Every enterprise is investing in AI, but few have solved the prerequisite: a single, governed source of customer truth that AI can reliably reason over. Today, customer data remains fragmented across CRMs, point-of-sale systems, loyalty platforms, and countless SaaS tools – each holding only part of the picture. AI initiatives are often built on these incomplete views, creating inconsistent outputs and compounding governance challenges. This is the “Context Gap,” and it quietly undermines AI before it delivers value.

This talk introduces a different approach. Drawing on real-world enterprise deployments, it shows how leading brands transform their existing data platforms into unified AI foundations. By combining identity resolution, zero-copy architecture, and a connected context layer, organizations can unify customer, operational, and performance data into a single, governed system.

The result is not another experiment, but a scalable, always-on engine that drives measurable growth from data you already own.

• Your data platform is your AI platform. The enterprises winning at AI aren’t deploying new infrastructure. They’re extending the warehouse they already own into a governed context layer that both humans and AI agents can reason over
• Identity is the invisible unlock. AI can’t personalize, decide, or optimize for a customer it can’t identify. Warehouse-native identity resolution is the foundation that makes every downstream AI initiative more effective
• Context, not models, is the moat. The difference between AI that drives growth and AI that generates noise is whether it can see the full picture: customer data, campaign history, content, brand rules, and business context, all governed and all in one place