Scouting for Growth

Patrick Van Deven: The Frontier Firm Has a Data Problem In this episode of Scouting for Growth, Sabine VanderLinden sits down with Patrick Van Deven to unpack one of the biggest hidden blockers to becoming a true AI-native enterprise: legacy data infrastructure. As organizations rush toward the “Frontier Firm” vision championed by Microsoft — intelligence on tap, human-agent collaboration, and AI-powered workflows — Patrick argues that most regulated industries are still running on fragmented data pipelines built decades ago. Beneath the excitement around agentic AI lies a critical operational reality: data remains horizontally distributed across systems such as SAP, Salesforce, Guidewire, and legacy warehouses, stitched together by opaque code that no one fully understands anymore. Patrick explains why the future of AI in regulated industries depends less on flashy copilots and more on deterministic, governed, audit-ready data transformation. Drawing from his 35 years in enterprise software and his leadership at Volspeed, he outlines how AI is now reshaping data engineering itself — automating the “plumbing” layer while generating the metadata and lineage AI systems need to operate responsibly. Together, Sabine and Patrick explore why re-architecting does not require a dangerous core system replacement, how organizations can solve tractable business problems in months rather than years, and why the next generation of enterprise leaders must bridge business expertise and data intelligence. This conversation is a practical roadmap for any executive navigating AI transformation inside complex, regulated environments. KEY TAKEAWAYS What stood out most to me in this conversation with Patrick was the reality that the “Frontier Firm” conversation is no longer about experimentation. It is about operational readiness. Every organization I speak to wants intelligence on tap, agentic workflows, and AI-enabled productivity, yet many are still constrained by fragmented legacy systems and undocumented data logic buried deep inside their infrastructure. Patrick made it very clear: if we do not solve the data foundation problem, we simply accelerate complexity and risk. One insight that resonated deeply was the idea that data engineering is entering the same transformation that software engineering experienced with generative AI. The real opportunity is not just automation, but abstraction — enabling smaller teams to solve historically impossible integration problems while creating governed, machine-readable metadata that AI systems can actually trust and consume responsibly. I was also struck by Patrick’s perspective on talent. Rather than replacing expertise, AI elevates the importance of subject matter experts who understand the business context behind the data. The future belongs to professionals who can bridge operational understanding with technical fluency and collaborate effectively with AI-enabled systems. Most importantly, this conversation reinforced that becoming a Frontier Firm does not require ripping out every core system overnight. The no-regret move is to start solving tractable, high-value data problems now — especially those tied to governance, lineage, regulatory reporting, and customer intelligence. Organizations that modernize their deterministic data layer today will be the ones capable of building scalable, trustworthy AI tomorrow. BEST MOMENTS “You can bolt all the AI you want on top of that. It will not make you a frontier firm. It will just make your regulatory problems arrive faster.” — Sabine VanderLinden “AI is coming to data engineering just like it came to software engineering.” — Patrick Van Deven “The board looks at AI at the end of the value chain of data. But how did that data come to be?” — Patrick Van Deven “There is no world where a company would run on one system.” — Patrick Van Deven “Treat the AI agent like an employee. Onboard it, brief it, give it a personality.” — Sabine VanderLind

The App Era Is Over: Wallet-Native Insurance & the Agentic Frontier — Marc Lampe × Ernesto Suarez In this episode of Scouting for Growth, Sabine VanderLinden sits down with Ernesto Suarez and Marc Lampe to explore why the future of insurance is moving beyond apps and into wallet-native, AI-ready experiences. The conversation begins with a powerful reminder of why customer experience matters: a traveler stranded abroad, unable to prove they had insurance in an emergency. From there, the discussion unpacks the hidden friction embedded across the insurance journey — especially in claims, servicing, and customer engagement. Ernesto shares how Gigasure was designed as a digital-native travel MGA focused on mobile-first engagement, instant gratification, and removing the traditional “handoffs” that frustrate policyholders. Marc explains how Wallet Studio, developed by Miss Moneypenny Technologies after nearly a decade of experimentation, enables insurers to create dynamic wallet-based insurance experiences that sit directly alongside boarding passes, payments, and loyalty cards. Together, they reveal how the partnership rapidly launched over 50,000 digital wallet cards in just a few months, achieving remarkable customer engagement and demonstrating that insurance can become proactive, contextual, and genuinely useful. The episode also dives into parametric claims, embedded insurance, MGA innovation, AI-enabled customer journeys, and why ecosystem collaboration — not disruption alone — is shaping the next era of InsurTech. KEY TAKEAWAYS What struck me most in this conversation is how both Ernesto and Marc are solving an issue the industry has talked about for years but rarely fixed: making insurance truly accessible and useful at the exact moment customers need it most. We often talk about “customer experience” in insurance, yet too many journeys still rely on PDFs buried in inboxes, disconnected claims processes, and handoffs between providers. This discussion showed what happens when founders design around real human behavior instead of legacy systems. I was particularly fascinated by the simplicity and power of wallet-native insurance. Consumers already use wallet technology every day for boarding passes, payments, loyalty cards, and transport tickets. Integrating insurance directly into that ecosystem feels obvious once you see it in action. The results speak volumes: more than 50,000 wallet cards issued within months and exceptionally high customer engagement rates. That tells us customers are ready for insurance experiences that are frictionless, visible, and mobile-first. Another important insight is how the MGA model is evolving. Ernesto highlighted how modern MGAs are increasingly powered by specialist InsurTech enablers rather than trying to build every capability themselves. The future is less about disruption in isolation and more about intelligent collaboration, integration, and speed to market. This partnership demonstrates how insurers, MGAs, and technology providers can create far more value together than separately. Finally, I loved the honesty around AI and the “agentic frontier.” Both guests acknowledged that technology alone is not enough. The real challenge is guiding customers through increasingly complex ecosystems in ways that remain trustworthy, intuitive, and human-centered. The winners in this next phase of insurance innovation will be the companies that combine intelligent automation with seamless customer trust. BEST MOMENTS “The era of the app, as we have known it, is over.” — Marc Lampe “88% said they have trouble finding their documents.” — Ernesto Suarez “Insurance has never been tangible. And I feel like this is a little piece that we can give customers for what they’ve purchased.” — Ernesto Suarez “The solution is not to build the perfect AI-driven functionality, but to deliver that actually to the customer.” — Marc Lampe “We’re all very good at selling, but it’s the post-sale se

What if the biggest opportunity in insurance isn’t pricing risk—but transforming it? Alan Martin brings a bold, necessary reframe to the life and health insurance industry: the future belongs to insurers who move beyond actuarial prediction and into active health orchestration. At the center of this shift is his concept of modifiable risk—the idea that many health outcomes are not fixed, but can be influenced through timely, personalized, and scalable interventions. For decades, insurers have operated within a reactive model: Assess risk at underwriting Pay claims when events occur Offer limited, often disconnected support But this model is breaking down under the weight of rising chronic disease, mental health challenges, and post-pandemic shifts in customer expectations. Alan exposes a critical flaw: most health propositions fail because they don’t engage. Low engagement → high cost per use High cost → reduced investment Reduced investment → poor customer experience This “engagement-cost doom loop” is reinforced by outdated service models—like generic nurse helplines—that lack personalization, digital access, and effective triage. Instead, Alan argues for a fundamentally different approach: 1. Intervene at the moment that matters most The point of diagnosis or claim is where behavior can change. Yet insurers are often absent. This is where personalized pathways, digital triage, and embedded services must come into play. 2. Redesign wellness to include everyone—not just the healthy Today’s programmes often reward those already fit. True innovation targets high-risk populations with affordable, scalable interventions that deliver measurable outcomes. 3. Build economic models around health improvement Modifiable risk enables: Dynamic pricing linked to behavior change New product innovation Reduced claims through prevention This is not philanthropy—it’s commercially viable prevention. 4. Embrace embedded health ecosystems Through platforms like CareVoice, insurers can orchestrate care journeys—connecting policyholders to the right services at the right time, seamlessly. 5. Rethink risk appetite for a new world Post-pandemic realities demand new assumptions: AI-driven insights Rising chronic disease burdens Increased focus on mental health Risk is no longer static. It’s dynamic, behavioral, and deeply human. This episode challenges insurers, startups, and policymakers alike to rethink their role—not as payers of claims, but as partners in health outcomes. Because the real question is no longer: How do we price risk more accurately? It’s: How do we reduce it—at scale, sustainably, and profitably? This episode is essential listening for: Insurance executives redefining product and risk strategy Healthtech founders building engagement and care platforms Policymakers shaping preventive health systems Innovation leaders designing embedded ecosystems Investors seeking scalable models in health and insurance So here’s the challenge: If you could influence risk before it becomes a claim… why wouldn’t you build your entire business model around it?

On this episode of Scouting For Growth, Sabine VdL welcomes Marinela Profi, Global Market Strategy Lead for AI, GenAI, and Agentic AI at SAS, for a sharp, grounded conversation on what’s actually happening in enterprise AI right now—and what leaders need to prepare for next. Together, they cut through the noise surrounding generative AI and focus on what comes after the chatbot era: agentic AI. If generative AI is the talented communicator in the room, agentic AI is the one who not only speaks—but takes action, executes workflows, and delivers outcomes. Marinela puts it simply: Generative AI talks. Agentic AI does. The episode begins by reframing a major misconception: LLMs alone don’t solve business problems. While generative AI chatbots are excellent at answering questions, summarizing content, and producing text, they typically stop at conversation. Business transformation, however, requires systems that can reason, make decisions, interact with data, follow rules, coordinate across tools, and carry tasks through to completion. That’s where agentic AI steps in—combining large language models with analytics, policies, data pipelines, governance frameworks, and real operational logic. Marinela explains that AI agents aren’t a futuristic fantasy—they’re a practical evolution of automation, made smarter through contextual understanding and orchestrated decision-making. To help business leaders and technical teams understand what “agent behavior” looks like in real life, she shares her 5-step lifecycle framework—a clear model for how agents operate end-to-end: Perception – sensing signals from users, systems, or environments Cognition – reasoning, interpreting context, and forming intent Decisioning – selecting the best course of action based on goals and constraints Action – executing tasks across workflows and tools Learning – improving over time through feedback and outcomes But the most important message in this episode isn’t just that agents are powerful—it’s that autonomy must be designed responsibly. Marinela emphasizes that the real leap forward for enterprises won’t come from more impressive demos. It will come from governance, because trust is becoming the true competitive advantage in AI. She forecasts that by 2026, governance boards will increasingly resemble digital oversight committees—not just approving AI deployments, but ensuring agents are safe, accountable, explainable, auditable, and continuously monitored. A critical insight: governance doesn’t end when an agent is launched. Performance and behavior must be monitored continuously, particularly as agents learn from human feedback loops. Marinela warns that learning mechanisms can’t be left unchecked—because allowing an agent to “self-update” in uncontrolled ways is not innovation, it’s operational risk wearing a futuristic costume. The conversation also tackles one of the biggest leadership questions emerging right now: How autonomous should an AI agent be? Marinela’s answer is refreshingly practical: most of the time, it depends on the risk and impact of the task. Low-risk activities may allow higher autonomy, while high-impact decisions demand constraints, oversight, and transparency. As she highlights throughout the episode, autonomy without accountability is a risk multiplier. Ultimately, this episode is a strategic guide for leaders who want to move beyond AI experimentation into reliable execution. The future isn’t just about faster answers—it’s about autonomous, governed intelligence that can explain what it’s doing, why it’s doing it, and who is responsible when it does. If your organization is wondering what comes after GenAI pilots, how to build AI trust at scale, or what enterprise AI will look like by 2026—this is the conversation to listen to. Because the winners in AI won’t be the ones with the flashi

On this episode of Scouting For Growth, Sabine VdL sits down with Rob Schimek, Group CEO at bolttech, to unpack how the company’s connector model is reshaping global insurance distribution — not through more paperwork, but through smarter ecosystems that meet customers where they already are. If you’ve ever wondered what happens when insurance stops being a product you buy and starts becoming protection that’s simply… there, this conversation is your preview. Insurance distribution is being rebuilt (and it’s not starting with carriers) Rob explains bolttech’s mission in one powerful phrase: closing the multi-billion-dollar global protection gap — a gap that isn’t shrinking, it’s widening. The reason? Too many people and businesses still lack the right coverage because protection remains too complex, too expensive, too fragmented, or too hard to access. bolttech’s answer is to create a seamless connection between: Insurance providers (who build protection products), and Distribution partners (who already have customers — think telcos, retailers, auto makers and platforms) This is B2B2C distribution at scale: tailored, affordable, accessible coverage — delivered with convenience, not friction. Rob makes the point that the more you remove friction from connections, the more protection becomes adoptable… and the faster the protection gap closes. The real differentiator: trust, data, and design This episode goes beyond business models into something more strategic: what makes protection work in the real world. Rob is clear — the future of insurance won’t be won by premium tables and policy wordings. It will be won by: Trust (because adoption depends on it) Data (because personalization depends on it) Design (because customers won’t tolerate clunky experiences anymore) And data is the unlock. Rob highlights how real-time information — like vehicle telematics — enables insurers to move away from “paint everything with one brush” pricing and instead reflect the actual risk of the individual in front of you. For enterprise leaders, the implication is huge: the winners will be those who can turn data into relevance, and relevance into trust. Leadership: stay obsessed with the problem One of the most memorable leadership lessons from Rob is this: If you have an hour to solve a problem, spend 55 minutes understanding the problem and 5 minutes designing the solution. He’s spent his career deep in the “problem,” and bolttech represents the path he’s chosen to bring solutions to market at global scale. That focus matters, because when the mission is crystal clear, distractions don’t stand a chance — even when markets get noisy. AI only works where trust exists Rob also delivers a critical reminder for every executive racing toward automation: If customers don’t trust how AI is used in their experience, AI won’t be accepted — and therefore won’t succeed. Trust isn’t a tagline. It’s a prerequisite. Why this matters now For insurers, brokers, and platform partners alike, this episode is a blueprint for the next era of distribution: embedded, frictionless, data-driven protection, delivered through ecosystems customers already rely on. Because by 2030, the most successful insurance brands won’t just sell protection. They’ll make it effortless to access — and impossible to ignore.

On this episode of Scouting For Growth, Sabine VdL sits down with Stephen Brittain, Co-Founder of InsurTech Gateway — the world’s first authorised venture builder and fund dedicated to insurtech — to explore what it really takes to build startups inside one of the most regulated, risk-averse industries on the planet. And yes… that industry is still insurance. (The land where “move fast and break things” gets politely escorted out by compliance.) Stephen has spent the last decade doing something most people talk about but few actually pull off: bringing early-stage innovation into the heart of large insurers — without killing the startup spirit in the process. He’s been a catalyst for corporate innovation leaders and a strategic guide for founders trying to navigate the labyrinth of regulation, procurement, and distribution at scale. From product design to mastering risk Stephen’s journey starts in product and service design, where he saw risk as the ultimate constraint. But instead of avoiding it, he leaned in. His curiosity became a turning point: what if understanding risk wasn’t a blocker… but the unlock for doing bigger and bolder work? That insight is what led him into insurance — not because it was “sexy,” but because it holds powerful, often underestimated capabilities: creating trust, enabling lending, and building mutual models that scale across ecosystems. The real job of a venture builder: keep the idea alive long enough to evolve InsurTech Gateway was born with a mission: find exceptional founders and help fast-track them into market with enough momentum (and resilience) to survive an environment where early ideas are too often treated as fixed commitments. But Stephen makes a critical point for every founder and innovation leader: no one gets it right on day one. Innovation is a learning journey, and the ventures that win are the ones that have the room—and the support—to adapt as reality hits. One of the biggest challenges in insurtech isn’t starting strong. It’s ensuring something that looked brilliant at launch actually evolves into the opportunity it promised. The missing link: connectivity between insurers and venture capital Stephen shares a blunt truth: the entrepreneurial excitement gets everyone moving, but on hard days, the question becomes sustainability. Because innovation doesn’t scale on enthusiasm alone — it scales when incentives align, capital supports the long game, and distribution becomes real. And historically, that alignment has been rare. Stephen highlights a structural disconnect: VCs and insurers haven’t traditionally sat at the same table, even though the future demands they build together. The good news? Pattern recognition has never been higher, and the cost of experimentation has never been lower. We can spot what works faster than ever before. The challenge is still the same: can you validate it with an insurer and get them truly onside? That’s where venture builders like InsurTech Gateway play a pivotal role — acting as the bridge between speed and scrutiny, ambition and regulation, creativity and sustainability. Why this matters for leaders right now For Fortune 500 executives, this episode is a reminder that innovation doesn’t fail because people lack ideas — it fails because they don’t understand risk well enough to scale them responsibly. For founders, it’s a roadmap for navigating the insurance operating system: if you can work with innovators, understand risk, and unlock sustainable adoption, you don’t just build a startup… You build something that lasts. Because the future of insurance won’t be shaped by the loudest demo. It will be shaped by the ventures that can earn trust, survive regulation, and scale in the real world.

On this episode of Scouting For Growth, Sabine VdL is joined by two powerhouse voices shaping the future of enterprise transformation: Ulrich (Uli) Homann, Corporate Vice President at Microsoft, and Mark Luquire, EY Global Microsoft Alliance Co-innovation Leader. Together, they unpack a question every insurance and financial services executive is quietly asking right now: How do you build an agentic AI enterprise that doesn’t just move faster… but gets smarter — and works for everyone? From rigid automation to outcome-driven intelligence Uli and Mark draw a clear line between yesterday’s automation and today’s agentic AI reality. Traditional automation was task-driven and brittle: workflows had to follow a fixed sequence, and you needed to know that sequence in advance. If anything changed, the whole process broke or required manual intervention. Agentic AI flips that model. Instead of obsessing over steps, leaders can now focus on the outcome — and allow intelligent systems to help figure out how to get there, even when conditions shift mid-process. In other words, enterprises can begin to rethink not only how processes run… but which processes even need to exist. That’s not incremental change. That’s operational reinvention. What happens when everyone gets access? Mark shares a critical EY lesson: when EY gave broad access to generative AI across the organization early on, people didn’t just use it to speed up tasks. They used it as a thought-partner — a way to sharpen ideas, improve deliverables, and accelerate the quality of work. Productivity moved up. Confidence moved up. Curiosity moved up. And then the tech evolved again. Mark reflects on the rapid shift from early GenAI tools to today’s emerging agent capabilities — systems that can now write code, build applications, and take action with very little prompting. That evolution opens the door to a new enterprise reality: AI that doesn’t just assist… it executes. Or as the conversation makes crystal clear: It’s not just providing information anymore — it’s taking on work, processing decisions, and driving momentum. The leadership mindset shift: “start with where you want to be” One of the biggest takeaways from this episode is refreshingly simple—and extremely hard to do in practice: Focus on where you want to be… then rethink how you’re going to get there. That’s the key to unlocking agentic AI at scale. Because most organizations try to bolt AI onto existing workflows, legacy systems, and operating models. But agentic AI forces a different question: What if the old workflow is the problem? What if your best path forward is not optimization — but redesign? Why this matters for enterprise leaders (especially in insurance) For C-suite leaders, this episode is a strategic blueprint for what’s coming next: an enterprise where AI becomes a true digital workforce, operating alongside humans — amplifying expertise, reducing friction, and accelerating execution. But the goal isn’t speed for speed’s sake. It’s building intelligence that creates measurable business value and earns adoption across the organization — from the boardroom to the front line. Because the future won’t belong to companies that deploy the most AI tools. It will belong to the ones that build an agentic enterprise that’s trusted, scalable, and designed to work for people — not around them.

On this episode of Scouting For Growth, Sabine VdL sits down with Yo Kwon, CEO of Voltaire.Claims, to shine a spotlight on one of the most underestimated pain points in insurance operations — and why fixing it can unlock serious financial and regulatory upside. Because while the industry loves talking about AI in underwriting, fraud, and customer experience… the real bottleneck is often buried in the back office. And yes — it’s the humble claims letter. The problem hiding in plain sight Yo shares how Voltaire.Claims started almost by accident. While testing AI applications for broader enterprise use, he noticed someone using their tool to write claims correspondence. That became the “wait… THIS is the problem?” moment. Claims adjusters don’t enjoy writing letters — especially denial letters. And when productivity is measured (as it always is), speed often wins over compliance. Adjusters rely on templates, cheat sheets, and copy-paste workflows, which makes it dangerously easy for small mistakes… and big ones… to slip through. The catch? In claims, correspondence isn’t admin. It’s evidence. It can determine whether a carrier stays compliant, avoids disputes, and prevents lawsuits. What Voltaire does differently: no shortcuts, no sloppy language Voltaire doesn’t just populate a template. It generates each claims letter from scratch, based on the actual claim context and policy language — reducing the risk of errors and misapplied clauses. But the real breakthrough is the guardrails. If an adjuster requests a denial letter and no valid policy exclusion exists to support it, the system stops the process and returns a message like: “No relevant policy language was found.” That one moment of friction can prevent: wrongful denials compliance violations reputational damage and the kind of disputes that turn into litigation This is what Yo means when he says: compliance is a product feature, not an afterthought. The economics are hard to ignore The financial case is as sharp as the operational one. Yo highlights that litigation alone adds an average of $10,718 per claim in loss adjustment expense. Voltaire estimates it can reduce litigated claims by 10% or more simply by producing clearer, more defensible correspondence. Even a conservative 5% improvement in leakage through better letters can translate into meaningful recovered value — not by working harder, but by communicating smarter. AI that improves the workforce, not just the workflow One of the most unexpected outcomes? Claims managers and adjusters told Voltaire that the AI was teaching them policy details they’d never known before. That’s the hidden advantage of well-designed enterprise AI: it doesn’t just automate tasks. It upgrades capability. It creates consistency. It protects the business from avoidable mistakes—at scale. Yo’s big insight is also Sabine’s warning to every leader: if you assume this problem should already be solved in 2025… you’re not alone. But claims complexity has grown faster than operational tools. And that’s exactly why this category is now ripe for reinvention. Why this matters for enterprise leaders If you’re a claims leader, COO, transformation exec, or innovation sponsor inside an insurer, this episode is a practical reminder that the next wave of competitive advantage won’t only come from shiny AI pilots. It will come from fixing the high-volume, high-risk workflows that quietly drive: compliance exposure loss costs customer trust and operational drag Because in insurance, trust isn’t built in slogans. It’s built one letter at a time.