The U.S. less-than-truckload freight industry faces its biggest operational shake-up in decades. Starting July 19, 2025, new National Motor Freight Traffic Association (NMFTA) rules will shift from commodity-based to density-based freight classification—a change that threatens to overwhelm shippers with compliance complexity.
C.H. Robinson is responding with a sophisticated AI solution that automates freight classification while supporting an expanding ecosystem of generative AI tools across the company's operations.
Key Takeaways
The incoming NMFC Docket 2025-1 represents a fundamental restructuring of how LTL freight gets classified. Beyond the shift to density-based classification, NMFTA is consolidating over 2,000 commodity listings into a much smaller number of categories, grouping similar items under single classifications.
For shippers, this means navigating new weight and dimension requirements while adapting to entirely different classification frameworks. According to NMFTA research, classification errors already cost the industry over $1.2 billion annually in delays, re-billing, and operational inefficiencies.
"Many LTL shippers are unaware or uncertain of the classification for their freight," explains Greg West, vice president for LTL at C.H. Robinson. "This is bound to increase with the massive overhaul of the national LTL freight classification system."
C.H. Robinson's response goes beyond traditional automation. The company has deployed over 30 AI agents performing tasks that previously required human intervention, with newer agents designed to support existing AI systems.
"We're building AI agents that help our AI agents," says Arun Rajan, chief strategy and innovation officer at C.H. Robinson. This layered approach addresses the complexity of freight classification, where accurate class determination requires analyzing multiple variables including size, weight, handling requirements, and now density calculations.
The results are measurable: C.H. Robinson's AI agent can classify freight in 10 seconds for new shipment types, dropping to just 3 seconds after additional training from LTL experts. Compare this to the 10+ minutes required for manual classification, and the efficiency gains become clear.
Similar intelligent automation solutions, like Trax Technologies' Audit Optimizer, demonstrate how AI can transform traditionally manual freight processes into automated workflows that improve both speed and accuracy.
The most significant impact targets small-to-medium businesses that rely heavily on email for freight orders. Before AI deployment, only 50% of C.H. Robinson's LTL orders processed through automation, primarily via direct integrations or shipper platforms.
Now, with AI agents handling email-based shipping requests, over 75% of all LTL orders achieve automation. The AI agent currently processes approximately 2,000 LTL orders daily, saving over 300 hours of manual work.
This transformation addresses a critical gap in freight technology adoption. While large enterprises typically implement sophisticated transportation management systems, SMBs often lack resources for complex integrations. AI agents bridge this gap by working with existing communication methods while delivering enterprise-level automation benefits.
McKinsey research indicates that SMBs represent 60% of LTL volume but generate 80% of classification errors due to resource constraints and manual processes.
The broader implications extend beyond immediate efficiency gains. Accurate freight classification directly impacts pricing, delivery speed, and carrier relationships. Misclassified freight triggers costly delays as carriers hold shipments for inspection, adjust invoices, or apply penalties.
By automating classification, C.H. Robinson improves speed-to-market for customers while reducing friction in LTL transactions. This positions the company advantageously as the industry adapts to new NMFTA requirements.
The approach also demonstrates how AI deployment can create competitive moats. Rather than replacing human expertise, the system augments LTL specialists by handling routine classification while escalating complex cases for human review.
C.H. Robinson advises shippers to ensure accurate weight and dimension data, partnering with dimensioner vendors to offer equipment discounts. This proactive approach recognizes that successful AI implementation depends on data quality—a principle that extends across freight technology applications.
As the July 19 deadline approaches, the success of C.H. Robinson's AI agent deployment will likely influence how other major logistics providers address NMFTA compliance. The company's layered AI approach—agents supporting agents—may become the industry standard for managing complex operational changes.