new code against those standards and sends it back with feedback until it passes. That point-counterpoint dynamic repeats at every stage of the pipeline: security scanning, penetration testing, active searches for exploitable code paths.

That adversarial structure is, I think, how we avoid trading one set of risks for another. It’s also why those leading teams aren’t spending all their time on manual code review—they’re spending it building and refining the systems that do that review for them.

Shilpa Khatana: Going back to your point about the risk of code quality degrading when teams don’t actively refactor or simplify: Have you seen AI accelerate the pace at which vulnerabilities, or technical debt more broadly, get created, simply because code is now so much easier to generate?

Chris Wright: I’d argue technical debt and security vulnerabilities are often two symptoms of the same root cause: code that was written quickly, without enough thought given to how it fits into the broader system. Humans have always introduced both by taking shortcuts under time pressure, just as AI can introduce sloppy or buggy code that’s harder to maintain and easier to exploit. We’ve built institutional processes over time to identify where we’re accumulating debt and to schedule work to pay it down. The task now is to take that same judgment, which used to live only in people’s heads, and encode it into the specifications, skills, and context we give agents.

There is one real difference worth flagging: Models are extremely good at optimizing for exactly the goal you give them, sometimes too literally. It’s the same failure mode you see when an agent “cheats” on a test because its only instruction was to pass it, and nobody told it not to cheat. We have to be explicit about what good architecture looks like, what it means to secure codebase, what constitutes technical debt, and how to avoid introducing it in the first place. And that needs to be built into the guidance we give development agents—alongside instructions to favor simplicity and avoid unnecessary layers of abstraction.

Shilpa Khatana: How optimistic are you that that can be managed effectively?

Chris Wright: What gives me confidence that this is manageable is that we’ve looked at how much code gets rewritten over time on real projects. Over a three-year window during a high-growth phase, a project like Kubernetes saw roughly 70 to 80 percent of its code rewritten. Even a mature, three-decade-old project like the Linux kernel saw about 25 to 30 percent of its code rewritten over that same three-year period. Code gets revisited and improved continuously, with or without AI. While it looks different with agents in the loop, I don’t see anything intrinsic to AI that makes technical debt a fundamentally bigger problem. It changes how precisely you need to specify what “good” means, not whether the problem is more or less solvable.

Martin Harrysson: What do you think is underappreciated in this area right now? Is there something you wish more executives were focused on?

Chris Wright: Some of it we’ve already touched on—understanding where you’re introducing risk through the software you build and the dependencies you rely on. Once you ship software, you’ve effectively become a vendor of your own product, with all the hygiene obligations that come with that—knowing what you’ve deployed, where, and how you’ll update it. This isn’t a new topic, but it tends to go in and out of fashion, and I think it deserves to stay in the foreground permanently.

I’ve spent a lot of my career in security, and one thing I’d urge leaders to do is get ahead of the narrative that open source is somehow inherently insecure. It’s not; it’s core infrastructure, and it’s something we can improve collectively faster than most alternatives.

The other area I’d point to is open models. The code underlying most AI infrastructure—agent frameworks, training libraries, harnesses—is reasonably well understood and often open-source itself, through projects like PyTorch. But we haven’t figured out the right collaborative model for open-weight models themselves: how we build them together and, more importantly, how we review a model’s content for efficacy, safety, and security once it’s built. We have decent guardrails, but the internals of how weights interact inside a large stochastic system aren’t well understood industry-wide. I’d like to see the same collaborative, transparent approach we’ve applied to open-source software—not just open weights, but open training data and training recipes, so the work is genuinely reproducible—extended to models themselves. That’s where I think the state of the art still needs to catch up.

Three horizons of quantum: A business road map to the future computing economy

2026-10-19T16:00:00+00:00

In the next decade, quantum technology could create trillions in value and break encryption, delivering opportunity but also risk. Leading companies are acting now to secure a competitive advantage.

Securing software in the age of AI: Red Hat CTO Chris Wright

2026-10-19T16:00:00+00:00

This interview has been edited for length and clarity.

Martin Harrysson: Given those real concerns, how do you see AI reshaping open source’s role and importance?

Martin Harrysson: So you see this having a positive overall impact?

Martin Harrysson: In the near term, what should a CIO, CTO, or CEO be doing about this?

Chris Wright: I’d point to three priorities—two are basic hygiene, and the third is more about where this is all heading.

Chris Wright: It’s a shared responsibility, but with real specialization by role.

Shilpa Khatana: How optimistic are you that that can be managed effectively?

Martin Harrysson: What do you think is underappreciated in this area right now? Is there something you wish more executives were focused on?

