( The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.)
C.H. Robinson is having an AI moment. Its CEO, Dave Bozeman, has been publicly promoting the company’s Lean AI transformation, including AI agents, automation, productivity gains, appointment scheduling, quote responses, load tracking, and the broader message that C.H. Robinson is using technology to solve logistics problems at scale.
That may be impressive. It may be good business, and it may help C.H. Robinson move freight faster, improve margins, increase productivity, and serve customers more efficiently. But after Montgomery, the industry should be asking a harder question: is that same AI investment, data discipline, and human judgment being applied to carrier vetting?
Because if C.H. Robinson, or any digital broker, is building AI everywhere except the part of the business that decides which motor carriers are safe, legitimate, and suitable to put on the road, that is not innovation. That is exposure.
The timing is hard to ignore. C.H. Robinson just lost a unanimous Supreme Court case involving broker negligent selection. The claim was that C.H. Robinson negligently hired a motor carrier with a conditional safety rating and that the broker knew or should have known from that rating that selecting the carrier was reasonably likely to result in crashes that would injure others.
Now, almost immediately after that decision, C.H. Robinson is publicly emphasizing the sophistication of its AI transformation. That creates a fair question for the industry: if the company has the capital, data, systems, and technical discipline to automate and optimize so many parts of freight brokerage, why should carrier vetting remain the place where sophistication stops?
The Supreme Court did not say carrier vetting must be manual. It did not tell brokers to stop using technology, and it did not require brokers to become motor carriers, inspect trucks, audit driver qualification files, or supervise drivers. But the Court did make clear that negligent hiring claims against brokers are not categorically preempted when they involve motor vehicle safety, and the majority opinion framed the issue around ordinary care in selecting a carrier.
Justice Kavanaugh’s concurrence is especially important for digital brokers. He acknowledged that brokers may not always be in a position to objectively assess the relative safety of every motor carrier, but he also wrote that brokers may sometimes become aware that a particular carrier operates unsafe trucks or hires unfit drivers. He emphasized that brokers should be able to defend themselves when they act reasonably and arrange transportation with reputable carriers, and he quoted plaintiff’s counsel saying brokers should hire carriers with a reasonable policy and ask the hard questions of the carrier.
That language matters. It does not describe carrier vetting as a blind box checking exercise. It describes judgment, inquiry, escalation, and a reasonable response to information when the broker has it or should have had it. This is where AI alone can fall short. Carrier vetting has a human component because not every carrier with a risk indicator should be automatically rejected. If a broker tries to fully automate carrier vetting with rigid pass or fail rules, it may lose significant capacity and still miss the practical judgment required to understand whether a carrier is appropriate for a particular shipment.
The human element is not ignoring the data. The human element is asking the hard questions when the data shows something that matters. It is someone looking at the red flags, digging deeper, deciding whether the issue is disqualifying or manageable, documenting the reasoning, and applying appropriate controls if the carrier is still used.
Why does this carrier have no inspections despite its claimed operations? Why is the authority so new? Why does the contact information not match FMCSA records? Why is the carrier’s volume inconsistent with its apparent fleet size? Why are there related entities with concerning histories? Why is the payment information inconsistent? Why is there a carrier identity mismatch at pickup? Why does this carrier appear suitable for this shipment despite a risk indicator? Those are not questions an algorithm should hide. Those are questions technology should surface so the right person can make a documented and defensible decision.
I recently interviewed Michael Leizerman, the plaintiff attorney who won Montgomery at the Supreme Court. During that interview, I asked him directly about brokers using AI and large data sets to automate operations and assign carriers. His answer should concern every digital broker because he made a critical distinction between AI that improves safety and AI that merely increases speed.
Michael recognized that AI can be used to make the roads safer. It can help identify chameleon indicators, related entities, VIN associations, address overlaps, fraud patterns, and inconsistencies in carrier data. But he also warned about the opposite use case: AI that onboards carriers faster, expands capacity, and assigns freight without meaningful vetting.
That distinction should be the center of the post Montgomery AI conversation. The problem is not that C.H. Robinson is investing in AI. The problem is if C.H. Robinson, or any digital broker, is investing in AI to move freight faster, price freight smarter, automate operational tasks, increase productivity, and improve margins, while carrier vetting remains disconnected from that same data environment.
Digital brokers collect enormous amounts of information. They analyze lanes, rates, carrier behavior, shipment history, tracking performance, dwell time, acceptance patterns, fraud indicators, communication data, location data, payment data, and operational outcomes. If that data is used to optimize profit but not to identify carrier risk, that imbalance becomes difficult to defend.
