C.H. Robinson: AI Agents Cut Logistics Inefficiencies by 42%

The gap between AI adoption and meaningful value creation has become increasingly apparent across industries.

While organisations rush to implement AI solutions, few are achieving the transformative results promised by the technology.

C.H. Robinson's deployment of AI agents to manage missed pickups in less-than-truckload shipping could offer a blueprint for how companies can bridge this divide.

The initiative centres on a dual-agent system that automates what was previously a manual, time-intensive process plaguing the logistics sector.

The company, which moves more LTL freight than any other third-party logistics provider in North America, has automated 95% of checks on missed pickups.

The system saves more than 350 hours of manual work per day while reducing unnecessary return trips by 42%.

Dual agent architecture delivers results

The technology employs two AI agents operating in tandem.

One agent contacts carriers about missed pickups while another determines the appropriate response.

The parallel processing capability allows the system to make 100 calls and 100 decisions simultaneously, potentially resolving missed pickups faster than human-led processes.

"Before this transformational tech, teams of people spent over half the day chasing missed pickups – manually checking carrier websites, making calls, recording updates and notifying customers," says Greg West, Vice President for LTL at C.H. Robinson.

"Now that all that time and capacity aren't being wasted, it keeps other shippers' freight from getting delayed."

The agents resolve hundreds of shipments per day across more than 11,000 customers within C.H. Robinson's network of 37 million annual shipments, representing US$23bn in freight.

The system also generates operational intelligence for carriers, identifying which electronic communications could be improved and enabling schedule optimisation.

This data feedback loop represents a secondary value stream beyond the immediate automation benefits.

The value creation challenge

The productivity improvements align with broader patterns in AI implementation.

According to PwC's 2025 Global AI Jobs Barometer, industries most exposed to AI, including logistics, have seen productivity growth nearly quadruple, up 27% compared to those that have not adopted the technology.

However, a McKinsey 2025 report found that whilst 88% of organisations use AI, only 6% are capturing meaningful enterprise value.

These "high performers" are characterised by their ability to fundamentally redesign workflows rather than simply overlaying AI onto existing processes.

This disparity highlights why C.H. Robinson's approach could be significant.

The missed-pickup agents are not merely digitising an existing manual process but reimagining the operational framework around AI's capabilities.

The challenge facing most organisations is identifying where AI can deliver genuine operational transformation rather than incremental improvements.

C.H. Robinson's focused methodology demonstrates that success lies in targeting specific pain points where automation can fundamentally alter how work gets done, creating measurable value rather than simply digitising existing workflows.

Lean AI methodology

Mark Albrecht, VP for AI at C.H. Robinson, describes the deployment as demonstrating what the company calls Lean AI.

"We don't just throw AI at anything and everything," Mark says. "We use AI agents only where they can deliver tangible business results.

"Our Lean AI processes helped us uncover the extent of time wasted in handling missed pickups and where artificial intelligence had the most potential to augment our automation software."

The methodology prioritises identifying specific operational pain points where intelligent automation can generate measurable returns, rather than pursuing AI adoption for its own sake.

This targeted strategy has enabled C.H. Robinson to build a portfolio of more than 30 AI agents, including others dedicated to LTL operations such as price quotes, orders, freight classification, shipment tracking and proof of delivery.

The operational challenge these agents address stems from the complexity of LTL shipping.

With one lorry carrying freight from up to 20 different shippers, the process involves collecting multiple shipments, transporting them to a terminal and recombining them on other lorries heading in the same direction.

"A missed pickup isn't just a minor inconvenience," Greg explains.

"When a lorry arrives and the freight or packaging isn't ready, or the carrier couldn't make it because they got stuck in traffic, it forces another lorry to come back the next day.

"That might not even be our shipper's freight, but it creates a domino effect for other freight that was supposed to get picked up and for all the other lorries down the line."

The technology's ability to address this cascading complexity while generating operational insights for carriers could signal how AI implementations can create value across interconnected business processes.

As the system scales, it could demonstrate whether targeted AI deployment can succeed where broad adoption has struggled to deliver returns.