Switch tracks: Rewiring rolling stock for a new era

2026-10-19T16:00:00+00:00

Fast-growing markets in Asia are difficult for global rail OEMs to access. Value could shift from new builds toward servicing and renewals—while OEMs prepare for future opportunities.

The hidden opportunity in the next $2 trillion of US–Latin America trade

2026-10-18T16:00:00+00:00

Quirch Foods began in Miami in 1967 as a family-owned importer of Latino staple foods and today generates more than $4 billion in annual sales across 40 US states, the Caribbean, and Latin America. Latina-owned Nopalera built a Mexican-inspired beauty brand into hundreds of US retail stores before expanding into Mexico. Angel network Angeles Investors co-led a $32 million Series A investment in Canela Media, aiding its growth to more than 60 million active users across the United States and Latin America. The city of Laredo, with almost 275,000 residents and 36 percent of its nonfarm workforce in trade, has developed distinctive capabilities in logistics, warehousing, and customs brokerage, enabling it to handle $354 billion in international trade in 2025.

These are just a few examples of the importers, exporters, investors, and specialized locations that fuel the US–Latin America trade corridor. It is one of the world’s biggest and most critical economic arteries, generating $1.5 trillion in trade in 2025 —about 1.5 times the total trade between the United States and Canada and the equivalent of 85 percent of all trade within the European Union. Discussion of the corridor tends to center on large forces that shape it, such as trade agreements and tariffs, geopolitics, commodity cycles, inflation, or immigration. These forces help set the baseline of how the corridor may change in overall size. However, analysis of these factors does not fully explain how US companies continue to create enduring value within the corridor in new ways.

The success of companies such as Quirch Foods, Nopalera, and Canela Media shows how US-based Latino consumers, businesses, and places can turn positional and proprietary advantages into value that flows back to communities.

Latino consumers in the United States, a population spanning distinct national-origin groups, generations, and cultures, can provide a large and growing base of demand and cultural influence able to extend to other consumer segments. Latino businesses can bring cross-border knowledge and networks that reduce friction. A fast-growing base of entrepreneurs can expand productive capacity and drive innovation. Latino communities often concentrate and connect these advantages—from logistics and customs hubs in Laredo and El Paso to commercial and energy hubs in Miami and Houston—and extend them digitally beyond any single geography.

These factors converge to provide pathways for economic mobility in communities with sizable Latino populations in the United States. The number of Latino-owned businesses increased by 7 percent annually from 2017 to 2023, far outpacing the rest of the nation’s near-flat growth. However, these businesses are underrepresented in some of the largest export sectors, accounting for 6 percent or less of manufacturing, mining, and IT firms. Instead of relying on organic opportunity creation as the only source of growth in trade, our analysis finds that a systematic approach to the development of the US–Latin America trade corridor, with coordinated efforts across investors, businesses, trade ecosystem institutions, and governments, could increase trade volumes by 2.3 percent annually to $1.8 trillion by 2035, presenting a meaningful opportunity for US-based Latino consumers, businesses, and places.

Yet while the advantages that US-based Latinos have through the trade corridor are real, they remain fragmented and underscaled, leaving limited pathways for stakeholders to holistically capture that value in each place. An integrated ecosystem and amplification engine for the US–Latin America corridor could help create and capture this potential value by focusing on five key areas:

Designing capital to enable scale. Expand access to fit-for-purpose capital, from working-capital loans, receivables financing, and trade finance to equity investment. Prior McKinsey analysis found that providing more business banking products and services for Latino business owners could mean closing a $200 billion business-lending gap. Extending capital from seed to scale-up will enable more firms to participate in trade, where longer cash cycles, currency risk, and high up-front investment are common challenges.

Building market access and distribution deliberately. Connect specialized hubs to Latino demand through multinational corporation (MNC) procurement, retailers, distributors, platforms, and import-export channels. Roughly 75 percent of US–Latin America corridor trade is concentrated through a handful of major ports, while a long tail of more than 100 smaller ports handles the rest. But not all cities are equally connected to the opportunity this creates. Among markets with similarly sized Latino populations, cities connected to the most ports receive more than three times the consumer goods from Latin America relative to other cities, highlighting significant commercial opportunity for businesses involved in the corridor supply chain. These networks can also be the infrastructure through which new exporters emerge. A survey of small businesses from the US Export–Import Bank (EXIM) found that while 53 percent of respondents would like to become exporters, 45 percent did not know where to start. As hubs become more connected, relationships between businesses can surface more knowledge about export opportunities and enable execution partnerships.

