AI is only the visible tip of a much deeper enterprise architecture. Beneath every successful AI initiative lies a massive, disciplined data foundation—anatomy, taxonomy, ontology, semantics, governance, lineage, quality, master data, data contracts, interoperability, feature stores, MLOps, knowledge graphs, RAG, and agents.

This session reveals the full Data‑First vs AI‑First iceberg model and shows why organizations that chase AI without building the underlying data mass experience failure rates above 80%.

Attendees will learn how to architect the deep layers that make AI reliable, explainable, governable, and scalable across the enterprise. If you want AI that works in production—not just demos—this session is your blueprint.