AI initiatives often struggle not because of model sophistication, but because the underlying data supply chain has not been mapped, synchronized, or aligned with operational decision-making. Just as a physical supply chain cannot function without visibility, coordination, and flow from source to destination, AI systems cannot create value when data sources are siloed, inconsistently governed, or disconnected from the “last mile” of decision execution. Even well designed models stall when the upstream data inputs are unreliable or the downstream decision processes are unclear.

This talk reframes AI implementation through the lens of the hidden data supply chain that propels it - from data sourcing and acquisition, to transformation, integration, governance, and delivery into decision environments. We will explore the continuous loop between data engineering and AI development, showing how model requirements should shape data architecture from the outset, and why data pipelines must be engineered as critical infrastructure rather than reactive fixes. Participants will leave with a practical framework for mapping their data supply chain, identifying bottlenecks and failure points in the data-to-decision flow, and building resilient data architectures that support reliable, explainable, and production ready AI systems.

Featuring Rosemarie Santa González, Ph.D., Research Scientist with the H. Milton Stewart School of Industrial and Systems Engineering and the NSF AI‑CARING Institute at Georgia Tech, and an instructor in the SCL professional education program.