AI in logistics pays off on the high-volume, rule-bound work that arrives as unstructured text: quote requests by email, orders and load tenders, bills of lading and proofs of delivery, status checks, missed pickups and "where is my freight" questions. The clearest public evidence is from C.H. Robinson, which says it moves 37 million shipments a year and reported in April 2025 that its AI agents had performed more than 3 million shipping tasks. AI pays off least where the data underneath is messy or the decision carries real money and judgment, such as contract pricing or tendering a load to a carrier you have never used. This guide covers where it works, with sourced figures only, what it needs before it works, the risks, and a table for deciding where to start.
Where AI pays off in logistics operations
Most of the published numbers come from one company, so read them as what is possible at very high volume, reported by the company itself. A broker moving a few hundred loads a month will not see the same totals, but the kinds of work are the same.
Quoting from email
Price requests arrive as free-text emails in every format a customer can think of. In May 2024 C.H. Robinson said it received over 11,000 emails a day asking for truckload pricing, and that its AI was answering 2,000 customer quote requests a day in an average of 2 minutes 13 seconds. The model reads the email, works out the mode (truckload, LTL, intermodal or air) and returns a price. By April 2025 the company said it had passed 1 million AI-delivered quotes. If you build the same thing, keep the model on the reading. The number should come from your own rate logic, never from the model.
Order entry and load acceptance
Once a customer accepts a quote, someone has to key the order into the TMS. C.H. Robinson reported 1 million orders processed by AI by March 2025, and more than 5,200 customers getting loads accepted in under 90 seconds, against waits of up to four hours for a person to reach the email. Its agents had also set more than 1 million pickup and delivery appointments. These are good first projects because the output is a structured record that can be checked against the email it came from.
Exceptions such as missed pickups
Exception handling is where agents start to do more than extract data. In January 2026 C.H. Robinson described agents that chase missed LTL pickups and decide how to keep the freight moving; the company says they can make 100 calls and 100 decisions at once. It reported that 95% of missed-pickup checks were automated, saving more than 350 hours of manual work a day, with 42% fewer unnecessary return trips and freight moving up to a day sooner. The same release says a fleet of more than 30 agents also handles quotes, orders, freight classification, shipment tracking and proof of delivery.
Documents: BOLs, PODs and rate confirmations
Bills of lading, proofs of delivery, rate confirmations and carrier invoices are the paperwork every broker reconciles by hand. A model can read a scanned or photographed document, pull out the load number, dates, signatures, piece counts and exception notes, match it to the load in the TMS, and flag anything that does not match for a person. C.H. Robinson lists proof of delivery among its agents' jobs but has not published a figure for it, so we will not quote one.
The closest Gigabit evidence is from an e-commerce operation, not a broker. In our operations automation for a four-channel retailer, a model parses wildly inconsistent vendor reply emails into structured ship dates and tracking numbers, and vendor PO follow-up dropped from 5 hours a week to 45 minutes. The same project processes 85% of returns with no manual handling and runs a nightly inventory reconciliation that lifted inventory accuracy from 94.2% to 99.1%. The pattern carries straight over to carrier paperwork: extract, match to the record, act on clean cases, route the rest to a person.
Track-and-trace updates and customer questions
Shippers want to know where their freight is, and most of the answers already sit in carrier status messages, tracking APIs and the TMS. An agent can read those, write the update and answer "where is my load" without a person looking it up. In a support project for a direct-to-consumer brand, order status was 23% of ticket volume; the AI agent we built resolves 64% of all tickets with no human and cut first response time from 4.2 hours to 6 minutes. That client sells home goods, not freight, but the question it answers most is the one logistics customers ask most. The rule that matters: the agent reports what the data says and never guesses an ETA.
Carrier matching
Ranking carriers for a lane by history, price, on-time record and current capacity is a reasonable AI task. Booking them without a person is a different matter, covered under risks below. Treat carrier matching as a recommendation an agent prepares and a person approves, at least for any carrier you have not worked with before.
The wider evidence
Outside freight brokerage, the most cited figure is McKinsey's: early adopters of AI-enabled supply-chain management improved logistics costs by 15%, inventory levels by 35% and service levels by 65% compared with slower-moving competitors. That is from April 2021, it covers supply chains broadly rather than brokerage, and early adopters tend to be better-run companies to begin with. Use it as direction, not a forecast for your business.
What AI needs before it works
Every example above has the same thing underneath: a system of record with clean, structured data that an agent can read from and write to. Without that, an AI model is reading emails into nowhere.
