AI Doesn’t Know What Your Planners Know

The conventional narrative for AI failure in supply chains often focuses on data quality and system integration issues, but this misses the core problem.
The real issue is that AI systems lack an understanding of the “operational context,” which includes unwritten rules and informal practices that inform decision-making in supply chains.
This gap in context means AI recommendations don’t align with the reality of how supply chains operate, leading to frequent overrides by experienced planners.
To be successful, AI implementations in supply chains must first undertake genuine operational discovery to understand how decisions are actually made by the staff on the ground.
Successful AI deployments start small, focus on specific contexts, engage knowledgeable practitioners early, and build trust based on understanding rather than top-down mandates.
Even with good infrastructure, the underlying problem is ensuring that AI systems account for the specific, and often undocumented, operational practices.
Ultimately, the readiness of the technology is less of a challenge than ensuring the AI knows enough about the specific operational context to be trusted by the staff who will use it.