AI Compliance for Broker Liability: SCOTUS Stakes

A single trucking crash in Illinois is now forcing the U.S. Supreme Court to answer a question that every broker, shipper, and carrier should care about: can states hold freight brokers liable for negligent carrier selection, or does federal law block those claims?

If you work in transportation and logistics, this isn’t “just legal news.” It’s a preview of the operating environment for 2026: more scrutiny on hiring and procurement decisions, more variation across states, and less tolerance for “we didn’t know.” The uncomfortable truth is that manual compliance programs don’t scale when the rules are moving and the data is messy.

This post breaks down the Supreme Court case, what it could change for broker and shipper exposure, and the practical piece most teams miss: AI in supply chain and procurement is quickly becoming the only realistic way to manage carrier risk, documentation, and state-by-state legal complexity at speed.

What the Supreme Court case is really about

Answer first: The case asks whether the Federal Aviation Administration Authorization Act of 1994 (F4A) prevents states from allowing lawsuits against brokers for negligent hiring / negligent selection of unsafe motor carriers.

The case is Montgomery v. Caribe Transport II, LLC. A truck accident victim sued not only the driver and the carrier, but also the broker involved in arranging the haul—arguing the broker selected an “unfit” carrier with a poor safety history. Lower federal courts ruled the claim was preempted by federal law under the F4A.

Now, 29 states plus the District of Columbia have filed an amicus brief urging the Supreme Court not to wipe out these state-law claims. Their core message is straightforward: states have traditionally regulated road safety and used tort law to deter unsafe practices, and Congress didn’t clearly say the F4A should erase that.

Why the states are united (and why you should care)

Answer first: The states are defending a tool they see as essential for road safety: the ability to impose consequences when a company’s choices help put unsafe trucks on the road.

What’s striking is the coalition itself. This isn’t a partisan alignment. It’s a practical one.

States are arguing that if negligent-selection claims disappear:

• Brokers face less pressure to screen carriers thoroughly
• The market rewards the cheapest option more aggressively (even when risk signals are obvious)
• Accident costs shift—to victims, insurers, and public systems—rather than to the decision-makers in the transaction

For logistics leaders, the issue is exposure. If the Court narrows preemption, carrier selection becomes even more like a regulated procurement decision with a paper trail that needs to stand up under scrutiny.

The operational reality: carrier selection is procurement

Answer first: Whether you call it “carrier onboarding” or “routing guide management,” it’s procurement—and courts treat it like a decision with foreseeable risk.

In the “AI in Supply Chain & Procurement” world, we talk a lot about supplier risk, auditability, and governance. Carrier selection fits that same pattern, except the failure mode is more immediate: a crash.

Most organizations still run carrier vetting with a mix of:

• static qualification checklists
• periodic insurance collection
• manual review of safety signals
• tribal knowledge (“we’ve used them before”)

That works right up until it doesn’t—especially during peak periods.

Seasonal pressure makes shortcuts more likely

Answer first: Peak shipping seasons create the conditions where negligent selection becomes more plausible.

It’s mid-December, and many networks are still clearing holiday volume and managing winter disruptions. When tenders get rejected and spot capacity gets tight, teams feel pressure to “just cover the load.” That’s when processes fray:

• a dispatcher accepts a carrier with thin documentation
• a broker bypasses a rule because “it’s only one run”
• a shipper looks away because service KPIs are on fire

Courts don’t care that it was peak season. Plaintiffs’ attorneys will argue the risk was predictable and the controls were inadequate.

The legal uncertainty is a data problem in disguise

Answer first: State-by-state differences in liability standards create a compliance mapping problem—and AI can turn that into an executable workflow.

The states’ brief emphasizes federalism and the long-standing role of states in road safety. Practically, that means the industry is stuck managing a landscape where:

• liability rules vary by jurisdiction
• legal interpretations evolve
• your exposure depends on where the crash happens, where the broker operates, and where the parties are domiciled

Even within the states’ argument, you see examples of how states calibrate outcomes differently (for instance, different negligence regimes). That variation is exactly what breaks spreadsheet compliance.

What changes if SCOTUS allows more state claims

Answer first: If state negligent-selection claims survive, your carrier selection process needs to be demonstrably consistent, documented, and explainable.

Expect three shifts:

1. More discovery pressure on broker and shipper systems

   • Plaintiffs will demand emails, chat logs, onboarding records, safety snapshots, exception approvals, and who signed off.

2. More emphasis on “what you knew when you booked”

   • Not what you learned later. What the record showed at tender acceptance.

