Most product teams have added AI features. Fewer have rebuilt their product process around AI as the core engine for discovery, specs, research, and decision-making. This panel explores what changes when AI moves from feature to foundation. Panellists share what's actually working (and what isn't) when AI stops being a bullet point on the roadmap and starts reshaping how the whole product team operates. Attendee takeaways - The difference between “AI features” and an AI-native product process - Where AI is genuinely changing discovery and specs, not just execution - Practical first steps for teams still treating AI as a bolt-on

Most product teams treat product-market fit as something you find once and move on from. In reality, for any company operating across more than one market, it's closer to a continuous, multi-year process of pivots, one that never fully settles. This panel challenges the "PMF as milestone" mental model and replaces it with a more honest one. Panellists bring the enterprise view: products built centrally often get commercialised very differently region by region, shaped by local regulation and customer expectations rather than the product team's original assumptions. The panel digs into why a product that wins convincingly in one region can fail to scale into another for reasons that have nothing to do with the product itself, and what that means for how teams should actually define and track fit. Attendee takeaways: - Why treating product-market fit as a one-time milestone sets teams up to misread their own results - How regulation and regional customer expectations reshape commercialisation, even when the product stays the same - A more useful way to track fit continuously, rather than declaring it "done"