C.H. Robinson Debuts AI to Run Global Supply Chains

In a first-of-its-kind launch, C.H. Robinson is pioneering an AI technology to operate a shipper’s global supply chain while continuously assessing and improving its performance. 

The global logistics provider has built the system to serve its 4PL Managed Solutions customers. 

The newly introduced Lean AI Engineer will work in concert with the Lean AI Planner, introduced in 2025, to create one connected system that uniquely enhances a supply chain as it runs.

The technology is autonomously handling 92% of 4PL shipments globally across trucking, ocean, air and rail. It manages freight from the moment an order is created through tendering, routing, delivery, exceptions and carrier payment.

Supply chain assessments completed in minutes

The Lean AI Engineer can assess an entire supply chain in 25 to 30 minutes and determine improvements before performance is impacted. This replaces traditional supply chain assessments that typically take up to four weeks and look backward at past events. 

While the Lean AI Engineer delivers intel, the Lean AI Planner manages shipments through hundreds of interconnected AI agents. This execution feeds data back to the Lean AI Engineer to develop even smarter 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. Jordan explains that the technology ends the need for separate supply chain intelligence and orchestration tools.

Scaling logistics expertise through technology

Premium logistics service has traditionally depended on talented 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.” 

Success depends on the data and context the system can access. With 450 in-house software engineers and data scientists, the proprietary context layer was built by methodically capturing institutional knowledge from workflows. 

This data comes from seasoned freight experts and feeds the model on an ongoing basis.

Preventing generic logistics recommendations

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

It is trained on the unique context from orchestrating freight, including details about goods, procedures, pickup and delivery locations, carriers, routing and risk tolerance. 

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

If an auto-parts maker 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 advanced AI takes into account more variables than human analysis or typical software analysis can process. Recommendations are prioritised and actionable for users.

Delivering million-dollar customer savings

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. This change delivered an annual savings of more than US$1mn.

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 behavior across lanes, transportation modes and customers, it will identify leading indicators of degrading performance. 

This allows 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 generally 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. 

“Our tech closes the gap, delivering 24/7 premium service with one unified system no one else can match.”