Leveraging artificial intelligence to support clinicians is no longer a future-state aspiration—it’s today’s strategy for improving care quality, reducing adverse events, empowering care teams, and minimizing time spent in the electronic health record (EHR). At Houston Methodist Hospital, we’ve launched and scaled an innovative evidence-based quality improvement initiative that brings this vision to life. This session offers an inside look at how we’re combining real-time AI risk models with AI-generated chart summaries and hands-on care navigation to reimagine post-discharge care coordination. Framed as a formal QI project, the initiative began with a baseline analysis of post-discharge outcomes, revealing that the most at-risk patients (top risk quintiles) accounted for 70% of 30-day post-discharge deaths. Building on the success of our initial pilot, we deployed AI-powered risk stratification tools and redesigned workflows to prioritize timely follow-up for high-risk patients—ensuring they are seen within 14 days of discharge, when intervention is most impactful. The result is a marked increase in follow-up rates, a decline in mortality and readmissions, and a sustainable model for delivering precision care across the continuum. Whether you're a health system executive, physician, nurse, or informatics leader, this session offers a replicable framework for launching AI-powered quality improvement initiatives that deliver measurable results, strengthen clinical teams, and improve patient outcomes. This work represents not just a pilot or a one-time intervention, but part of an ongoing commitment to continuous quality improvement—refining processes, deepening insights, and evolving care delivery to meet the needs of tomorrow’s patients.