Scouting for Growth

Insurance has become extraordinarily good at finding the few. The next competitive advantage may come from clearing the many.

In this episode of Scouting for Growth, I sit down with Manjit Rana, Executive Vice President for Insurance across the UK, EMEA and APAC at Clearspeed, to explore a deceptively simple question: can insurers establish trust at the same speed at which they are automating decisions?

More than 90 out of every 100 customers coming to an insurer are genuine. Yet much of the industry's fraud infrastructure has historically been designed around identifying the minority that may represent risk. Manjit's argument is not that those systems were wrong. They were built for a different information environment. Today, AI is accelerating both the automation of insurance decisions and the ability to manufacture convincing evidence, making the trust problem more urgent.

We explore the emerging concept of a Trust Intelligence Layer: a continuous, voice-derived, regulatory-ready risk signal designed to help genuine interactions move forward quickly while directing scarce human judgement toward the cases requiring closer attention. Manjit's airport metal detector analogy captures the distinction well. The signal does not make the final decision. It identifies where something unexpected deserves a closer look, allowing everyone else to keep moving.

The implications extend well beyond claims. Historical data and behavioural profiles can indicate risk, but they cannot necessarily reveal what an individual knows about an application, renewal or claim happening right now. As insurers automate more of those interactions, the challenge is to preserve trust without simply adding more friction, more profiling or more manual intervention.

Most importantly, the conversation moves from technology to operating model. If you could clear more genuine customers, identify more risk and make decisions faster, what would that change about your people, processes, workflows and economics? Manjit argues that this is where leaders should start. Because the question is no longer whether insurance will automate. It is whether insurers can tell which of those automated decisions deserve their trust.

KEY TAKEAWAYS

I want to challenge the way we have traditionally framed fraud and trust in insurance. For decades, we have become increasingly sophisticated at identifying the interactions that may represent risk. But if the overwhelming majority of customers are genuine, there is another side to the equation: how quickly can we establish sufficient trust in those customers to let them move forward? The distinction matters because every genuine interaction held up by unnecessary scrutiny creates friction, cost and a poorer customer experience. The opportunity is not simply to find more needles. It is to become much better at clearing the hay.

This is why we explore the Trust Intelligence Layer. The principle is not to replace existing fraud systems or human judgement with another automated decision-maker. It is to introduce an additional signal that helps determine where human attention may be valuable. That also exposes the limitations of relying exclusively on historical data and profiles. Data can tell us a great deal about patterns and previous behaviour, but it cannot necessarily tell us what an individual knows about the interaction taking place right now. For me, that is also where fairness and customer experience begin to converge: the closer we can get to assessing the interaction itself, the less we need to rely on broad proxies for suspicion.

The AI implications make this urgent. As underwriting, claims and customer journeys become faster and increasingly automated, trust cannot remain a slow layer sitting outside the process. It needs to operate at the same speed. But leaders should not begin by asking whether a particular technology works. They should first ask what happens to the business if it does. If more genuine customers can be cleared automatically and more high-risk interactions identified, do your people, workflows and governance know what to do next? The practical starting point is deliberately focused: one workflow, one clean baseline and 90 days. The advantage will not come from adding another score. It will come from redesigning the operating model around better information.

BEST MOMENTS

“More than 90 out of every 100 customers who come to an insurer are genuine.” – Sabine VanderLinden [03:42]

“We really need to look at a solution stack that looks at how do we clear the genuine people.” – Manjit Rana [07:16]

“The machine's job is just to go, there is something there that we're not expecting to see there, and then the human is making the decision.” – Manjit Rana [14:52]

“It's about the source. It's about the individual, not the evidence.” – Manjit Rana [24:18]

“It shouldn't be the trust intelligence layer that's making the decision. It's just another signal.” – Manjit Rana [39:53]

“The choice in front of every claims and underwriting leader listening is not whether to automate. That has been decided already. It is whether you can tell which of those automated decisions deserve your trust.” – Sabine VanderLinden [01:03:24]

ABOUT THE GUEST

Manjit Rana is Executive Vice President for Insurance across the UK, EMEA and APAC at Clearspeed. With three decades of experience in insurance, his career spans entrepreneurship, corporate innovation and consulting. He founded insurance innovation consultancy Ingenin, helped establish the AXA UK Innovation Lab, and previously led corporate innovation alongside Sabine at Startupbootcamp InsurTech. In 2026, he was named Insurance Personality of the Year at the British Insurance Awards.

