Moving from AI experimentation to enterprise impact remains one of the biggest challenges in healthcare today. In this session, UNC Health shares how they built and scaled a programmatic approach to AI and automation, from early roadblocks to system-wide adoption.

Expectations placed on the data office have shifted faster than its mandate. Leaders are no longer judged on report delivery, but on whether data work materially protects margin, improves operational efficiency, and supports enterprise change such as M&A, platform consolidation, or new care models. Yet many teams remain stuck in a service posture, reacting to inbound demand with little authority to challenge priorities or stop low-value work. Without explicit ownership of outcomes, prioritisation rights, and clear success measures, even mature teams regress into ticket queues. The point is not tooling, it is redefining what the data office is accountable for, and what it is allowed to say no to.