Supply chain planning has evolved technologically, but its underlying processes remain rooted in historic practices that no longer fit today’s volatile, complex environment. Packaged applications promise faster, lower-cost, lower-risk business outcomes, yet extensive customization, fragmented data, and rigid workflows often undermine that promise. Cloud data and AI platforms like Databricks and Snowflake offer an alternative: build interoperable applications on top of existing planning systems to address gaps in direct procurement, plan consumption, bullwhip reduction, operational planning, and dynamic master data. The opportunity is not simply to automate existing processes with AI and agentics, but to redefine how work gets done through end-to-end orchestration, value-based trade-offs, and clear decision governance.

Remember the story of the frog in the pot of water? If the water temperature rises slowly, the frog doesn’t recognize the danger until it is too late. Put the frog directly into boiling water, and it jumps.

I think the analogy applies to today’s supply chains.

Companies are absorbing increasing volatility and complexity without fundamentally redefining how work gets done. Companies have confused historic practices with best practices. They are now taking those same processes and attempting to automate them with agents and agentic AI—without first stopping to ask a more fundamental question: Is it time to jump? Here I give a five-step action plan.