At Clearspeed, Manjit focuses on taking voice-based risk assessment into production within major insurance organisations and exploring how an additional risk signal can help insurers establish trust throughout the policyholder journey. His work spans claims, underwriting, application and renewal, with an increasing focus on what trust means as AI and automated decision-making reshape insurance operations.

Download the report Moving at the Speed of Trust on the Clearspeed website.

ABOUT THE HOST

Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.

If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights.

And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

AI is changing the economics of business. But it is not rewriting the fundamentals of strategy.

In this episode of Scouting for Growth, I sit down with Professor Gianvito Lanzolla, Professor of Strategy at Bayes Business School, advisor to boards and CEOs, and co-author of Diversification in the World of Data and AI, to examine what actually creates competitive advantage when access to intelligence is rapidly becoming ubiquitous.

The uncomfortable truth is that better technology alone does not create a better strategy. Being early to AI can accelerate learning, but scaling before you understand how the technology creates value, how the organization captures that value, and what customer problem it solves can simply mean buying expensive learning. Gianvito makes an important distinction between being early for learning and being early for scaling.

We then explore why data in context matters more than data volume. Tesla's understanding of driving behavior can translate naturally into insurance because the data is relevant to risk. Tesco's knowledge of what customers buy did not automatically reveal what they wanted to watch. The strategic question is not how much data you hold, but what the data describes, how it was generated, and which decisions it can improve.

That same discipline applies to diversification and governance. Companies still need a credible right to win, supported by continuous data in context, domain expertise, and the ability to orchestrate an ecosystem. And as AI increasingly influences or makes decisions, governance cannot remain a sequence of static gates. Gianvito argues for adaptive governance built around monitoring, feedback loops, escalation mechanisms, and the ability to revisit decisions as the technology evolves.

Most importantly, the conversation brings strategy back to fundamentals. If AI ultimately becomes more like electricity than a source of differentiation in itself, advantage will come from the business model built around it. Gianvito leaves leaders with three questions: Value, Visibility, and Viability. What new customer problem can you solve? What can you see or do now that was previously impossible? And what can you organize and execute economically today that you could not before?

Because AI may change the context, the economics, and even the architecture of decision-making. But competitive advantage still depends on understanding where value is created, why you have the right to capture it, and how you build an organization capable of doing so.

KEY TAKEAWAYS

I want to challenge the assumption that moving first on AI automatically creates an advantage. Gianvito's distinction between being early for learning and early for scaling is particularly important. Organizations should experiment early because they need to build the capacity to understand and implement the technology. Scaling is different. That decision requires a credible hypothesis about the business model: how does AI create value, how will the company capture it, and what becomes possible economically that was not possible before? Access to increasingly powerful models will spread. The differentiation comes from what the organization builds around them.

This is also why the conversation around data needs to become more precise. It is not enough to say that a company has enormous amounts of data. What matters is data in context: what the data describes, how it was generated, and which decisions it can inform. Gianvito identifies three capabilities behind AI-enabled diversification: generating a continuous flow of relevant data, combining AI with domain expertise, and orchestrating the ecosystem required to deliver the proposition. Even then, traditional strategic questions remain. Do you genuinely know something your competitors do not? Do you have a right to win? And is the opportunity economically large enough to justify pursuing?

For me, the governance implications are just as significant. As AI evolves and takes on greater decision authority, we cannot govern it solely through fixed approval gates designed for relatively stable technologies. Governance itself needs feedback loops, monitoring, escalation, and the ability to revisit decisions. The same principle matters for founders selling into enterprises: do not lead with how much AI sits inside the product. Lead with the bottleneck you remove and the outcome you enable. As operational activities become easier to automate, validation and trust may become increasingly scarce sources of value. The strategic question remains remarkably familiar: what customer problem can you solve, what can you now see or do, and can you execute it in a way that is economically viable?

