Most large AI organizations have a data problem that isn’t what they think it is. The problem isn’t missing data — it’s data that exists, was built deliberately, is maintained by real people, and still isn’t being reused. It sits in a pipeline nobody else can find.

This talk examines what happens when a large-scale AI organization shifts from a project-centric to a product-centric operating model — and discovers that building data products doesn’t automatically mean anyone benefits from them. Across a portfolio of more than 140 data products supporting five distinct AI strategies, the patterns are consistent: data scientists rebuild ingestion pipelines for data that already exists, reusable frameworks go undiscovered by the teams who need them most, and the inventory grows while the acceleration effect of reuse does not.

This is not a clean success story. We’ll walk through what the product-centric model solved, what it didn’t, and what the discovery and trust gap actually costs — in terms practitioners recognize: the project that started from scratch because no one knew a shared datamart existed; the parser framework that sat idle while three teams built their own. We’ll then cover what it takes to close the gap: building a searchable, unified context layer that makes data products findable, evaluable, and reusable without requiring every team to know what every other team built.

Practitioners will leave with a diagnostic framework: how to distinguish a discovery problem from a data quality problem, the leading indicators that your organization has an invisible asset layer, and the ordering of interventions that helps — starting with discoverability, then trust signals, then governance.