As artificial intelligence (AI) becomes increasingly embedded in healthcare, organizations face the dual challenge of ensuring both the accuracy and equity of AI-driven clinical decision support. This presentation explores ECU Health’s journey to mature its AI monitoring and governance capabilities. We detail our transition from model-centric oversight to problem-first monitoring, emphasizing the importance of embedding technical resources within governance structures and aligning threshold selection with real-world workflow realities. By restructuring our AI governance according to our AI maturity roadmap, we aim to streamline decision-making, prioritize the most impactful problems, and foster a continuous cycle of improvement as we are adopting learning health system concepts. Attendees will gain actionable insights into building sustainable, scalable monitoring practices, leveraging modular automation, and ensuring responsible, equitable AI deployment across diverse clinical workflows.