For food manufacturers managing delivery appointments, seasonal demand and thin margins, getting that foundation right matters.

Begin with What You Can Explain

Consider accessorials, the charges beyond basic linehaul and fuel. Our analysis of millions of domestic less-than-truckload (LTL) and truckload bills from the past year found that about 32% carried at least one accessorial. On those bills, accessorials averaged nearly 20% of the carrier’s charge.

If detention, unloading and reconsignment all land in one freight account, it’s difficult to explain what’s driving the expense or who can address it. Rates cause similar trouble. If the current agreement was never loaded, a correctly billed invoice can look wrong, and disputing it only creates more work.

In LTL, the root cause of recurring reweigh and reclass charges shows up when you compare the billed weight and class with the original bill of lading. That only works if both documents are captured where the analysis happens.

A practical first step is reviewing 12 months of bill-level data. How many bills match a shipment or purchase order? How many have an applicable contract rate? Can you tie accessorial dollars to a facility and a reason? Check for duplicate PRO numbers and payments, allowing for legitimate rebills and balance-due invoices.

From there, compare each invoice (what the carrier says happened) with the shipment documents (what you expected) and the governing agreement. That gives the team something to investigate and correct.

Give AI a Specific Job

AI can read bills of lading and delivery receipts to fill missing fields, suggesting values with a confidence level so someone can verify the critical ones. Unusual charges, detention patterns and less obvious outliers are fair game too, and the more history it has, the better it gets at spotting them.

Budgeting is another fit. When a carrier announces an increase, a model can lay out the impact down to the location, the lane, even the individual shipment. It can also test mode choices: what a group of LTL shipments would cost consolidated into truckload, or whether a long-haul lane could move intermodal within the delivery window. That only holds up when each mode’s bills tie back to their own agreements. Truckload rates often live in spot quotes and email rather than a loaded contract, so a gap in one mode’s data can look like savings in another.

The real test is what a planner can do with the output on Monday morning. The details should show the affected shipments, the contract terms behind each number and enough context for a conversation with operations. Start where you already suspect an opportunity and expand once the results earn it. That first rollout builds the trust people need to accept a model in their workflow. Change management matters as much as the math.

Leave Room for Operational Judgment

A lower rate is only part of a food freight decision. A refrigerated load still needs to meet its delivery window, arrive with enough remaining shelf life and get unloaded when the receiver can take it.

Holding an order for consolidation might reduce freight expense but put the customer appointment at risk. Retailer on-time, in-full penalties can erase those savings. The same goes for a mode shift: intermodal may win on paper and still miss a produce window. A planner must weigh both before accepting the recommendation.

Use AI to pull the evidence together. Keep someone involved who understands the operation when that evidence doesn’t settle the decision.

Ask Vendors to Show Their Work

Most AI tools for freight audit and transportation look good in a sales demo built on tidy sample data. Before you buy, ask the vendor to show you what the tool does with incomplete records — not just clean ones — and answer these questions:

• What happens when a shipment, rate or required field is missing?
• How often does a flagged charge turn out to be a real billing error?
• Does each recommendation connect the invoice, agreement and supporting documents?
• Who approves an action before it changes a payment or starts a dispute?
• How does the tool integrate with our transportation and enterprise systems?
• What audit trails and access controls do the tool provide?

Make Accountability Part of the Process

A flag is a reason to review a charge, it isn’t proof of an error. In our audit work, roughly one in four flagged bills was cleared without any correction.

Base approval rules on dollars and risk. A small variance might qualify for automatic approval when the shipment and contract match. Paying a disputed charge or changing a contract rate should require sign-off.

Capture the reason for every override and review those reasons monthly. If overrides increase, investigate whether the data, terms or model need attention. Sample automatically approved items, too. Looking only at exceptions won’t tell you what the system missed.

Before you rely on AI to guide freight decisions, connect the underlying records, identify the gaps, and assign responsibility for fixing them. Then test whether the recommendations hold up against actual shipments and charges. Confidence in the result comes from that work, not from the model.