Supreme Court Case Puts Broker Safety on the Line

About 5,000 people die each year in crashes involving large trucks in the U.S. That number is why a Supreme Court case about paperwork-sounding terms like “preemption” and “negligent hiring” is actually a big deal for anyone who ships freight, manages carriers, or signs off on risk.

The case—Montgomery v. Caribe Transport II, LLC—asks whether states can allow injured motorists to sue a freight broker for negligent selection of a carrier, or whether a federal deregulation law blocks those claims. On its face, it’s a legal fight. In practice, it’s a fight about incentives: who has to care, how much, and what counts as “reasonable” carrier selection when something goes wrong.

For this AI in Supply Chain & Procurement series, here’s the angle that matters: if legal accountability shifts, procurement and transportation teams will respond by changing process. And the fastest way to change process at scale is to turn it into data + policy + automation—which is exactly where AI fits.

What the Supreme Court is really deciding (in plain terms)

Answer first: The Supreme Court is deciding whether the 1994 Federal Aviation Administration Authorization Act (F4A) prevents states from applying their traditional negligence laws to a broker’s carrier-selection decisions.

Here’s the scenario that brought this to a head:

• A serious crash in Illinois leads the injured driver (Shawn Montgomery) to sue not only the driver and motor carrier, but also the broker that hired the carrier.
• The claim is straightforward under many state laws: the broker chose an “unfit” carrier (think poor safety history), and that choice contributed to harm.
• Lower federal courts ruled the claim is barred because the F4A preempts (blocks) state laws “related to” a broker’s services.

Now 29 states plus Washington, D.C. have filed a brief backing the injured driver’s right to bring a negligent-hiring claim under state law. Their position: Congress didn’t clearly intend the F4A to wipe out long-standing state road-safety tort law.

If you run procurement, a brokerage, a shipper transportation desk, or a carrier management team, the practical question becomes:

If a broker can’t be sued for negligent selection in many states, what happens to the day-to-day discipline of carrier vetting?

Why states are lining up: incentives, not politics

Answer first: States are defending negligent-hiring claims because they’re one of the few tools that directly pressure market behavior—they make unsafe choices expensive.

The states’ argument has three legs:

Federalism: road safety has historically been a state responsibility

States set and enforce a lot of the rules that keep highways safe: licensing, inspections, traffic laws, and civil liability standards. Their brief leans on the idea that if Congress wants to erase state authority in a traditional area, it should say so clearly.

That matters because negligent hiring isn’t “regulation” in the classic sense. It’s a liability rule that says: if you had reason to know a carrier was risky and you hired them anyway, you can share responsibility.

Tort claims create a safety feedback loop

The feedback loop is simple:

• Brokers and shippers face consequences for careless selection
• They improve vetting and monitoring
• Unsafe capacity gets less freight

If those claims disappear, the states warn that the market can slide toward lowest-cost capacity with weaker screening—especially in tight markets when service failures hurt more than abstract safety risk.

States rely on different liability frameworks

One overlooked point: states balance responsibility differently.

• Some states use contributory negligence (a harsh rule that can bar recovery if the plaintiff shares fault)
• Many use comparative negligence (fault is allocated proportionally)

Preemption can flatten those differences into a single federal outcome—regardless of how a state has chosen to balance safety, commerce, and compensation.

Why this case matters to AI in transportation risk management

Answer first: The more uncertainty there is around broker liability, the more valuable auditable, consistent, data-driven carrier selection becomes—and AI is the most scalable way to do that.

A lot of teams treat AI as a cost play: automate dispatch, reduce empty miles, speed up tendering. That’s real. But this case highlights another role AI is already good at: defensibility.

When a crash happens, the question isn’t “Did you use AI?” It’s “Did you act reasonably?” AI helps you answer that with evidence.

AI turns carrier selection into a repeatable control

Carrier vetting often lives in tribal knowledge:

• “We’ve used them before.”
• “They were cheap on this lane.”
• “They picked up quickly during the last surge.”

That’s not a system. It’s a memory.

An AI-supported selection workflow can create repeatable controls such as:

• A risk score combining safety signals, operating authority status, insurance, inspection/out-of-service patterns, and performance data
• Automated thresholds (for example, “no new carrier without verified insurance + minimum on-time history + acceptable safety signals”)
• Exceptions that require approval and are logged with reasons

Even if the Supreme Court narrows state negligent-hiring claims, plaintiffs will still litigate facts, regulators will still investigate, and insurers will still price risk. Good records remain valuable.

Predictive analytics shifts the focus from “check once” to “monitor always”

Most procurement teams vet carriers at onboarding, then assume the world stays stable. It doesn’t.

AI-based monitoring is useful because it’s continuous:

• Detect deteriorating performance (rising claims, late deliveries, missed appointments)
• Flag operational instability (sudden capacity spikes, unusual lane behavior)
• Trigger re-verification cycles and tighter tender rules

This is the same philosophy we use in supplier risk management: initial qualification is table stakes; ongoing surveillance prevents surprises.