BEST MOMENTS

“First mover advantage or advantages, they've never been about being first.” – Professor Gianvito Lanzolla [06:48]

“I would separate early for learning from early for scaling.” – Professor Gianvito Lanzolla [09:13]

“It's not data, it's data in context.” – Professor Gianvito Lanzolla [22:06]

“There is a conceptual shift from governance as gates to governance as a continuous learning system.” – Professor Gianvito Lanzolla [43:25]

“Access to AI is not a competitive advantage in itself.” – Professor Gianvito Lanzolla [01:02:41]

“AI strategy doesn't start with AI, starts with the three V, value, visibility, viability.” – Professor Gianvito Lanzolla [01:05:44]

ABOUT THE GUEST

Professor Gianvito Lanzolla is Professor of Strategy at Bayes Business School, City St George’s, University of London, Founding Director of the Digital Leadership Research Center, and an advisor to boards and CEOs.

Originally trained as a mechanical engineer, Gianvito's work has evolved around a fundamental question: if technology matters but is not enough, what else determines who creates and captures value? His research and advisory work spans strategy, digital transformation, AI, diversification, business models, governance, and organizational change.

He is co-author, with Constantinos Markides, of Diversification in the World of Data and AI and has researched how organizations can scale AI through adaptive governance. His work connects enduring principles of strategy with the rapidly changing economics of data, AI, and digitally orchestrated ecosystems.

You can buy the book on Amazon wherever you are in the world: Diversification in the World of Data and AI

ABOUT THE HOST

Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.

If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights.

And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

Discovery is becoming abundant. Customer ownership is not.

In this solo episode of Scouting for Growth, I go inside the Distribution Intelligence Layer and examine what happens when AI assistants become a new front door to the market. Customers can increasingly discover products, compare options, generate quotes, and decide what to do next before they ever reach a company website. The strategic question is no longer simply how they find you. It is what happens to the relationship when they arrive.

The uncomfortable truth is that AI may not remove the broker. It may remove the search box and relocate the customer's first interaction into an interface the enterprise does not control. As search, comparison, form-filling, and routine administration become cheaper, value moves downstream toward advice, trust, complex judgement, service, and renewal.

Walmart shows what this shift looks like when an enterprise treats agentic distribution as an operating model rather than a chatbot. Aviva and Plymouth Rock show the same logic beginning to emerge in insurance, using ChatGPT as part of the discovery and quotation journey while connecting customers back into enterprise systems. The visible assistant is only the shop window. Behind it must sit product data, identity, payments, service, governance, and accountability.

I frame the Distribution Intelligence Layer around three essential jobs: be found, be buyable, and be remembered. Product information must be accurate enough for an assistant to understand. Secure connections must allow quotation, purchase, and service. And when the customer moves into the enterprise journey, their details, permissions, context, and choices need to move with them. The hand-off determines whether the organization simply receives a transaction or begins a relationship it can continue through service and renewal.

Most importantly, I leave leaders with three practical moves: try to buy your own product through an AI assistant, make one product AI-ready from discovery through service, and map the complete hand-off from the customer's first question to renewal. Because being found creates an introduction. The real commercial advantage lies in receiving the customer, serving them, and earning the right to keep the relationship.

KEY TAKEAWAYS

I want to challenge the idea that agentic distribution is simply another digital channel. When an AI assistant can understand the customer's problem, narrow the available choices, and send the business a customer with stronger intent, it begins to change where the market starts. Discovery becomes cheaper, and some of the value historically attached to search, comparison, form-filling, and administration moves elsewhere. This is why I do not believe the broker simply disappears. The broker's value moves downstream toward advice, trust, judgement, and ongoing service.

This is why I frame the Distribution Intelligence Layer around three jobs: be found, be buyable, and be remembered. Discovery is only the first leg of the journey. The hand-off into the enterprise must carry customer data, identity, permissions, consent, context, and choices while connecting securely to payment, service, auditability, human accountability, and renewal. The distinction is important because customer ownership is not automatically surrendered to the platform. It is shaped by how the journey is designed and by who controls what happens after the introduction.

