How AI Erased a $184 Billion Supply Chain Blind Spot

Enterprise software has spent a decade telling companies where their supply chains are breaking down. The harder problem—deciding what to do about it and acting on it without waiting for a human to notice—is where artificial intelligence (AI) is moving next.

C.H. Robinson, a third-party logistics (3PL) company, launched its Lean AI Engineer on June 3, a system that assesses an entire supply chain in 25 to 30 minutes and identifies improvements before performance degrades, down from the four weeks traditional methods require. Its Lean AI Planner already autonomously manages 92% of the company’s global fourth-party logistics (4PL) shipments across truck, ocean, air and rail. Rather than flagging problems for review, it executes decisions across hundreds of interconnected AI agents in real time.

The results are specific. One early adopter cut loads by 17% across 20 locations, saving more than $1 million annually, by switching to a consolidated weekly shipping schedule. A second reorganized pickups so one stop served three delivery locations, cutting loads by 81% and reducing costs by 40%. Those are the kinds of gains that traditional assessments surface after weeks of backward-looking analysis. The new system surfaces them in under 30 minutes and acts on them before performance slips.

The Gap Between Seeing a Problem and Fixing It Carries a Price Tag

Supply chain disruptions cost businesses approximately $184 billion annually as of 2025, according to a study from global consulting firm J.S. Held, reported by NetSuite. Much of that cost doesn’t come from the disruption itself. It comes from the lag between when a problem is identified and when someone acts on it. The Hackett Group estimated that $1.7 trillion in global working capital is tied up in excess inventory, held as a buffer precisely because companies lack the real-time data to set inventory levels with confidence rather than padding.

McKinsey found that manufacturers who improved supply chain visibility achieved a 15 to 20% improvement in inventory turns and reduced expedited-service costs by 30 to 50%. Those gains translate directly into working capital release and margin protection.

Jordan Kass, president of C.H. Robinson Managed Solutions, said in a statement the system is built as one closed loop. “It will run continuously, improve the operation it’s running and heal itself when something breaks,” Kass said, “without an alert or a human noticing a problem first.” The new system holds historical and current data simultaneously across an entire network and looks forward rather than backward.

Goods and Manufacturing Lead the Enterprise Agentic AI Adoption Surge

The move from visibility to execution is happening across the enterprise, not just in logistics. PYMNTS reported that goods and manufacturing enterprises saw one of the biggest jumps in agentic AI implementation, going from almost no usage in August to nearly one in four firms using or piloting the technology by November.

The same pattern is showing up in procurement. Coupa acquired workflow automation platform Tonkean in May to build what it called an agentic trade network — a system that reads documents, routes approvals, connects suppliers and executes pieces of the transaction flow without manual handoffs at each step.

The governance question follows the capability question closely. Autonomous systems that influence inventory availability, purchase order timing and supplier payment cycles need institutional knowledge behind them, not just data. C.H. Robinson’s system was built by capturing expertise from the company’s freight specialists and feeding it to the model continuously. The AI runs the operation, but the judgment it draws on came from people. That distinction between a system that executes and one that executes with context is where the practical gap between early adopters and the rest of the market is opening.

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