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.