As enterprises move from AI experimentation to production, the limiting factor is increasingly not model capability, but the readiness of the data those models depend on. Most enterprises already have vast amounts of data, but it is often fragmented across databases, APIs, documents, and applications, with gaps in semantics, governance, connectivity, and discoverability. Building reliable AI systems therefore requires more than selecting the right model or adding an agent layer, it requires an enterprise data foundation designed for AI to query, learn, reason, and act on.
The talk presents a practical blueprint for building an AI-ready enterprise data foundation, with a focus on how data, knowledge, governance, and AI applications come together as an integrated ecosystem. It explores the architectural choices and mindset shifts needed to build this foundation incrementally, rather than treating AI readiness as another layer on top of existing systems. It also examines two complementary directions: Data for AI, which prepares enterprise data to support AI, and AI for Data, where AI helps prepare, enrich, and continuously improve the data foundation itself.
Agenda
5:30 – 6:00 PM – Registrations
6:00 – 7:30 PM – Talk followed with QnA
7:30 – 8:00 PM – Networking and Refreshments

