C.H. Robinson: Inside Supply Chain AI Implementation

C.H. Robinson has introduced technology that assesses and operates supply chains autonomously. The system was built for the company's 4PL Managed Solutions customers.

According to C.H. Robinson, the technology handles 92% of 4PL shipments across trucking, ocean, air and rail. It manages freight from order creation through tendering, routing, delivery, exceptions and carrier payment.

Assessment completed in minutes

The Lean AI Engineer can assess a supply chain in 25 to 30 minutes and determine improvements before performance degrades. This replaces assessments that take up to four weeks and analyse past events.

The system works alongside the Lean AI Planner, introduced in 2025. The Lean AI Engineer delivers analysis while the Lean AI Planner manages shipments through hundreds of interconnected agents.

Execution data feeds back to the Lean AI Engineer to develop refinements. Jordan Kass, President of Managed Solutions at C.H. Robinson, says: "It will run continuously, improve the operation it's running and heal itself when something breaks – without an alert or a human noticing a problem first."

The Lean AI Planner executes in real time while the Lean AI Engineer studies results, identifies patterns and adapts logic. According to Jordan, the technology ends the need for separate supply chain intelligence and orchestration tools.

Encoding expertise into operations

Supply chain service has depended on people to manage complexity, make decisions and intervene during disruption.

Jordan adds: "The problem was that talent didn't scale. We've changed that by encoding expertise in the technology itself. Shippers will get infinite talent and expertise, consistently applied across every shipment, regardless of who's available in what time zone or how much their shipping volume grows or spikes. Their team and our team can focus on strategic priorities and driving the best business results."

The system depends on the data and context it can access. C.H. Robinson has 450 in-house software engineers and data scientists.

The proprietary context layer was built by capturing knowledge from workflows. This data comes from freight experts and feeds the model on an ongoing basis.

The technology uses data on all steps of shipping end to end, rather than the parts that separate tools see.

Training on operational context

The system was trained on context from orchestrating freight, including details about goods, procedures, pickup and delivery locations, carriers, routing and risk tolerance.

Jordan says: "That's how the Lean AI Engineer knows which improvements are right for you, instead of making generic or theoretical recommendations."

If a manufacturer ships cross-border to a just-in-time assembly line five days a week, the system will not suggest saving money by shipping once a week. The technology takes into account more variables than human analysis or software analysis can process.

Recommendations are prioritised and actionable for users. At launch, the Lean AI Engineer will identify optimisations and hidden savings for businesses.

One early adopter learned that switching from a varied shipping schedule to once a week would reduce loads by 17% across 20 locations. According to C.H. Robinson, this change delivered an annual savings of more than US$1m.

Monitoring carrier behaviour patterns

Another customer reorganised shipments so that one pickup serves three different delivery locations. This adjustment cut total loads by 81% and saved the company 40% in costs.

The Lean AI Engineer will roll out to more customers to begin assessing other factors like carrier performance. By continuously monitoring carrier behaviour across lanes, transportation modes and customers, it will identify leading indicators of degrading performance.

This could allow the system to recommend corrective actions before service failures happen. Arun Rajan, Chief Strategy and Innovation Officer at C.H. Robinson, notes how supply chains do not suffer from a lack of information.

Arun explains: "They suffer from the gap between knowing and doing. Tech that sits above or outside of a supply chain can aggregate data, harmonise signals and recommend. But it relies on someone else to execute on the signals and someone else to learn whether those actions worked."

According to Arun, the system closes the gap by delivering 24/7 service with one system.