What AI should never do in a freight brokerage

A rep pastes a load’s dimensions into ChatGPT, gets back a list of states that “require permits and escorts,” and puts a number in front of the customer. Nobody called a permit office. The quote goes out at 11:40 because the shipper wanted it by noon.

That is not a technology failure. The prompt worked as designed. It produced a fast, confident, organized answer, and somebody treated that answer as a decision instead of a starting point.

I spent eight years brokering freight at TQL. I now coach brokers on getting AI working in their business, which mostly means telling people where to point it — and the more useful half is telling them where not to. Every other article on this is published by a company selling software. This one is not, so I can say the part they cannot: some jobs in your brokerage AI should not touch, and a few are the ones vendors demo hardest.

Never let AI make permit or compliance calls

There is a genuinely good prompt going around for oversize and overdimensional freight. A broker on r/FreightBrokers described it well: “By using one prompt along with ZIP codes, dimensions, and weight, I can quickly determine which states require permits, escorts, curfews, and more” (r/FreightBrokers).

I like that prompt. Use it. It orients you in thirty seconds on a load you have never moved and tells you which states to worry about before you start dialing.

It is also not an authority on anything. The federal government does not issue oversize or overweight permits at all — FHWA is explicit that permitting is a state function and that you have to contact each state you intend to travel through (FHWA Office of Operations). Escort thresholds, curfews, and route restrictions vary by state and change. A chat model is working from whatever it absorbed in training, not the current permit manual.

The rule: the prompt tells you what to verify. The state permit office, or your permit service, tells you what is true. If a dollar figure or a delivery commitment depends on the answer, it came from the permit office.

Carrier vetting and fraud checks need a person

AI is fine at summarizing a carrier packet, pulling numbers out of a PDF, or flagging that something looks off — three addresses across three documents, a phone number that matches nothing else in the file, a name one character off a carrier you already use.

It should never be the thing that clears the carrier. Authority, insurance, and identity get confirmed against the source of record, every time. Authority in FMCSA’s MOTUS USDOT Registration System Search. Insurance confirmed with the agent or certificate holder, not from the certificate you were emailed — FMCSA says plainly that “insurance certificates can be fraudulent” and that document examination is critical (FMCSA). Phone numbers checked against the same MOTUS record. If the number the carrier gave you does not match the posted one, call the posted one.

The risk you are managing is double-brokering and identity theft, where somebody uses a legitimate carrier’s DOT number to take your load. An AI summary of a fraudulent packet is a tidy summary of a fraudulent packet. It will not save you. The phone call will.

Never let AI reach a customer before you do

I would not connect a model to your outbound email and let it send. Not to shippers, not to carriers. Draft, yes. Send, no.

The drafts are not the problem — they are usually better than what a busy rep types at 4:50pm. The problem is that the failure mode is invisible. A model that commits to a delivery date you cannot hit, or quotes with the wrong accessorial assumption, produces an email that reads perfectly. There is no red flag in the prose. You find out when the customer holds you to it. Same for quotes: if a number leaves your building, a human who knows the lane looked at it.

Credit decisions and pay terms stay with you

Do not let a model set a credit limit, approve a new shipper, or decide pay terms. It has no view of your receivables, no relationship history, and no exposure to the consequences. Where it helps: reading a credit report and telling you which three things in it to ask about. That is research. The decision stays with whoever eats the loss.

Never trust AI on data you never gave it

This is the mistake I correct most often.

A chat model does not know today’s spot rate. It does not know your load board. It does not know what your carrier paid on that lane last Tuesday. Ask “what should I pay to move a dry van from Laredo to Atlanta this week” and you get a number — a plausible-sounding average from training data of unknown vintage. It is a guess formatted like a rate.

The brokers getting real value do the opposite. One described feeding DAT, GreenScreens, and internal data into the model for RFP lane analysis, and getting back work that “identifies outliers, combines averages, and highlights opportunities for better margins” (r/FreightBrokers). That works because the data came from him. The model did the arithmetic. It did not supply the facts.

A broker in that thread put the principle better than I can. Treating the output as fact would be “childish.” That is the right posture, and it comes from someone using these tools daily, not someone dismissing them.

The real blocker is access, not intelligence

Another broker in that thread was building a document-retrieval system for a brokerage client and reported: “I haven’t yet observed significant advantages from this approach” — partly because “we don’t have access to the McLeod API” (r/FreightBrokers).

That is the shape of most disappointment here. The model was not the problem. It could not see the system where the work lives. When a demo looks impressive, the question is not how smart the model is. It is what the model is connected to.

The rule for telling safe AI work from unsafe

Here is the heuristic I use. It holds up better than any list of approved use cases.

AI is safe when the output is reviewed by someone who would actually catch the error, and when being wrong is cheap and visible. It is dangerous when the output is final, invisible, or expensive to be wrong about.

Both halves of that first sentence matter. “Reviewed” by someone who would not notice the mistake is not review. A rep who has never run oversize freight is not a check on a permit answer.

Safe, by that test:

• Drafting a carrier outreach email that a rep reads before sending
• Summarizing a 40-page shipper RFP into the terms that matter
• Turning a messy tender email into structured fields a human confirms before entry
• Building a daily market brief from data you supplied — the r/FreightBrokers 7am version covering capacity, diesel, and tender rejections, “the ideal five-minute read while enjoying my coffee”
• Auditing invoices against rate cons and listing discrepancies for a person to resolve
• Explaining a regulation in plain English so you know what to ask your compliance contact

Dangerous, by that test:

• Final permit, escort, or route determinations
• Clearing a carrier for a load
• Sending anything to a customer unread
• Setting rates or quoting without market data in the window
• Credit approvals and pay terms
• Anything auto-executing inside your TMS with no human step

Two freight broker prompts that stay on the safe side

The first forces the model to show you where it is guessing. That is what makes it reviewable.

You are drafting for my review, not making a decision.

Task: [describe the task — e.g., draft a reply to this shipper asking for
a rate on the attached lane]

Context I am giving you:
[paste the email, the lane history, the rate data, the load details]

Rules:
1. Use only the information above. Do not supply market rates, transit
   times, permit requirements, or carrier information from your own
   knowledge.
2. Write the draft.
3. Then, under a heading "Assumptions I made," list every assumption
   you had to make to write it.
4. Then, under a heading "What I could not determine," list everything
   I need to confirm before this goes out, and who or what would
   confirm it (specific office, system, or person).
5. If a fact is missing and material, leave a bracketed blank instead
   of filling it in.

The second uses the model as a checker rather than a producer. It is the highest-value use I know of and the one brokers try last.

Audit my work. Do not rewrite it and do not add new information.

Here is a rate confirmation and the original quote I sent the customer:
[paste both]

Compare them line by line and list:
1. Any figure that does not match between the two documents
2. Any accessorial, detention term, or fee on one and not the other
3. Any date, appointment window, or reference number that conflicts
4. Anything blank that should not be blank

Output a numbered list of discrepancies only. If you find none, say
"no discrepancies found." Do not guess at what the correct value
should be.

Both get better if you tune them with three to five real examples out of your own inbox rather than using the generic version (MVMNT).

Where a brokerage still needs a person

The honest limit on my own advice: the heuristic tells you which side of the line a task falls on, not whether your team will hold the line. Reps under quota skip the review step, and the drafts are good enough that skipping it feels safe for a while. If you adopt any of this, review has to be somebody’s stated job, not an understanding. And none of the safe uses above eliminate work. Reviewing a good draft beats writing from nothing, and it is not free.