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.