Prioritizing the coordination of institutions to lead change. Establish an accountable coordination layer (or corridor-wide institutions) that connects capital, distribution, and hub-level efforts. Support today could be scaled: The United States Hispanic Chamber of Commerce (USHCC) represents more than 260 chambers, while the National Minority Supplier Development Council (NMSDC) connects more than 15,000 minority-owned businesses through 23 regional councils. The Korea International Trade Association illustrates how this could work on a global scale by providing tailored support in target overseas markets for South Korean businesses in the form of advisory, market intelligence, customer matching, finance, training, and advocacy (see sidebar “South Korea’s example: Intentionally driving economic value”). A US-based version of this could support businesses in the corridor to enter US Latino micromarkets with culturally distinct needs while also supporting Latino-owned businesses in the United States with export potential to enter international markets.

Making commercialization an explicit objective. Build a repeatable pipeline that identifies promising firms, validates demand, develops products and talent, and prepares businesses for export and market entry. Latino-owned exporters show outsize productivity, yet only about 2 percent of Latino-owned employer firms export today.

Protecting ownership and reinvestment. Design mechanisms that support Latinos to participate more broadly in ownership as the corridor grows rather than taking income alone.

Countries around the world, including France, Japan, South Korea, and the United Kingdom, have connected these five elements to create broader economic value. This kind of integrated ecosystem and amplification engine could create similar economic opportunities for communities with significant US Latino populations by growing the number of successful corridor-related Latino businesses as well as creating significant ownership and employment opportunities.

Understanding a critical economic corridor

The trade corridor between the United States and Latin America ranks among the six largest regional trade relationships globally. Almost one-fifth of total US trade activity flows through it, growing by an average of about 3 percent annually in real terms since 2015. While growth is projected to slow to 1.5 to 2.0 percent in the next decade, the corridor is still projected to be worth $1.7 trillion to $1.8 trillion by 2035. Additionally, potential headwinds and uncertainty exist around tariffs, inflation, and trade agreements, and scenarios with other challenges to trade could impact growth rates. But regardless of how these changes unfold, the US–Latin America corridor is likely to continue to play a significant role in global economic activity, and other known structural shifts may alter where value comes from.

Today, around two-thirds of goods and services traded are concentrated in five categories: electronics, services, transport equipment, energy resources, and machinery (Exhibit 1). And while significant trade flows both ways, with around $779 billion heading north and $677 billion south, the linchpin is Mexico, which accounts for about 66 percent of US–Latin America trade and nearly 80 percent of imported goods to the United States.

An increasingly integrated economic relationship

The production system between the United States and Latin America is also becoming increasingly integrated, with growing activity in higher-value sectors. While the corridor remains concentrated in the same major trading relationships, the roles different countries play within it are becoming more diversified. Between 2019 and 2025, for instance, Mexico added approximately $126 billion in electronics trade as well as growth in transport equipment, machinery, and medical and scientific instruments. That shift is continuing: Computer server exports from Mexico reached nearly $83 billion in the first half of 2026, with almost 94 percent of that destined for the United States.

The corridor’s integration is also deepening based on continued country-specific specialization. Costa Rica nearly doubled its bilateral trade with the United States from 2019 to 2025, driven in part by roughly $5 billion of additional medical and scientific instrument trade and $3 billion in electronics. Chile’s and Peru’s roles in metals, minerals, and energy value chains are expanding, as are Brazil’s and Colombia’s roles in higher-value services, including business, digital, financial, and intellectual property (IP)–related services. Three factors are driving this:

Latin America’s proximity, relatively lower vulnerability to transport disruption, time zone alignment, and supplier integration are powerful advantages as companies endeavor to secure supply chains and move production closer to customers in US markets to realize cost and operational efficiencies. Regional trade dynamics have recently reinforced the trend, with meaningful actions and announcements from leaders in Latin America, such as Mexico and Panama, focusing on the United States as a trade partner rather than other nations. This dynamic could create openings for US Latino-owned firms and workers across the value chain, from production and logistics to professional services and finance.

Digital connectivity is making more activity tradable across the corridor in both directions. Data costs fell by around 21 percent annually on major US–Latin America routes from 2022 to 2025, fueling the expansion of cross-border delivery of software, fintech, media, professional services, data, and AI. Digitally deliverable services were worth about $85 billion in 2025, having increased by around 9 percent annually since 2023. Knowledge-based services alone could represent a $100 billion export opportunity. AI could accelerate this shift: A recent McKinsey analysis estimates that fully utilizing AI and automation could unlock up to $450 billion in annual economic value in Latin America by 2030 while helping the region gain efficiencies in the tradeable sectors where it leads and expand across the value chain toward digital and advanced manufactured goods, further strengthening and diversifying its economies.

