Regional IDNs represent the most common operating model in U.S. healthcare, yet they face some of the highest barriers to successful AI adoption due to limited in-house technical capacity, constrained IT resources, and competing operational priorities. This session follows the real-world journey of a regional IDN moving from initial AI awareness to practical implementation, including how leadership evaluated early use cases, made procurement decisions when no clear “best” tool existed, and secured executive sponsorship in an environment where strategy and technology expertise are not always co-located. The discussion also examines how external forces, including cloud ERP modernization and automation pressure points, are accelerating adoption timelines whether IDNs are fully ready or not.

Through a moderated interview with supply chain and technology leaders, this session will explore how regional systems are translating AI ambition into executable initiatives while balancing infrastructure and workforce constraints.

Learning Objectives:
1. Assess build-versus-buy considerations for AI adoption in resource-constrained regional IDNs. 
2. Identify operational dependencies, including staffing, IT infrastructure, and change management, that influence adoption success. 
3. Examine how enterprise system modernization is accelerating AI adoption across supply chain functions. 
4. Translate an AI adoption framework into a practical supply chain use case from selection through measurement.