Across healthcare, AI initiatives stall not on the model, but on the data underneath: petabytes of records without clear ownership, meaning, retention, or business context. The cost shows up everywhere. Analytics teams reverse-engineer definitions, AI outputs no one trusts, regulatory exposure on data no one needed to keep, and infrastructure spend scaling faster than value. Meanwhile, the data office is asked to govern AI at scale on an estate it can't fully see, with a mandate that rarely matches the authority it's been given.

This session presents a working approach for making the data estate safe, usable, and economically efficient at scale. Drawing on enterprise deployments across regulated industries, the session will show how establishing business context across the data estate, and acting on it, gives data leaders the authority, visibility, and economic story they need to govern AI at scale, while converting data from a liability into a defensible foundation the CFO will fund.

Key Takeaways:

• Why context, not model sophistication, is the rate-limiting factor for healthcare AI, and why the CDO is the only role positioned to fix it

• How to apply Protect, Purge, Prosper to convert the data office from reactive service desk to evidence-based strategic function

• The economics of data minimization: a CFO-grade cost-avoidance and compliance-scope story the CDO can take to finance and actually get funded

• How to sequence classification, disposal, and AI investment so each phase builds CDO authority and self-funds the next, turning three competing demands into one coherent program