Capital stock from foreign direct investment in the corridor reached $464 billion in 2025, with about 84 percent of that represented by investments into Latin America from the United States. However, US investment into Latin America has remained flat since 2020, while US companies building new businesses in Latin America, or greenfield investment, has fallen by about 7 percent annually (Exhibit 2). During the same period, global greenfield investment (excluding the United States) into Latin America nearly tripled from $45 billion to $133 billion, suggesting there could be further potential for the United States to position itself to capture the corridor’s next wave of growth.

These three shifts are creating new corridor opportunities, though who captures them will depend heavily on geography, sector, and existing market access. Latino-owned businesses and communities with distinctive positional advantages and large Latino populations (those in trade hubs, in high-growth sectors, or with established cross-border networks) are best placed to benefit. An emerging ecosystem is already turning those specific advantages into repeatable plays that scale value across markets.

The importance of US Latinos in creating value from trade

Five distinctive advantages across consumers, businesses, and places highlight how Latinos today create outsize value in the corridor. First, Latino consumers provide a large and growing base of demand that companies are increasingly accounting for in their strategic decision-making. Second, Latinos wield a variety of unique cultural identities with influence that can extend beyond Latino markets. Third, Latino businesses can bring cross-border knowledge and networks that reduce friction and enable success in the corridor. Fourth, a fast-growing base of entrepreneurs and workers can expand productive capacity and unlock innovation. Finally, physical and digital integration in specific places, such as Latino-population hubs and ports of entry, concentrates and connects these advantages, enabling new business models and extending their reach across the corridor.

Consumers: Distinctive demand and cultural influence create a launchpad for growth

Large and rapidly growing Latino-linked demand can provide a promising base market for businesses operating across the corridor. In broad terms, around 68 million US Latinos today represent $2.8 trillion in household consumption, which grew three times faster than non-Latino consumption from 2019 to 2024. Latino-linked demand already accounts for about 15 percent of Latin American final-goods imports, up from 12 percent a decade ago and above Latinos’ share of US household income. As an example of how Latino-linked demand can enable mainstream adoption, Modelo Especial’s US off-premises retail sales grew from around $2 billion in 2018 to more than $4 billion in 2024, while the share of the brand’s business coming from non-Latino consumers rose from 30 percent to 45 percent. It is now the top-selling beer in the United States by dollar sales.

Latino cultural influence also extends beyond Latino consumers, creating a path to scale promising business models for products that are otherwise new to non-Latino consumers. For example, more than half of US music listeners engage with Latin music. Its share of US recorded music revenue has doubled since 2020 to roughly 9 percent of the market. Similar dynamics are seen across industries. Modelo and Tajín have become mainstream consumer brands, while beauty players such as Ceremonia and Nopalera are translating Latino heritage into differentiated propositions. In fashion, reggaeton superstar Bad Bunny’s Adidas collaborations and designers such as Willy Chavarria have brought Latino cultural references to global audiences, while sports and travel—from Inter Miami to Puerto Rico tourism around major cultural events—show how cultural relevance can translate into broader spending and economic activity. These are some of the early examples of how business leaders are starting to realize the significant pool of value related to sports, entertainment, fashion, beauty, and other sectors within the Latino community.

Businesses: Cross-border capabilities and a growing productive base serve and scale demand

Knowledge and networks can reduce cross-border friction, lowering the search and transaction costs of connecting customers, suppliers, partners, and institutions across markets. When paired with strong operations, they can turn cross-border familiarity and connectivity into commercial advantage. This emanates from deep interpersonal relationships that connect the United States and Latin American markets even outside the arena of trade. For instance, phone traffic between the United States and Mexico represents the world’s largest bilateral route, accounting for about 8 percent of global international call minutes. Such interconnectivity has a tangible business impact: Latino-owned exporters generate around $325,000 in revenue per employee, about 3.5 times that of Latino-owned nonexporters (Exhibit 3).

Latino business and labor make up a prominent proportion of total productive capacity in the United States. Per the US Bureau of Labor Statistics, Latinos now make up roughly one in five US workers and have accounted for 70 percent of US workforce growth since 2020 (Exhibit 4). And according to the US Census Bureau American Community Survey 2024 release, Latinos are increasingly skilled: For example, the percentage of foreign-born Latinos who have been in the United States five years or less with a bachelor’s degree or higher increased from 14 percent in 2010 to 26 percent in 2024, while US-born Latinos with a bachelor’s degree or higher similarly increased from 17 percent to more than 26 percent during the same period.

