Supply Chain AI Has an Execution Problem

AI is already standard equipment in supply chain software. It drafts forecasts, flags exceptions and answers planners' questions in plain language. The open question, though, is whether it's improving their decisions.

The early evidence is mixed. Gartner predicted last year that more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Those are execution failures, and supply chain is where they get expensive fastest.

Supply chain teams decide under real constraints. Plants have finite capacity. Inventory is limited. Lead times matter. Customer commitments compete with production realities, and cost, service, labor and transportation objectives interact in ways unique to each business. AI makes that complexity easier to work with. But easier to work with doesn't automatically mean better decisions, and closing that gap is the industry's challenge right now.

Generative AI excels at interaction. It helps people prepare data, ask questions in natural language and explore scenarios without becoming optimization specialists. However, many supply chain questions need something more: a system that evaluates possible actions against operational constraints and determines what is actually feasible.

This story was originally published by Supply Chain Dive. Read the full story at Supply Chain Dive →