Bastiaan de Goei: From Intelligence Layer to Human in the Loop

More AI pilots will not fix an operating model that cannot scale them. In this episode of Scouting for Growth, I sit down with Magda Ramada, Senior Managing Director and Global InsurTech Innovation Leader at WTW, for an impromptu conversation recorded on the floor of ITC Vegas. Coming directly after our AI Horizon 2030: The Readiness Problem summit, we explore why so many promising AI experiments in insurance still struggle to move into production—and why the industry's familiar problem of pilotitis is becoming increasingly difficult to ignore. The uncomfortable truth is that AI has not created the pilot problem. It has made the transformation required to overcome it more demanding. Horizontal productivity tools may help people write emails or manage general tasks, but insurance-specific AI must understand the complexities of underwriting, claims, pricing, and capital modelling. Agents need to interact with specialized insurance systems, and organizations need to redesign how people and technology work together. Another proof of concept will not solve an operating model that remains unchanged. Magda identifies three constraints that leaders consistently underestimate: people, integration, and data. Skilled professionals must help shape the systems they use, rather than simply being asked to adopt them. Agents need to connect with the insurance software that supports real business decisions. And the data required for agentic workflows reaches beyond structured databases into what Magda calls golden data sets: evolving, machine-readable representations of expert knowledge, reasoning, decisions, and exceptions. These must be continuously updated as experts encounter new cases, identify patterns, and refine their conclusions. This becomes especially important as experienced underwriters and actuaries retire. Scaling expertise is not about replacing the people who possess it. It is about capturing how they think, what they question, and how they make decisions so that intelligence can be shared more effectively across the organization. Done well, AI becomes a partner that challenges assumptions, exposes blind spots, and helps experts identify mistakes. The objective is not to eliminate human judgement, but to build the trust required for people and agents to collaborate—and to increase autonomy only where it has been earned. We also explore how agentic distribution could create new opportunities for growth, as personal AI assistants increasingly become the first point of contact between customers and insurers. But a faster quotation is not necessarily a better one. Magda offers a simple example: an insurer might require answers to five specific questions before providing a quotation, while a customer's personal AI assistant may not know which information matters. Insurers will need systems that validate incoming information, ask the right questions, and protect underwriting appetite and pricing strategies from being reverse-engineered by automated agents operating at scale. Most importantly, Magda leaves insurance leaders with a practical agenda for Q1 2027: value existing experts and develop future talent, improve connectivity across insurance systems, and understand the architecture, controls, harnesses, and evaluation layers required to scale agentic AI responsibly. Because when intelligence becomes increasingly accessible, the advantage will not come from having more pilots. It will come from building the organizational capability to turn expertise, technology, and trust into repeatable execution—and sustainable growth. KEY TAKEAWAYS I want to challenge the assumption that launching another AI pilot brings an organization meaningfully closer to becoming AI-ready. Magda reminds us that insurance has been collecting shiny technology experiments for years. Agentic AI makes this familiar problem more complex because deploying an intelligent system is not the same as integrating it into a business process that people understand, trust, and can improve. Horizontal productivity gains have their place, but the more important question is whether AI can perform insurance-specific work within underwriting, claims, pricing, and distribution. Scaling AI is fundamentally an operating-model challenge, one that requires organizations to rethink workflows, responsibilities, and human–agent collaboration rather than simply add another technology layer. This is why our conversation focuses on people, integration, and data as an interconnected challenge. An insurer needs the knowledge of its best professionals, agents that can interact with specialized applications, and a sustainable way to extract and update the expertise that makes its decisions valuable. The golden data set is not a static repository of documents or historical information. It is a living representation of human judgement, capturing the questions experts ask, the reasoning behind their decisions, the exceptions they recognize, and the order in which work should be reviewed. For me, this is where trust becomes essential. AI should strengthen human expertise, not replace critical thinking. As systems demonstrate reliability, organizations can delegate more responsibility while keeping people accountable for the decisions that matter. The practical implication is to prepare for scale and growth at the same time. Agentic distribution could transform how customers discover insurance, compare products, and request quotations, but insurers must ensure that automated interactions contain the right information and do not expose sensitive underwriting or pricing knowledge. Magda's priorities for Q1 2027 provide a clear starting point: invest in current experts and the next generation of talent, strengthen connectivity between systems, and develop the architecture, safeguards, and continuous evaluation needed for agentic AI to improve over time. The opportunity is not simply to become more efficient. It is to create new ways of serving customers and expanding the business. But there is a growth opportunity only for organizations prepared to capture it. The insurers that succeed will be those that move beyond isolated experiments and build operating models capable of turning intelligence into trusted, repeatable value. BEST MOMENTS "It's very difficult to scale human underwriting expertise." – Magda Ramada [05:47] "Data in the age of agentic systems is a very different animal." – Magda Ramada [09:01] "I don't think it will ever replace human judgment." – Magda Ramada [14:25] "These channels can actually reverse engineer your underwriting rules, your appetite, your pricing strategy." – Magda Ramada [16:56] "It's not like, you know, Copilot is going to do your pricing anytime soon." – Magda Ramada [19:27] "There is a growth opportunity, but only for those that are ready." – Sabine VanderLinden [20:54] ABOUT THE GUEST Magdalena “Magda” Ramada Sarasola, PhD, is Senior Managing Director and Global InsurTech Innovation Leader at WTW. She brings more than two decades of experience in international consulting, innovation, and research and development, helping insurers translate emerging technologies into practical capabilities across pricing, underwriting, and distribution. Her work focuses on embedding human expertise into AI systems and connecting technological change with the operating models needed to make it useful. In this conversation, she explores how evolving golden data sets, expert participation, and trust can move insurers beyond pilotitis—while preparing them for a future in which personal AI assistants reshape insurance distribution. 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 [email protected].

AI can prepare the quotation. It cannot take ownership of the decision. In this episode of Scouting for Growth, I sit down with Bastiaan de Goei, who leads financial services marketing at OpenAI, for a conversation recorded live at ITC Vegas about what happens when AI moves beyond an impressive demonstration and into the daily work of an insurer. Having worked across AXA and Instabase, Bas understands both the promise of frontier technology and the reality of bringing it into established insurance operations. The uncomfortable truth is that automating individual tasks is not the same as designing a useful workflow. Bas points to practical progress already being made: at Travelers, he describes an OpenAI-powered tool handling 80 to 90 percent of claims intake data, while Oscar Health has accelerated claims verification by 50 percent. Those gains matter, but they are only the beginning of what becomes possible when intelligence can connect the steps between systems. We explore the intelligence layer through the working day of a commercial underwriter. Instead of manually sorting every broker submission, extracting data, checking pricing and preparing quotations, an agent could coordinate those steps and present a set of cases ready for review. The underwriter becomes the orchestrator rather than the operator, with more time for uncertainty, exceptions and decisions that genuinely require expertise. But greater automation does not transfer responsibility to the machine. Bas is clear that the final quotation remains the underwriter's decision. The technology must make its evidence visible: the steps taken, the assumptions made and the source data used. Without that transparency, human oversight risks becoming little more than a final click. Most importantly, Bas argues for what he calls 'efficiency by a thousand cuts': start with a real point of friction, prove the value and build from there. An inbox triage improvement that surfaces worthwhile submissions previously left untouched may be more valuable than an 18-month transformation programme. We also examine the economics of capacity, the need for continuing cyber safeguards and the importance of giving experienced people room to experiment. Because the objective is not to remove the expert from the workflow. It is to expand what skilled people can do, while making the decisions they remain responsible for more informed, more efficient and more trustworthy. KEY TAKEAWAYS I want to challenge the way we often think about AI adoption as a collection of isolated use cases. Improving claims intake or accelerating verification is valuable, but the bigger opportunity comes when those capabilities can work together. The intelligence layer Bas describes connects submissions, documents, pricing systems and quotation templates into a process the expert can oversee. For underwriters already overwhelmed by incoming business, the shift from operating every step to orchestrating the workflow can create capacity not just to work faster, but to consider risks that would otherwise go unreviewed. This is why I believe meaningful human oversight has to be designed into the workflow, not added at the end. Bas makes the accountability point explicit: the ultimate quotation belongs to the underwriter. That person needs to know what the agent did, which assumptions it made and what evidence informed its recommendation. An agent can prepare the work and identify uncertainty, but the human must be able to challenge it and own the decision. Trust in this environment depends on making the reasoning and source material accessible enough for judgement to remain real. For me, the practical lesson is to begin with friction and measure the business outcome rather than becoming fixated on tokens or technology costs alone. Bas's 'efficiency by a thousand cuts' could begin with something as modest as inbox triage, then compound through small improvements that save time, recover capacity and create new opportunities. Leaders should give their people room to test these tools, while designing appropriate safeguards as the technology evolves. The advantage will not come from an ambitious transformation announcement. It will come from improvements that earn trust, become part of the work and make skilled professionals more effective. BEST MOMENTS “What I found is that it is much better to basically achieve efficiency by a thousand cuts.” – Bastiaan de Goei [07:28] “You become essentially an orchestrator of the underwriting process, rather than the operator of one.” – Bastiaan de Goei [15:42] “The ultimate quotation is the underwriter's responsibility and that person is accountable for that decision.” – Bastiaan de Goei [19:52] “Now, if I'm able to identify two or three submissions that would otherwise have been ignored, I can now use my extra capacity to underwrite them. I have just paid for myself.” – Bastiaan de Goei [27:47] “I would deliberately not do large scale transformations that take 18 months.” – Bastiaan de Goei [34:48] “Believe in your people.” – Bastiaan de Goei [38:07] ABOUT THE GUEST Bastiaan de Goei leads financial services marketing at OpenAI, working with insurers and banks to translate advanced AI capabilities into useful, accountable workflows. His career spans AXA Group, where he worked across retail, life and specialty insurance, wealth management and banking, before relocating to San Francisco to support AXA Next's transformation efforts. He later joined Instabase, focusing on the industry's unstructured-data challenges. A Dutch-born economist with a PhD in business economics from the University of Cambridge, Bas brings an insurance-industry perspective to technology adoption. His work focuses on connecting technological capability with professional judgement, business value and trust, while expanding the capacity of experienced teams rather than replacing their expertise. He has also written about the sector through his Insurtech Blog. 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 [email protected].

Your next AI pilot will not fix an operating model that cannot scale it. Two rooms. Two cities. One verdict. In this solo episode of Scouting for Growth, I connect the conversations from AI Horizon 2030 in Las Vegas and Majesco’s Frontier Customer Summit in New Orleans, where two very different groups arrived independently at the same conclusion: AI capability is increasingly available. The operating model required to turn it into measurable results is not. The evidence reinforces the point. Research conducted with my students at Bayes Business School found that 26 of 30 leading carriers have yet to cross the halfway mark on both the People and Operating Model transformation axes. In Las Vegas, executives rated their own readiness to run agents in production at just over three out of five. The technology is moving quickly. Most organizations remain stuck in the middle. But readiness is not simply about becoming more technologically sophisticated. Boston Mutual moved from nine-day policy issuance to same-day issuance by redesigning the work around its platform. Manulife transformed a heavily customized model into something repeatable and scalable, while increasing straight-through processing for digital claims from 80% to 98%. What matters is not another isolated capability. It is whether the organization can redesign workflows around the outcomes intelligence now makes possible. I frame the practical response around three moves: name the accountable owner, choose the operating model before the model, and make trust demonstrable. Every agentic initiative needs a named executive with authority over the outcome. AI needs to sit inside an end-to-end workflow rather than beside it. And organizations need to be able to explain who authorized an action, whether it was permissible, what produced the decision, and which human ultimately carries accountability. Because when intelligence becomes widely available, access to intelligence stops being the differentiator. The advantage moves to the organization around it: how decisions are made, how humans and agents share work, how trust is governed, and how quickly a promising experiment can become a repeatable operating model. KEY TAKEAWAYS I want to challenge the assumption that another AI pilot will move an organization closer to becoming AI-ready. The conversations in Las Vegas and New Orleans pointed to the opposite conclusion. Most insurers do not appear to be constrained primarily by access to models, vendors, or technical capability. They are constrained by how work is organized around those capabilities. When intelligence becomes increasingly inexpensive and widely accessible, the more important question moves from “Where can we use AI?” to “How must the work change?” That shift starts with the customer workflow and works backwards. This is why accountability, workflow design, and trust have to be treated as part of the operating model rather than as implementation details. Every agentic initiative needs a named owner with the authority to change what happens next. The AI itself needs to operate inside a redesigned end-to-end process, supported by clear identity, entitlements, decision rights, and auditability. And trust must be demonstrable. If an agent takes an action, leaders should be able to explain who authorized it, whether the action was permissible, which data and model informed it, and which human carries accountability. For me, the principle is increasingly human in the lead, not simply human in the loop. The practical implication is deliberately simple: start with one owner, one process, and trust you can prove. Boston Mutual and Manulife show why this matters. Their progress did not come from layering intelligence over unchanged workflows. They redesigned the work and connected operational improvement to a better customer or member outcome. Readiness is therefore not a product an enterprise can purchase. It is a leadership choice about how the organization will operate when intelligence is everywhere. The firms that make that choice deliberately will move from isolated frontier moves toward becoming frontier firms. BEST MOMENTS “Insurance does not have an AI problem. It has an operating model problem.” – Sabine VanderLinden [01:56] “When intelligence is nearly free, you stop competing on accessing it because everyone has access to it. You start competing on the operating model wrapped around it.” – Sabine VanderLinden [07:02] “You can launch an MVP for one client. You cannot scale with one.” – Sabine VanderLinden [09:08] “Readiness is not something you buy. It is something you design.” – Sabine VanderLinden [10:42] “On top and beside is a chatbot with a budget line. Inside, the workflow is a redesigned business process where humans and agents share the work.” – Sabine VanderLinden [12:46] “When intelligence is free, what exactly is your operating model for?” – Sabine VanderLinden [16:53] 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 [email protected]

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 [email protected]

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 [email protected]

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 [email protected]

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 [email protected]

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 [email protected]

Denise Garth: The Frontier Insurer, One Year In Denise Garth returns to Scouting for Growth one year after the Frontier Firm idea began to move from a strategic aspiration into insurer operating models, technology roadmaps, and boardroom priorities. As Chief Strategy Officer at Majesco, Denise offers a sharper test for the Frontier Insurer: Is the organization simply using AI? Is it redesigning selected processes around AI? Or is it rebuilding the enterprise around intelligence, connecting its data, core platforms, people, partners, and decisions? Only the third represents genuine transformation. The uncomfortable truth is that most carriers now have AI enthusiasm, pilots, and isolated productivity gains. Far fewer have the intelligent core, embedded governance, and human-agent operating model required to scale them. Majesco’s research captures the disconnect: 82% of insurance leaders believe AI will define the industry’s future, yet only 14% have integrated it into their operations. Sabine and Denise explore what has changed over the past twelve months, why intelligent orchestration now matters more than another point solution, and how insurers can move from experimentation towards a genuinely redefined operating model. The conversation is for insurance CEOs, COOs, CROs, CDOs, transformation leaders, and technology founders navigating the move from AI experimentation to an intelligent, governable operating model. KEY TAKEAWAYS One year on, I see the conversation around AI in insurance moving from “What is this technology?” to a much more consequential question: “What does this mean for the business?” The 82%-14% gap tells us that the challenge is no longer access to technology. It is leadership. Most insurers are experimenting, but experimentation alone will not create a Frontier Insurer. The real opportunity is to rethink the business around the outcomes we want to create for customers, employees, and shareholders. I also believe we need to stop thinking about AI as something we bolt onto existing workflows. The Frontier Insurer embeds intelligence into its core, connects high-quality, accessible data with workflows and agents, and builds governance and auditability into its architecture. This changes what people actually do. Claims professionals can move from repetitive adjustment towards risk reduction and customer empathy; underwriters can become more focused on portfolio management and judgment; and employees can spend more time on knowledge-based work rather than manual processing. The biggest shift, however, is towards orchestration. I am increasingly convinced that the winners will not be the insurers with the most AI pilots or the biggest AI budgets. They will be the organizations that can bring together intelligence, governance, and core systems into a coherent operating model, while building trust along the way. Becoming a Frontier Insurer means being bold enough to rethink organizational structures, jobs, processes, technology partnerships, and ultimately the economics of insurance. The question is no longer whether AI will change insurance. It is whether leaders are prepared to redesign their businesses around it. BEST MOMENTS “Every insurer in 2026 will tell you that they are doing AI. Ask your harder question. Who has rebuilt the business or operating model around it?” – Sabine VanderLinden [00:16] “The gap isn't a technology story. It is a leadership story.” – Sabine VanderLinden [00:16] “It isn't about cutting people. It's about elevating them.” – Denise Garth [14:49] “It is about a rip and replace of business capabilities and the operating model.” – Denise Garth [43:34] “Somebody else is going to jump ahead of you because they're going to look at it from an orchestration standpoint and rethink.” – Denise Garth [38:19] “Redefined their operating model and embraced AI in a way that accelerated that redefinition of the operating model.” – Denise Garth [95:10]   ABOUT THE GUEST Denise Garth is Chief Strategy Officer at Majesco, where she leads strategy, marketing, industry relations, and innovation in support of the company’s client-centric vision. She works closely with insurers, technology partners and the wider industry ecosystem to identify emerging shifts and translate them into practical priorities for property and casualty, life, annuity and benefits organizations. An internationally recognized insurance and InsurTech thinker, Denise focuses on the forces reshaping the industry—from AI, data, and intelligent core platforms to new operating models, changing customer expectations, and ecosystem-based growth. Her work challenges insurers to move beyond incremental modernization and build organizations capable of learning, adapting, and delivering value continuously.   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 [email protected]

Bárbara Fernández: Human-Agent Collaboration, Agentic AI, and the Rise of the Human Boss The future of work will not be won by humans who move faster. It will be shaped by humans who know how to orchestrate intelligence. In this episode of Scouting for Growth, Sabine VanderLinden sits down with Bárbara Fernández, Deputy Director of MAPFRE Open Innovation and Head of insur_space, to explore one of the defining leadership questions of the agentic AI era: what happens when humans stop doing every task and start directing systems of intelligence instead? As AI assistants evolve into autonomous collaborators, work is no longer simply about increasing productivity. It is about preserving judgment, accountability, and human agency while learning how to collaborate with increasingly capable machines. Together, Sabine and Bárbara examine where the line sits between augmentation and abdication, why convenience can quietly erode critical thinking, and how organizations must rethink trust, behavioral design and customer interaction as AI becomes embedded into everyday decision-making. They also explore what insurance looks like when protection becomes predictive, contextual, and continuously present. This conversation is essential listening for insurers, transformation leaders, AI strategists and executives preparing their organizations for the next era of human-agent collaboration. KEY TAKEAWAYS One of the most important lessons from this conversation is that the future of work is not about competing with AI but learning how to direct it responsibly. As intelligent agents become increasingly capable of reasoning, coordinating, and executing tasks, our value shifts from producing outputs ourselves to asking better questions, challenging assumptions, and exercising sound judgment. Critical thinking is becoming one of the most valuable leadership capabilities we can develop. Bárbara also reminded me that the greatest risk is not artificial intelligence itself but our willingness to stop questioning it. Humans naturally gravitate towards convenience, and AI makes it remarkably easy to accept recommendations without scrutiny. If we allow productivity to replace thoughtful decision-making, we risk automating poor judgment rather than improving it. Human oversight must remain an active discipline, not a passive checkpoint. Our discussion also highlighted how customer interaction will fundamentally change as AI agents begin acting on behalf of individuals. Behavioral science, user experience, and trust must evolve beyond designing for human interfaces alone. Organizations will increasingly need to design services that are understandable to both humans and intelligent agents while maintaining transparency and accountability throughout every interaction. Ultimately, this conversation reinforced that technology will not replace what makes us uniquely human. As intelligence becomes abundant and inexpensive, empathy, emotional understanding, curiosity, and ethical judgment become even more valuable. The leaders who thrive will be those who know when to rely on AI—and when human presence is irreplaceable. BEST MOMENTS "We need to really stop and think and evaluate what the machine is saying to us." – Bárbara Fernández  "We need to train critical thinking." – Bárbara Fernández  "If something is making your day easier—I don't know if it's better—but at least easier, you will adopt that." – Bárbara Fernández "The danger is not bad AI. The danger is passive humans." – Sabine VanderLinden  "We need to open our minds, be curious and play with it." – Bárbara Fernández  "You need to keep learning... because if you don't understand what's behind it, you can't challenge assumptions." – Bárbara Fernández  "You cannot design trust. You have to demonstrate it." – Bárbara Fernández  "Trust is something very fragile. It takes a lot of time to build and one second to lose." – Bárbara Fernández  "If we are predictive, we act before. It means insurance becomes a prevention service." – Bárbara Fernández  "When intelligence becomes abundant, presence becomes premium." – Sabine VanderLinden  "We really need to find the proper questions and ask difficult questions." – Bárbara Fernández  "Machines can simulate emotions, but they don't have feelings—and that's something uniquely human." – Bárbara Fernández    ABOUT THE GUEST Bárbara Fernández is Deputy Director of MAPFRE Open Innovation and Head of insur_space, MAPFRE’s insurtech accelerator. She works at the intersection of strategy, innovation, behavioral science, and emerging technology, helping MAPFRE explore how new interaction models, AI assistants, and intelligent agents will reshape business, customer experience, and organizational design.   Her work focuses on how companies adapt to structural shifts in artificial intelligence, human-machine collaboration, decision-making systems, and the future of interaction. Through strategic foresight and behavioral science, Bárbara helps leaders understand not only how technology evolves, but how humans adopt, trust, challenge, and collaborate with it.   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 [email protected]