The number of Latino-owned firms with employees grew by about 6.8 percent annually from 2017 to 2023, compared with growth of less than 1.0 percent for non-Latino-owned firms. In addition, Latino-owned employer firms generated around 7 percent of net new jobs in the United States from 2017 to 2022 and, according to Stanford University, are more represented in emerging technologies such as blockchain and Web3 compared with White-owned businesses at 5 percent versus 1 percent, respectively (representation in AI sectors is comparable). Taken together, a larger, more skilled, and more entrepreneurial Latino base of workers and owners could translate to a latent resource for the corridor that increases both supply and demand for its commercial offerings.

Places: Physical concentration and digital integration multiply Latino advantages from trade

Physical and digital integration concentrates the advantages of Latino businesses and consumers and connects them across markets. Trade capabilities are highly concentrated geographically, with eight leading US–Latin America trade hubs accounting for roughly three-quarters of corridor imports. These hubs bring together specialized infrastructure, industries, networks, and demand, including logistics and customs capabilities in Laredo and El Paso, energy expertise in Houston, and commercial connections in Miami. Beyond ports of entry, Latino population hubs also create downstream opportunities born out of the corridor by concentrating demand and opening business opportunities around traded goods. However, direct connectivity to the corridor still matters for both consumers and businesses: Among places with similarly sized Latino populations, stronger connections to ports importing from Latin America are associated with substantially greater availability of Latin American consumer goods.

Digital connectivity can extend the same advantage beyond a single geography. Recurring cross-border interactions include scalable services, proprietary data, platforms, and IP. Businesses are finding different ways to translate cultural knowledge, institutional access, and cross-border data into business models that serve customers across multiple countries, such as the following:

Félix Pago, a fintech start-up, facilitates US–Latin America remittances through a fully digital WhatsApp-native interface.

Desteia, which has raised seed funding of $11.5 million as of 2025, integrates with Mexico’s customs filing portal to simplify compliance for MNCs.

Sonoro, which raised $12.5 million in Series A funding, has built one of the world’s largest podcast networks by creating culturally relevant audio content for audiences across the continents.

By creating ways to serve long-standing needs of Latino customers across geographies, digital integration converts the advantages of Latino consumers and businesses into market-defining plays.

From distinctive advantages to proven plays

Latino consumer, business, and place-based advantages are already translating into action. Across the US–Latin America corridor, companies, entrepreneurs, investors, governments, and ecosystem institutions are using them in a set of recurring plays that create and capture value, showing how Latino-linked demand and capabilities can successfully create opportunities across markets.

MNCs are importing and commercializing Latin American products in the United States, often using existing Latino demand as an initial market before scaling more broadly. Modelo illustrates the potential, scaling from a heavily Latino consumer base to become the number one beer by dollar sales in the United States. MNCs are also investing to expand across Latin America, bringing capital, technology, infrastructure, procurement, and operating capabilities. This trend is particularly pronounced in the technology sector: For example, Microsoft invested $2.7 billion into cloud and AI infrastructure in Brazil, and Amazon announced a $5 billion cloud-region investment in Querétaro, Mexico.

US Latino-owned businesses and entrepreneurs are pursuing a broad set of cross-border models. Across the models, each uses their unique knowledge of the Latino consumer and preexisting networks to carve out a competitive advantage. Some import and commercialize culturally connected goods, using their knowledge of Latino demand and sourcing networks to build scaled businesses. Others export brands and business models into Latin America, leveraging propositions first developed in the United States as well as cross-border market knowledge and owned assets. Nopalera, for example, which has an established footprint across more than 700 retail points in the United States, recently partnered with Ulta Beauty to launch in Mexico and leveraged a $4 million Series A funding to further fund its domestic and international expansion (see sidebar “US Latino-owned business spotlight: Nopalera”). Others are building digital services that solve recurring cross-border frictions: Félix Pago processed about $1 billion in remittances in 2024, Nuvocargo reports customs clearance times that are up to 34 percent faster, and Nowports reports shipment delays that are up to 40 percent lower through technology-enabled logistics and customs capabilities.

Places are developing plays of their own. State and local governments could build infrastructure and specialized capabilities to help make trade easier or focus on particular sectors rather than trying to replicate a full gateway. Laredo and El Paso illustrate deep logistics and customs ecosystems, for example, while Tampa shows how a secondary hub can build differentiated capabilities in cold-chain and food distribution. Specialized connectivity can materially expand access to corridor trade. Among cities with similarly sized Latino populations, stronger links to Latin American entry ports are associated with around three to four times greater availability of imported goods, and cities such as San Antonio illustrate how logistics and sector specialization can turn that connectivity into economic activity.

Capital providers and ecosystem institutions play a complementary role by helping promising corridor businesses access the capital, customers, and capabilities needed to scale. Capital providers offer fit-for-purpose capital, including growth equity, working capital, and trade finance. For example, L’Attitude Ventures built a $100 million venture capital platform for early-stage investments in US Latino-owned businesses (including Nopalera), while fintech platform and marketplace Mercado Libre uses proprietary sales and payment data to rapidly assess creditworthiness and provides working-capital financing to merchants that are underserved by traditional lenders.

