The inbox is not the problem
At transport companies, orders, freight confirmations, and instructions still arrive largely by email. Planners read them, manually re-enter the data into the TMS or planning system, and move on. That takes time and introduces errors. The promise of AI email processing is clear: let the machine read the email and fill in the right fields. And technically, it works. What we see time and again is that the inbox itself is the easiest part of the problem.
So what does not work with AI email processing for transport companies?
The real obstacle lies in what sits behind the email. An AI worker reads an email from a shipper: loading location, unloading location, weight, reference number. Fine. But then that information has to go somewhere. And that is where it starts: which customer record belongs to this sender? Does the address in the system match the address in the email, or does the customer have three variants in the master data? Is the customer's reference number the same field as your own order number, or do they need to coexist side by side? The AI extracts the data from the email, but if the destination is a messy system, the automation stops exactly where it hurts. You have built a smart reader, but not a working process.
The lesson: start at the output, not the input
The approach we now follow: start at the destination. Where does the processed order need to land, and in what state? Once that question is clear, you can work backwards to determine what the AI needs to extract from the email and how the two connect. That sounds logical, but in practice projects often start with the email itself, because it is visible and tangible. The system behind it is assumed to be familiar territory. That assumption is almost always too optimistic. Master data with inconsistent customer records, free-text fields used differently by each planner, systems accessible only through an outdated API: these are the things that become either half an hour of preparation or three weeks of work. The difference between those two outcomes is determined by looking at them early.
When is AI email processing immediately deployable?
There are situations where things move quickly. If you have a limited set of fixed shippers with a consistent email format, extraction is predictable and matching is straightforward. If your TMS has a solid API and your master data is in order, you can move from email to completed order in production without lengthy build sessions. If the organisation wants to keep exceptions with a human and let the AI handle only the straightforward cases, the human-in-the-loop principle works well: the AI handles the data entry for the eighty percent that is clear-cut, and the planner decides on the rest. But if you expect the AI to also handle exceptions, poor data, and ambiguously worded emails without any intervention, you are setting yourself up for disappointment.
What you can do concretely as a transport company
Make an honest assessment: how many of your incoming emails come from a fixed set of senders with a recognisable format? How clean is your customer master data? Do you have a system with an accessible API or export option? Those three questions determine ninety percent of how quickly and smoothly AI email processing for transport companies goes into production. If the answers to all three are good, you can start quickly. If one of the three is a problem, that is the first project, and the AI processing comes after. That may sound like a delay, but it is the opposite: it prevents you from building an automation that stalls after two months on exactly the same manual exceptions as before, only with an AI layer around it that planners do not trust.