For me, the practical implication is that leaders should stop debating this shift in the abstract and experience the journey themselves. Try to buy your own product through an AI assistant. Make one product AI-ready from discovery through service rather than attempting to transform the entire portfolio at once. Then draw the complete hand-off and identify who owns the data, identity, payment, service, renewal, and next conversation at every stage. For founders, the same test applies: an impressive interface is not enough unless it can connect securely into a real enterprise journey. The strategic prize is not simply access to the transaction. It is continuity of the customer relationship.

BEST MOMENTS

“That is not simply a new channel. It is a new front door to the market.” – Sabine VanderLinden [00:53]

“The broker does not vanish. The broker's value moves downstream.” – Sabine VanderLinden [04:01]

“For founders, the clever assistant is no longer enough. The product must connect to real systems, respect permissions, leave an audit trail, and help the client keep the customer.” – Sabine VanderLinden [05:26]

“It is important to note that customer ownership is not automatically lost to the platform. It is a design and commercial decision in the end.” – Sabine VanderLinden [09:02]

“Think of a relay race. The assistant runs the first leg. Your firm runs the second. The customer data and trust are the baton.” – Sabine VanderLinden [09:54]

“Being found is useful. Keeping the customer in the end is the business model.” – Sabine VanderLinden [13:37]

ABOUT THE HOST

Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.

If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights.

And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

Four excellent tools can still produce nothing. Intelligence only creates value when it connects, compounds, and scales.

In this solo episode of Scouting for Growth, I introduce the Enterprise Intelligence Stack, a practical framework for understanding why promising agentic pilots stall, why different AI agents can operate with completely different views of the same customer, and why the third pilot can end up costing as much as the first.

The uncomfortable truth is that most enterprises are not suffering from a lack of capability. They are suffering from a lack of composition. Data, models, platforms, people, and governance may all exist, but they rarely operate as one coherent system. The result is fragmented intelligence, duplicated infrastructure, weak accountability, and pilots that work beautifully in isolation but struggle to scale.

I break the Enterprise Intelligence Stack into four layers and two rails: Data and Judgment; Models and Orchestration; Distribution and Access; Customer and Experience; supported by the People and Trust rails. I then introduce a four-part Composition Test built around one shared source of truth, one registry, one audit trail, and one accountable human.

Most importantly, I leave leaders with three practical moves: draw the stack, run the Composition Test, and fund the rails before funding the next layer.

Because in the age of agents, the winners will not necessarily be the firms with the most intelligence. They will be the ones whose intelligence connects, compounds, and scales.

KEY TAKEAWAYS

I want to challenge the way we continue to diagnose enterprise AI problems. We often blame procurement, technology, models, talent, or adoption when an agentic pilot fails to scale. But increasingly, I believe the real constraint is composition. We may already own many of the capabilities we need, but they are not working together as one system. Four excellent tools can still produce nothing if they operate from different data, different logs, different governance models, and different assumptions about the customer.

This is why I introduce the Enterprise Intelligence Stack: four layers covering Data and Judgment, Models and Orchestration, Distribution and Access, and Customer and Experience, supported by the People and Trust rails that have to run through the entire system. The distinction is important because layers can be purchased, whereas rails must be designed and governed by the organization itself. The Composition Test then gives us a practical way to challenge every new initiative: is there one shared source of truth, one registry, one audit trail, and one accountable human?

For me, the biggest takeaway is that we need to stop asking simply, “What should we buy next?” and start asking, “How will this connect to what we already have?” Whether you are an enterprise leader or an AI venture selling into one, the objective is the same: build intelligence that can connect, compound, and scale. The next move is not necessarily another purchase. It may simply be to draw the stack and understand what you actually own.

 

BEST MOMENTS

“It is not a tooling problem. It is a composition problem.” – Sabine VanderLinden [00:16]

“The real failure is that the capabilities we already own don't work together.” – Sabine VanderLinden [00:16]

“A firm can use four suppliers and still have one shared spine. It can also use one supplier and end up with four disconnected systems.” – Sabine VanderLinden [08:00]

“Enterprise buyers do not need another brilliant soloist. They need a venture that can join the orchestra.” – Sabine VanderLinden [08:00]

“A pilot or product integration that can't connect to the wider system is a cost, not an option.” – Sabine VanderLinden [10:00]

“Do you have a stack or do you have a shopping list?” – Sabine VanderLinden [14:00]

 

ABOUT THE HOST

Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.