Ecosystem institutions such as Latino chambers and supplier networks connect firms to customers, distributors, capital, specialized hubs, and country-specific expertise that would be difficult for any business to assemble alone. One example is ConnectAmericas, an Inter-American Development Bank (IDB)–created platform enabling Latin American businesses to achieve 17 percent higher average export value by connecting them to a network of about 240,000 companies with capital opportunities, buyers, suppliers, and training.

Taken together, these initiatives show the corridor already contains many of the ingredients needed for broader value creation. The challenge is not the absence of successful models but rather that they remain fragmented and are not yet connected to an integrated system that can scale them consistently.

Driving greater US–Latin America economic value in the corridor

Broad and largely external forces such as global policy, cross-border investment, and commodity trade cycles drive much of what forecasts anticipate for the corridor. Based on these trends, a McKinsey Global Institute analysis implies a change in growth rate from 3.0 percent in real terms in the past decade to around 1.5 to 2.0 percent per year through 2035, translating to $1.2 trillion to $1.7 trillion in cumulative added trade volume, with the current balance between northbound and southbound holding roughly stable as a share of corridor trade.

Yet there is a potentially sizable additional opportunity centered around the distinctive capabilities of US Latinos. Despite these advantages across businesses, consumers, and places and proven value creation plays, the ecosystem needed to capture the value remains fragmented. Capital, market access, institutional support, commercialization pathways, and ownership mechanisms are frequently unavailable or too dispersed for the companies and places best positioned to use them. We estimate that systematically addressing these barriers that prevent proven models from scaling could add $170 billion to $295 billion in incremental trade through the corridor in the next ten years, expanding the corridor’s growth by 18 percent. Taken together, the corridor’s momentum and this added opportunity represent up to $2 trillion in cumulative added trade over the next decade. The benefits would extend beyond the businesses that gain new opportunities to import and export. There could also be sizable spillover effects into the broader economy that create jobs, lower prices, and generate more choice for consumers and more vibrant places.

But the additional value is not guaranteed. Capturing it depends on addressing the five system constraints.

Limited access to capital

Surveys of Latino entrepreneurs have documented that more than 70 percent of Latino-business owners used their personal funds to start their businesses, and as few as 12 percent use banks or financial institutions ; Latino-owned firms are also less likely to receive the full financing they request compared with other groups. At the same time, firms based in Latin America that would otherwise be exporting or importing are unable to access finance to engage in trade. It is estimated that expanding access to trade finance (for example, through working-capital loans, receivables financing, letters of credit, and supplier finance) could increase trade volumes by 6 to 9 percent across Mexico, Guatemala, and Honduras, all of which count the United States among their largest trading partners. Improving access to different capital products at different stages of growth on both sides of the corridor could enable more businesses with promising business models to capture value from trade, especially in the critical transition when founder-funded firms seek institutional scale.

Latino-owned businesses have long shown their ability to effectively use capital to expand into international trade and business activity. By the late 2000s, Brightstar, founded in Miami by Marcelo Claure, became the largest Latino-owned business in the United States by building a scaled mobile phone exporting business that grew revenue from $140 million in 1999 to more than $1 billion by 2003. The key to its early expansion was using working-capital financing to build inventory that could be sent to Latin America. Today’s businesses are using newer financial mechanisms to reach customers across markets. Miami-based tech start-up Félix Pago, for example, has raised multiple rounds of venture funding to expand its remittance platform across multiple Latin American countries. Since its founding in 2020, it has served six million customers across 11 Latin American markets and processed $8 billion in transactions.

Narrow market and distribution connectivity

About 75 percent of trade in the corridor is concentrated in leading hubs that reach only around 16 percent of US Latinos. And further dispersion of traded goods from ports of entry to consumers in other Latino population hubs is uneven. Some of the largest Latino markets in the United States remain relatively disconnected from the trade infrastructure and distribution networks that would allow demand to translate more fully into corridor flows (Exhibit 5). This finding is consistent with McKinsey’s The economic state of Latinos in America report, which documented that 41.5 million Latinos live in consumer deserts and are 1.3 times as likely as White populations to have low access to consumer goods.

This highlights meaningful opportunities for greater consumer import penetration. For example, Los Angeles and Las Vegas have sizable Latino populations but a comparatively low level of Latin American imports, suggesting room to expand sourcing and distribution, particularly given their proximity to Mexico and cross-border logistics advantages. Companies may consider whether cost, lack of knowledge about Latin American imports, access, or other factors are barriers for consumers. Other markets show what stronger connectivity may unlock for cities that are not major ports of entry themselves: Tampa has built a differentiated position in food distribution, for instance, while Dallas functions as a major logistics hub that supports Latin American imports well beyond what local demographics alone would suggest.

More broadly, among places with similarly sized Latino populations, stronger connections to Latin American entry ports are associated with materially greater availability of Latin American consumer goods. Latino businesses need consistent pathways to access MNC procurement, retailers, distributors, platforms, corporate customers, and export channels. Without them, commercially viable businesses tend to stay local or niche. Broader market access and distribution connectivity may unlock significant economic value by converting demand into repeat purchases, larger orders, and national or regional reach.

Source Logistics, founded by Latino entrepreneurs, highlights this opportunity. It has made growing coverage of the US Latino population its core growth strategy to meet rising demand for Latin American goods, especially food and beverage. Through M&A and new warehouse builds, it has expanded to 5.8 million square feet of warehousing nationwide, reaching 80 percent of the US Latino population and 75 percent of the total US population with same-day delivery; its longer-term goal is to achieve 95 percent total US population coverage. This growth helped it rank on the Inc. 5000 2025 list of America’s fastest-growing private companies for the second consecutive year.

As connectivity improves between trade hubs and other cities, opportunities will emerge for businesses with rights to win as exporters. Quirch Foods, which started as an importer of consumer goods from Latin America, built distribution networks across the United States from a central distribution node in Miami. While 80 to 90 percent of its business remains domestic, over time, its import business led to relationships with domestic meat and seafood producers that it combined with its cross-border networks and infrastructure assets in Miami to expand into exports. Now Quirch ships more than 100 containers a week globally, with a focus on Latin America. Other successful export businesses in the corridor highlight the value stronger networks can create. For example, SolvChem, a Latino-owned chemicals distributor based in Houston, created an international division in 2009 to export chemicals to Latin America. Having already built chemicals handling infrastructure in Houston and a regional network of suppliers, it used its proximity to a key corridor port to become a dominant player in chemicals exports, generating more than $100 million in revenue per year.

Low institutional coordination

Despite an extensive support ecosystem, Latino trade participation remains low. For example, the USHCC represents more than 260 chambers, and the NMSDC connects more than 15,000 minority-owned businesses through 23 regional councils, but only 2.1 percent of Latino-owned employer firms export, compared with 2.6 percent for all US employer firms. An accountable corridor-wide coordination layer could connect commercial development, capital, distribution, specialized hubs, country-specific regulation, market knowledge, and skills, surfacing promising opportunities that would otherwise be missed.

The United States also lacks a coordinated and scaled set of efforts to amplify the commercial power of Latino cultural assets. Such vehicles have been deployed globally to great effect, such as Enterprise Ireland, which holds capital, capability building, and market access under one mandate: The same agency that funds a company also opens markets for it with its 42 overseas offices so the firm never has to assemble the system itself. In 2025, its client companies had record export sales of €38.86 billion and employed 232,425 people. The United States has already had major success promoting commercialization of other cultural assets at home and abroad, such as through its film industry, which has an estimated global annual trade surplus of $15 billion. A coordinated group of stakeholders in the United States, such as investors, trade associations, or nonprofits, could focus on identifying emerging trends with commercial potential from within the Latino community and support them to develop their business models, leveraging import and export opportunities.

Brazilian influencer Camila Coelho illustrates what’s possible when entrepreneurs can scale products in the corridor. She leveraged her more than ten million social media followers to launch two companies generating more than $10 million in combined sales by 2021: a fashion label with Revolve and a beauty line built around ingredients native to Brazil reaching more than 400 Ulta Beauty stores and shipped to 17 countries. She has used the corridor by turning Brazilian ingredients into US-launched products sold back into Latin American markets. With an enabled ecosystem, entrepreneurs like Coelho could establish a new age of US cosmetics and fashion rooted in the US Latino community.

Nascent commercialization pipeline

The opportunity for Latino firms’ broader participation in the corridor is not constrained by a lack of demand or business performance but by an uneven ability to commercialize and scale. The US–Latin America corridor lacks a pipeline that identifies promising firms and assets, validates demand, develops products and talent, builds export or market entry readiness, protects IP, codifies playbooks, and replicates what works. Having repeatable capabilities, playbooks, and intermediaries that identify promising opportunities and help move businesses from proof of demand to scaled market participation could unlock substantial value for Latino-owned businesses. Other economies have built central agencies or intermediary institutions to perform this pipeline-building function. For example, Business Sweden, a collaboration between the Swedish government and Swedish Foreign Trade Association, institutionalized the concept of getting proven firms export-ready and has helped thousands of Swedish companies enter international markets, including prominent and fast-growing businesses, such as the electric and autonomous freight company Einride, which went public earlier this year.

