AI initiatives often fail to scale; not because of the models, but because the underlying data foundation is manual, brittle, and difficult to evolve. Without an automated, governed approach to data delivery, organizations struggle with slow development cycles, inconsistent data, and mounting technical debt that limits AI’s impact.
 
In this session, Simon (Engaging Data) and Paul Watson-Gover (WhereScape) draw on real-world experience to examine why many AI programs stall after early pilots. They highlight common architectural and operational pitfalls, including hard-coded pipelines, unmanaged change, and a lack of repeatable delivery frameworks.
 
The session explores how an automated, metadata-driven data foundation enables trusted, AI-ready data at scale. Attendees will learn practical strategies to accelerate time-to-value, reduce delivery risk, and transform AI from isolated initiatives into a sustainable, repeatable capability supported by data warehouse automation.