If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights.

And if you’re interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

Kevin Maney: The Category Creation Formula for Winning Markets in the AI Era

AI is making products easier to build, but meaningful differentiation harder to sustain. A stronger feature set, a faster model, or simply adding an “AI-powered” label may not be enough to convince customers that a new market should exist in the first place.

Kevin Maney, bestselling author, technology commentator, and founding partner of Category Design Advisors, joins me to unpack the Category Creation Formula: Context + Missing + Innovation = New Market Category. Developed through nearly a decade of fieldwork with more than 50 companies, the framework offers a practical way to understand when markets are ready to change: identify what has shifted around the customer, uncover the newly urgent need that shift creates, and then design the innovation the market is ready to embrace.

Our conversation moves beyond conventional product-market fit toward a more fundamental question: What would market-product fit look like? We explore why being first matters less than establishing the accepted dominant design, why competitors can actually validate rather than weaken a new category, and why successful category creation starts outside the organization, not with the technology already sitting inside it.

In an era of AI abundance, compressed innovation cycles, and increasingly crowded markets, this conversation offers a different way to think about growth. It is particularly relevant for CEOs, transformation leaders, investors, and founders asking not simply how to build a better product, but how to create a market that wants it.

KEY TAKEAWAYS

What I found particularly compelling in this conversation with Kevin is his challenge to one of the most deeply embedded assumptions in innovation: that we should build a product and then find its market. Kevin turns that logic around with the idea of market-product fit. Instead of starting with what our technology can do, we should start by understanding what has changed in the world around our customers, what new problem that change has created, and what innovation could solve it. His Category Creation Formula, Context + Missing + Innovation, gives leaders a simple but powerful way to make that shift.

I also loved Kevin’s perspective on category leadership. Being first is not necessarily the objective. The real goal is to establish the dominant design that shapes how customers understand and expect the category to work. And, counterintuitively, competition can help because other players entering the market validate the category in customers’ minds. For me, this is a particularly important lesson in today’s crowded AI landscape.

Finally, Kevin makes a distinction I think every leader should consider: using AI to make the existing business more efficient is only one side of the opportunity. The bigger question is what entirely new products, services, and ways of operating become possible because AI has changed the context. As AI itself becomes foundational and ubiquitous, the differentiator will increasingly be what humans do with it, and the ideas they create about what comes next.

BEST MOMENTS

“Category design is a strategy. And when I think of positioning and marketing, those are things that a company does.” – Kevin Maney [13:52]

“Product-market fit has led a lot of people to think we'll build this product and figure out a market to put it into. And we talked about it, saying, like, it's actually market-product fit.” – Kevin Maney [25:00]

“Your ultimate goal is to win the dominant design. It's not to be the day one, first mover, be the first one to say, ‘We're creating this category.’” – Kevin Maney [29:57]

“Your goal is not to wipe out all competition, your goal is to be the dominant design and make all of your competitors follow you.” – Kevin Maney [29:57]

“I actually believe that the big AI models... that's going to become, at some point, as boring as the internet or as electricity. What excites me is what you can do with it.” – Kevin Maney [37:26]

“AI is trained on the past, and it's only going to understand the past, and it's going to be the humans that create the future.” – Kevin Maney [49:19]

 

ABOUT THE GUEST

Kevin Maney is a bestselling author, award-winning technology commentator, and founding partner of Category Design Advisors. He is the co-author of The Category Creation Formula and Play Bigger, and spent 22 years as a technology journalist at USA Today. His work has also appeared in publications including Newsweek, Fortune, The Atlantic, and Wired.

Kevin helps leadership teams identify, define, and develop new market categories. Through nearly a decade of category-design work with more than 50 companies, his focus has included market-product fit, dominant design, the adjacent possible, strategic points of view, and the leadership alignment required to translate innovation into an understood and adopted market choice.

 

ABOUT THE HOST

Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.

If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights.

And if you’re interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures