What if AI isn’t your competitive advantage—and never will be? In this episode of Scouting for Growth, Sabine VanderLinden sits down with Rich Edwards, CEO of Mindspan and former product leader at IBM Watson, to dismantle one of the most persistent myths in AI-driven transformation: that algorithms are where differentiation lives. They’re not. Rich brings a refreshingly contrarian—and deeply practical—perspective shaped by decades at the intersection of machine learning, regulated industries, and real-world execution. His core belief is simple but uncomfortable: as AI becomes commoditised, your advantage won’t come from the model you buy—it will come from the data you already own and how responsibly you activate it. This conversation cuts through the hype. Rich explains why most organisations are sitting on vast reserves of underutilised first-party data, particularly in banking and insurance, and why those data assets—when governed correctly—can unlock entirely new levels of customer value, operational intelligence, and trust. You’ll hear why: Generative AI is rapidly becoming a utility, not a differentiator First-party data is the only asset competitors can’t copy AI is not a single “thing” but a pipeline of components working together Treating AI like an all-knowing brain is a category error The real risk isn’t AI itself—it’s poor data governance Rich uses a powerful analogy to reframe AI’s role: think of it less like a genius and more like a highly capable summer intern—fast, eager, scalable, but entirely dependent on the context, guardrails, and judgement you provide. From there, the discussion moves into territory many leaders are only beginning to confront: ethics, compliance, and responsibility. In sectors where personal and sensitive data is foundational, Rich argues that governance is not a blocker to innovation—it’s the enabler. Facial recognition, behavioural data, and predictive insights all raise questions that technology alone cannot answer. This episode is especially relevant for leaders asking: How do we stand out when everyone has access to the same AI tools? What does “responsible AI” actually look like in practice? How do we turn data into an operating asset—not a liability? Where should we invest when technical talent is scarce and change is accelerating? Rich also shares lessons from his own journey—how being the person who “knew how to get things done” inside IBM put him at the forefront of Watson, and why buying and leading Mindspan became a natural next chapter in focusing on impact over novelty. 🎧 If you’re an executive, founder, or investor who suspects that the AI arms race is missing the point—this episode will validate your instinct and sharpen your strategy. Because in a world where algorithms are everywhere, data stewardship, context, and trust are what will separate leaders from followers. And growth, as ever, belongs to those who understand the difference.