AI success is rarely about starting from scratch. It is about using existing data capabilities effectively. This session introduces a practical framework for understanding data strategy maturity and how different levels of maturity enable different types of analytics and AI outcomes.

The talk walks through low, medium, and high data maturity environments, showing how data architecture, pipelines, data quality practices, and ownership models support increasingly sophisticated analytics and AI use cases over time. Rather than promoting a single end state, the session highlights realistic opportunities available at each stage, along with the tradeoffs teams navigate as they evolve.

Using anonymized, real-world case studies, attendees will see how teams aligned their data strategy with their AI goals, what technical changes helped unlock new capabilities, and how incremental improvements created momentum. Developers and technical leaders will leave with a clear mental model for assessing their current environment, identifying AI opportunities that fit their maturity, and making intentional decisions that set their teams up for sustainable AI success.