AI helps align safety with service (instead of trading them off)

Here’s what works in practice: don’t treat safety as a blocker. Treat it like a lane constraint.

A strong AI routing/tendering approach can:

• Prefer lower-risk carriers when two options are similar on cost and service
• Reserve higher-risk capacity for lower-consequence freight (when policy allows)
• Re-route around high-risk conditions (weather, congestion, construction zones) during peak season

December is a perfect example. Peak retail shipping, winter weather variability, and year-end capacity pressure tend to collide. AI shines when constraints stack up.

What changes depending on the Court’s ruling

Answer first: A ruling that expands preemption likely reduces negligent-hiring exposure for brokers in many states, but it doesn’t eliminate safety expectations; it changes where pressure comes from—insurance, contracts, and shipper requirements.

Let’s make it concrete.

If the Court sides with the broker (broader preemption)

Expect more of the following:

• Contractual safety addendums: Shippers will push broker/carrier contracts to define minimum safety criteria and monitoring duties.
• Insurance-driven controls: Underwriters will demand tighter selection documentation and may price policies based on process maturity.
• Shipper-led scorecards: Large shippers may require brokers to prove carrier vetting standards with audit trails.

AI becomes a practical way to meet those demands without adding headcount. Not because it’s trendy—because manual compliance doesn’t scale.

If the Court sides with the states (narrower preemption)

Brokers and shippers should assume negligent-selection theories remain active and plan accordingly:

• Stronger due diligence expectations: “We checked a box” won’t be persuasive.
• More discovery focus: Plaintiffs will ask for tender history, exception approvals, and carrier selection rationale.
• Operational governance: Leadership will want a documented policy for when and why exceptions are allowed.

In this world, AI isn’t only about faster decisions—it’s about consistent decisions.

A practical playbook: using AI to reduce accidents and liability

Answer first: The safest and most defensible approach combines policy, data, and automation—AI supports all three.

Here’s a procurement-friendly playbook I’ve seen work.

1) Define “unfit carrier” in operational terms

Don’t leave this vague. Translate it into measurable criteria.

Examples of policy inputs:

• Authority and insurance verification rules
• Minimum safety thresholds (and what data qualifies)
• Claims and cargo incident limits
• Performance minimums by lane or freight type

Then decide what happens when a carrier doesn’t meet the bar:

• Blocked from tender
• Allowed only with manager approval
• Allowed only for certain freight categories

2) Build a tiered carrier risk model

Not every shipment has the same risk profile.

A simple tiering structure:

• Tier 1 (low risk): Preferred carriers; auto-tender eligible
• Tier 2 (managed risk): Tender allowed with monitoring
• Tier 3 (high risk): No tender unless documented exception

AI helps by keeping the model current and explaining the drivers behind a tier change.

3) Automate the audit trail (your future self will thank you)

A strong audit trail includes:

• The data used at the time of tender
• The risk score and its drivers
• Any overrides, who approved them, and why
• Post-load monitoring outcomes

This isn’t just legal self-defense. It’s also how you improve. Patterns show up fast when decisions are logged.

4) Use predictive monitoring during execution

Execution is where accidents happen.

AI signals worth watching in near real time:

• Route risk (weather, construction, known congestion zones)
• Driver behavior proxies (speed variance, harsh braking where telematics exists)
• Appointment pressure (late pickups that encourage risky driving)

The procurement tie-in: when you see repeat patterns, you change lane guides and service expectations—before the next incident.

5) Treat safety as a supplier risk category

In supply chain & procurement, we already track:

• Financial risk
• Cyber risk
• Geopolitical risk
• Quality risk

Road safety belongs on that list. The same governance approach applies: segmentation, monitoring, escalation paths, and corrective action plans.

What to do in Q1 2026 if you want fewer surprises

Answer first: Don’t wait for the Supreme Court decision to start tightening carrier selection and documentation—your insurer, your customers, and your own incident rate won’t wait either.

Three realistic next steps:

1. Map your current carrier-selection workflow from onboarding to tender to exception approvals. If you can’t draw it on one page, it’s not controlled.
2. Pick three metrics you’ll manage weekly (not quarterly): tender acceptance by tier, incident/claim rate by tier, and exception frequency by planner.
3. Pilot AI risk scoring on one region or business unit and measure outcomes like fewer exceptions, better on-time performance, and fewer claims.

The legal landscape may change. The operational truth won’t: unsafe capacity costs more—sometimes immediately, sometimes catastrophically.

The Supreme Court can influence who gets sued, but it can’t change what your customers expect or what the public tolerates. The teams that build AI-backed, auditable safety controls into procurement will be the ones that scale without getting blindsided.

What standard do you want your organization held to when a plaintiff’s lawyer—or an insurer—asks, “Why did you hire this carrier?”