🚀 Native-AI Supply Chain Planning: from “brownfield optimization” to “greenfield reinvention.” Reading Lora Cecere's latest article reminded me that our industry is standing at a true inflection point. For years, supply chain planning has been built on rigid architectures, relational databases, pre-defined hierarchies, deterministic logics. Systems that required pristine data and delivered insights… too late. The emergence of Native-AI supply chain planning platforms changes the paradigm. Not by adding AI agents to old tools, but by rebuilding planning around learning systems, semantic data layers, and interoperability. What stands out in Lora’s blueprint is this simple truth: 👉 We must unlearn before we can learn again. Redefine “demand” as real consumption, not orders. Treat “variability” as a signal, not as noise. Manage flows rather than time-phased volumes. This approach moves us from buying planning platforms to designing planning protocols, a subtle but radical shift. As consultants, technologists, and leaders, our role is to bridge this transition: from functional optimization to systemic orchestration.