1. More scrutiny of exceptions
   • The existence of a policy doesn’t help if exceptions are frequent, undocumented, or rubber-stamped.

This is where AI belongs—not as a buzzword, but as a way to make compliance real when you’re processing thousands of decisions.

Where AI actually helps: compliance you can prove

Answer first: AI is most valuable when it turns carrier risk signals into consistent decisions with audit trails—without slowing down operations.

I’ve found that teams get the most ROI from AI when they stop trying to “predict crashes” and instead focus on three boring, high-impact jobs: standardization, documentation, and early warnings.

1) AI-driven carrier risk scoring (with human guardrails)

Answer first: Use AI to prioritize review and enforce minimum standards—not to auto-approve everything.

A practical setup:

• A rules layer defines hard stops (expired insurance, missing authority, out-of-policy limits)
• A model layer creates a risk score from multiple signals (safety history, claims patterns, inspection trends, lane/context risk, prior performance)
• A workflow layer routes loads based on thresholds:
  • green: normal approval
  • yellow: require documented justification
  • red: block unless a named executive approves

The key is governance: the model can recommend; policy must decide.

2) “Compliance copilots” for changing state and federal requirements

Answer first: AI can translate complex legal standards into operational checklists and alerts.

Most compliance failures aren’t malicious—they’re informational. People don’t know what changed, or they can’t keep it all in their head.

A good AI compliance copilot should:

• track policy versions and effective dates
• flag mismatches between current practice and updated requirements
• generate lane-specific or state-specific prompts (example: winter operations, chain requirements, local permitting norms)
• produce plain-language rationale for why a carrier was blocked or escalated

This is how you keep speed without losing control.

3) Audit trails that stand up in litigation

Answer first: If you can’t reconstruct your decision later, assume it won’t help you.

In negligent-selection litigation, the paper trail matters as much as the policy.

AI can help by:

• automatically storing a “decision snapshot” at booking time
• recording which signals were checked and their values
• capturing exception approvals with reason codes
• summarizing communications into a consistent case file

If the Supreme Court decision leads to more state-law claims, this capability moves from “nice to have” to “board-level risk control.”

What brokers, shippers, and carriers should do right now

Answer first: Build a carrier selection program that’s consistent across the network, but flexible enough to reflect state-level risk—and automate the evidence.

Here’s a practical 30–60 day plan that doesn’t require rebuilding your entire tech stack.

Step 1: Write down your “minimum viable defensible process”

Document a short policy that answers:

• What are the non-negotiable disqualifiers?
• What triggers escalation?
• Who can approve exceptions?
• What documentation is saved—and where?

If it takes more than two pages, most teams won’t follow it under pressure.

Step 2: Identify the top 10 exception reasons and eliminate half

Pull the last quarter’s tenders and ask:

• Why did we override?
• Was it truly unavoidable?
• Did the override correlate with claims, service failures, or chargebacks?

A lot of “exceptions” are really process gaps (missing documents, slow updates, unclear ownership).

Step 3: Add AI where the friction is highest

Most organizations should start with:

• automated document validation (insurance, authority, W-9 equivalents)
• automated safety and performance summaries
• automated booking-time decision snapshots

The goal isn’t to replace your team. It’s to reduce the number of judgment calls made with incomplete information.

Step 4: Treat carriers like strategic suppliers

This is the series tie-in that matters: AI in supply chain and procurement works when the data model treats carriers as suppliers with risk, performance, and compliance attributes.

Create a supplier-style scorecard that includes:

• safety and compliance signals
• claims and incident history
• on-time and tender acceptance performance
• document freshness
• exception frequency

Then use that scorecard in routing guide decisions, not just in quarterly reviews.

The stance: preemption debates won’t save sloppy processes

Answer first: Regardless of how SCOTUS rules, the industry is heading toward higher expectations for broker and shipper diligence.

If the Court sides with the states, exposure expands more directly. If the Court sides with brokers, the reputational and commercial pressure won’t disappear—especially as shippers tighten risk requirements and insurers price based on controls.

Either way, “we followed a process” isn’t a defense unless you can prove it, consistently, at scale.

The teams that will win 2026 procurement bids are the ones that can say, with a straight face: we can move freight fast, and we can show our work when something goes wrong.

If you’re building your AI roadmap for supply chain and procurement, put carrier selection, compliance automation, and audit-ready documentation near the top. It’s not glamorous. It’s also where lawsuits and loss ratios are headed.

What would change in your network tomorrow if every tender had to be defendable in court—down to the screenshot of what you knew at booking time?