Product-led organizations invest heavily in frictionless onboarding, relevance and engagement. Now they are racing to add copilots, assistants and agents. But no amount of product polish or AI can compensate for fragmented customer data, inconsistent definitions and disconnected business context. When an organization cannot reliably understand its customer and how they interact across products and channels, product-led strategies hit a ceiling. This session explores why data is no longer infrastructure beneath the product; instead, it is becoming the intelligence layer of the product itself. Drawing on lessons from enterprise-scale data and AI transformation, the speaker shares a practical framework for building products that understand context, make better decisions and act intelligently, while challenging the trend of shipping disconnected AI features without the foundations required to make them useful, trusted and differentiated. Attendee takeaways: - Why traditional product-led playbooks break down when customer context is fragmented across systems, products and business units - How to distinguish an AI feature from a genuinely intelligent product and what the difference means for competitive advantage - A practical framework for connecting trusted data, decision logic and agentic action into a coherent product intelligence layer - Where proprietary context and workflow ownership create a moat that foundation models alone cannot replicate