- A TMS or WMS with an API. Loads, statuses, customers, carriers and documents need IDs the agent can look up and update. Spreadsheets and shared inboxes do not count.
- Integrations to where the work arrives. The quoting inbox, EDI load tenders and shipment status messages (X12 204 and 214 in the US), carrier tracking APIs or ELD feeds, load boards and accounting.
- Reference data you trust. Customer records, lanes, accessorials, rate tables and carrier profiles. If two people would price the same lane differently, an agent will too.
- Written rules. Margin floors, which customers get auto-quotes, when an exception goes to a person, and who approves what.
- Logging and tests. Every agent action recorded against the load, and a test set of real past emails and documents to measure accuracy before and after each change. Our AI agent eval metrics guide covers what to measure.
This is where many brokers actually start. The founder behind our logistics SaaS MVP, a 15-year freight veteran, saw mid-size brokers handling 50 to 500 shipments a month running on 2000s-era TMS software, Excel, email chains and phone calls. Gigabit built the platform in 11 weeks for $48,000: shipment creation, multi-stop routing, carrier assignment, document upload, real-time tracking on a map, automated notifications, a KPI dashboard and per-shipment billing. It reached 14 paying customers and $19,000 in monthly recurring revenue by month 6, and the founder closed a $2.1M Series A four months after launch. That build was a platform, not an AI agent. It is the structured data layer that agents need, and the right first step for a broker who does not have one.
The risks, and how to contain them
- A wrong quote commits margin. Set a margin floor the agent cannot cross, send quotes above a value threshold or for new customers to a person, and log the inputs behind every price.
- Misread documents. A model can misread a smudged scan with full confidence. Match every extracted field to the load record, set a confidence threshold, and route mismatches and exception notes such as damage or shortage to a person.
- Freight fraud. An agent that books carriers or changes payment details automatically is a target. Check authority and contact details against FMCSA's records and your own carrier profiles before any tender, and never let an agent change remit-to details. The FMCSA's fraud alerts page lists schemes that impersonate FMCSA itself, including its SAFER lookup, which shows how much of this is going on.
- Made-up status updates. Tracking messages must come from carrier data. If the data is missing, the agent says so and escalates.
- Integrations that drift. Carrier portals, APIs and email formats change. Monitor failure rates and alert a person, the way you would for any production system. See why AI systems break after launch.
- Customer data in prompts. Shipper names, addresses and rates go into the model. Check your model provider's data terms and keep the processing in accounts you control.
The general rule is human review where a mistake costs money or trust, and full automation where the output can be checked against a record. We cover how to place those checkpoints in human-in-the-loop for AI agents.
Decision table: where to start
| Task | Good first AI project? | What it needs | Keep a person on |
|---|---|---|---|
| Quote requests by email | Yes, if you already have rate logic | Quoting inbox, rate tables, margin rules | New customers and quotes above a threshold |
| Order entry from email or PDF | Yes | TMS API, customer records | Low-confidence fields |
| BOL and POD processing | Yes | Document storage, load matching | Mismatches, damage and shortage notes |
| Track-and-trace updates | Yes | Status messages, tracking APIs or ELD feeds | Delays that break a delivery promise |
| Missed pickups and other exceptions | Yes, for triage and outreach | Status feed, carrier and shipper contacts | Rebooking that adds cost |
| "Where is my load" questions | Yes | Read access to the TMS | Claims and disputes |
| Carrier matching | As a recommendation | Lane history, carrier profiles, FMCSA checks | Every tender to a new carrier |
| Contract pricing and bids | No, analysis support only | Clean historical data | The decision |
Pick the row with the most volume and the cleanest data, prove it on real past emails or documents, then move to the next. If your TMS already ships one of these features, use it. Building your own is worth it when the vendor's version does not fit your process or your data lives across several systems.
Where Gigabit fits
- One workflow, such as email quoting or POD matching: our AI Quick Win Sprint is $8,000 fixed over 3 weeks, with integration to up to three tools, error handling and 30 days of light support.
- A production agent, such as an exceptions or customer-status agent: Gigabit Agents from $8,000, flat per agent.
- Not sure where to start: the AI Transformation Sprint is $25,000 over 2 weeks. It maps your workflows, models the return and ends in a scoped, ready-to-build agent, and the fee credits toward a Gigabit Agents build.
- No usable system of record yet: start with the platform, as the logistics MVP did, through custom software development.
If your volume is a few dozen loads a week and one person handles them comfortably, you probably do not need any of this yet. Clean up the data and the inbox first. The agents will be cheaper to build later.
