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.

Academic medical centers occupy a unique position in the AI landscape, often combining advanced research capability and technical talent with deeply established clinical and operational governance structures. This session explores how these IDNs approach AI adoption in supply chain, including where internal development provides strategic advantage, how experimentation is balanced with institutional caution, and how leadership evaluates which opportunities warrant investment versus partnership or procurement. While academic systems may have greater technical capacity, they also face complex decision-making environments that influence the pace and direction of adoption.

Through an interview-style discussion with an academic supply chain leader, this session will examine how research-driven environments navigate AI implementation decisions differently than traditional IDN models.

Learning Objectives:
1. Compare AI build-versus-buy decision-making between academic medical centers and non-academic IDNs. 
2. Describe how in-house technical and research capacity influences AI development cycles and iteration speed. 
3. Analyze how organizational culture impacts prioritization, risk tolerance, and adoption pathways. 
4. Evaluate which AI implementation lessons from academic systems are transferable to broader IDN environments.

At multi-state scale, AI adoption is shaped less by access to tools and more by the complexity of operating across multiple regions, platforms, and governance structures. This session examines how large IDNs are approaching AI implementation in environments characterized by fragmented data systems, inconsistent operational processes, and distributed decision-making authority. The discussion follows a practical adoption pathway beginning with high-value, low-clinical-risk use cases such as contract and rebate optimization, before expanding into more complex operational and clinical applications. It also addresses the governance requirements needed to move beyond pilot activity, including CFO and IT alignment, data access considerations, and the operational implications of AI-driven decision support.

Through an executive interview format with supply chain leaders from large multi-state systems, this session will explore how IDNs are building scalable AI governance and avoiding stalled or duplicated pilot efforts.

Learning Objectives:
1. Sequence AI deployment strategies by prioritizing high-value, low-risk use cases before expanding into clinical applications. 
2. Diagnose data fragmentation challenges across ERP, CLM, purchase order, and invoice systems in large IDNs. 
3. Construct governance approaches that align IT, finance, and supply chain leadership around AI implementation. 
4. Anticipate organizational and clinical change-management impacts associated with scaled AI adoption.