Warehouse automation decisions are often based on assumptions about future volume, labor, SKU mix, workflows, and facility requirements. The challenge is that those assumptions rarely stay fixed over the life of an automation investment.

Using a practical, nine-step framework and real examples from demand and supply planning, including AI-generated scenarios that surface capacity risks before a Supply Review even begins, JT will show what's possible today, what's still emerging, and how to avoid the governance pitfalls that derail AI adoption.