The more data a company collects, the harder it becomes to argue that obvious risk indicators were unknowable. In litigation, the question will not sound technical. It will sound simple: you used AI to improve productivity, margin, tracking, appointments, and quoting, so why did you not use the same data and technology to identify whether the carrier was safe, legitimate, and suitable for the load?
The CAVRA Standard addresses this directly. It recognizes that technology has changed carrier vetting and that modern transportation providers now have access to safety data, fraud indicators, monitoring tools, onboarding platforms, tracking systems, and operational analytics that make carrier vetting faster, more consistent, more scalable, and easier to document. But CAVRA also states that automation is not the same as reasonableness, because technology can support a reasonable carrier vetting process but does not create one by itself.
That is the core point. A digital broker may have predictive carrier matching, AI assisted pricing, automated tenders, real time tracking, and operational AI agents. But if its carrier vetting process still reduces the inquiry to active authority, insurance, and not unsatisfactory, the technology does not solve the negligent selection problem. It may make the problem worse.
A modern broker should be able to explain whether its technology considers carrier safety data, flags conditional and unsatisfactory ratings, identifies new authority, recognizes limited or no inspection history, screens for chameleon indicators and related entities, detects identity inconsistencies, accounts for fraud and stolen authority risk, compares shipment risk to carrier suitability, triggers human review when elevated risk appears, prevents casual load by load overrides of adopted safety rules, and documents why the carrier was used.
If the answer is no, the company may not have a technology problem. It may have a standard of care problem. That distinction is especially important because carrier vetting is not limited to onboarding. CAVRA states that carrier vetting begins before onboarding but does not end there. Review should occur before first use, before tender when risk changes, during operations when red flags appear, and over time as the carrier profile changes. That principle matters for digital brokers because scale creates repeatable risk. If a weak carrier selection process is automated, the weakness does not disappear. It scales. That is why we automated it will not be enough. The better answer is that the company automated a reasonable process. A reasonable process does not mean every carrier with a risk indicator is rejected. It means the system identifies the risk, routes the issue to the right person, requires the hard questions, records the mitigating facts, applies appropriate controls, and documents the decision.
That is where the human element belongs. Not manually reviewing every carrier for every shipment. Not turning brokers into motor carriers. Not paralyzing operations. But ensuring that when a carrier presents meaningful safety, fraud, identity, or suitability concerns, a trained person can look deeper before freight is tendered.
C.H. Robinson’s AI push may be impressive. But after Montgomery, carrier vetting cannot be the place where sophistication stops. If the company wants credit for being a technology leader, it should expect scrutiny over whether that leadership extends to the part of the business that selects the motor carriers actually hauling freight on public roads. This is not anti AI. It is pro responsible AI. The freight industry needs better technology, and AI can make carrier vetting faster, more consistent, more scalable, and easier to document. It can help detect patterns that human users may miss, and it can help brokers preserve capacity by sending elevated risk to human review instead of relying on crude automatic disqualification.
But the technology has to be pointed in the right direction. Digital brokers cannot collect operational intelligence for profit and ignore risk intelligence for safety. After Montgomery, the future of defensible freight brokerage will not belong to companies that merely move freight faster. It will belong to companies that can show their technology helped them move freight responsibly.
Cassandra Gaines is a nationally recognized transportation attorney, expert witness, and founder and CEO of Carrier Assure, an industry leading carrier vetting platform. She provides expert analysis and testimony in broker liability, negligent selection, carrier vetting, FMCSA safety data, transportation safety analytics, cargo theft prevention, and broker standard of care matters for both plaintiff and defense cases. Gaines previously held legal and leadership roles at large brokerages and trucking companies and has become a leading voice on transportation safety analytics and broker standard of care issues. She is also the author of the CAVRA Standard, a practical carrier vetting framework that has been downloaded and reviewed by nearly 1,500 industry professionals. She has spoken at more than 100 industry conferences nationwide and was named one of Business Insider’s 100 People Transforming Business in North America. She can be reached at cassandra@logisticsriskexpert.com.
Source notes: This opinion piece references Montgomery v. Caribe Transport II, LLC, Michael Leizerman’s interview with Cassandra Gaines, the CAVRA Standard by Cassandra Gaines, and recent public reporting and company materials regarding C.H. Robinson’s Lean AI strategy and AI driven operational transformation.
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