Most AI initiatives fail not because the technology is immature, but because organizations treat AI as a set of tools, not as an operating capability.

Enterprises launch pilots, dashboards, and proofs of concept, yet struggle to scale them into measurable client and business value. The root cause is rarely data or models - it’s the absence of a clear, client-centric AI operating model.

Drawing on my work leading enterprise-wide AI transformation inside a global industrial organization, I will show how companies can move from fragmented AI experiments to a Client-Centric AI Factory - an operating model where AI, automation, and agentic systems are aligned around real client and business outcomes.

We will explore:

- why most AI initiatives stall after the pilot phase,

- how to structure AI governance, ownership, and accountability,

- and how to design AI systems that integrate into core processes instead of sitting on top of them.

The session focuses on practical leadership decisions, architectural patterns, and real-world lessons that help organizations turn AI from a cost center into a scalable, value-generating capability.