The missed commercialization opportunity is especially stark for Latino cultural assets, which are unlocked by trade but have potential for economic spillover effects in the US economy. Bad Bunny offers one example: The singer and rapper’s rise was enabled in part by Puerto Rico–based Rimas Entertainment, which secured an early YouTube monetization agreement that helped Bad Bunny reach audiences without relying on a traditional major-label model. That model has translated cultural demand into broader economic activity. During Bad Bunny’s 2025 Puerto Rico residency, total spending in San Juan rose nearly 20 percent versus comparable weekends a year earlier, while international spending around the venue increased by more than 15 percent. The residency also connected visitors with local businesses and helped expand Puerto Rico’s reach into new tourism markets, a tangible display of the economic power of culture that went global just months later with Bad Bunny’s performance at the Super Bowl LX halftime show.

If the United States could build a similar coordination and amplification apparatus to the same results that South Korea achieved from its Hallyu cultural engine, the economic opportunity in the United States could be worth more than $300 billion over ten years, with trade-related activity adding another $60 billion.

Weak ownership and reinvestment mechanisms

Economic growth in the US–Latin America corridor can generate income without creating durable, Latino-owned assets that let value compound over time, such as equity, brands, proprietary data, platforms, and distribution networks. Weak mechanisms for ownership and reinvestment mean founders, creators, and communities often don’t retain a lasting stake in the value they help create, which is evident at both the household and the firm level.

At the household level, Latino households that own a business hold roughly 2.2 times the net worth of those that do not, yet only 6 percent of Latino families receive an inheritance, part of a $380 billion wealth transfer gap that McKinsey documented.

At the firm level, Latino-owned firms capture less of their industries’ value than peers, generating less than half as much revenue per firm as other firms (excluding MNCs). This suggests a scale gap that compounds the ownership gap.

Latino firms in the corridor show how scaled ownership can create significant value. Goya Foods, for example, highlights the payoff of retained ownership and reinvestment. It has grown into a multibillion multinational food distributor while retaining family ownership despite multiple buyout attempts. Its former COO went on to found AUA Equity, which has an investment strategy built around its distinctive knowledge of scaling products nationally from the Latino business ecosystem.

Creating and capturing greater value from the US–Latin America corridor

A more integrated US–Latin America ecosystem holds the promise of generating significant economic value, especially for US Latinos—although it will not materialize automatically. Creating and capturing this value requires investment to build an integrated ecosystem and amplification engine for the corridor that eases entry for new participants while helping existing players scale. Such a system would allow US Latinos to apply their distinctive capabilities to proven plays to capture more value while creating spillover effects that benefit communities. For stakeholders already engaged with the corridor, the ecosystem’s amplification engine can help them succeed more often and scale faster by connecting them to system support they may be missing. As more plays succeed, assets are created along with knowledge, relationships, data, brands, and capital that strengthen the next cycle of growth in the corridor. New participants will also benefit by having better access to the existing ecosystem and access to the market entry playbooks that have been codified.

Realizing these opportunities in the corridor could have substantial impact on the economic mobility potential for Latinos in the United States. As Latino-owned businesses succeed and grow more frequently, the share of wealth that the Latino population claims through ownership increases. When stronger networks enable better exchanges between businesses about the proven plays, more businesses can enter adjacencies that have higher potential for export, a proven way to increase value creation. As businesses in the corridor grow, they create employment opportunities for other Latinos in their communities, which in turn enables skill development and opens career pathways for wealth creation. Finally, corridor participants reinvest resources into their communities through direct spending and through tax revenue generation, creating a financial base to improve quality of life through services and amenities.

Investors, businesses, governments, and mission-driven organizations looking to participate in the corridor to access these opportunities don’t need to build every capability themselves. Key stakeholders need to proactively collaborate to make this integrated ecosystem and amplification engine a reality. Each actor will be responsible for building distinct but complementary and coordinated levers that can work together to open the pipeline of opportunities. A summary of each stakeholder, how they benefit from the ecosystem, and steps they could take is in the table below.

The trade corridor between the United States and Latin America is big, growing, and critical to the economic dynamism of the region—but there are pockets of opportunity that have yet to be realized. The path is not without risk. Headwinds and uncertainty could impact how these opportunities can be realized. Nonetheless, commercial activity in the corridor will continue, and many of the ingredients for generating greater value are already in place. An integrated system that consistently scales distinctive Latino capabilities could turn this potential into greater economic momentum, providing another pathway for economic mobility for Latinos and the communities they live in nationwide.

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