Drafting freight documents means creating CMRs, packing slips and other shipping documents needed to put a shipment on transport. For a logistics employee or freight forwarder, this typically happens in a TMS or ERP system, based on order data that is already recorded. The question is not whether this can ever be automated, but what is already possible today.
Three axes shape the picture here: structuredness, volume and compliance.
Structuredness scores high. A CMR follows a fixed format, a packing slip follows a fixed format, and the data that needs to go in it -- sender, consignee, number of packages, weight, reference number -- is already in the order system. No text needs to be written, only data taken over and placed. That is precisely the work rule-based automation (RPA) was made for: pulling data from system A and placing it in the correct fields of system B or a document template.
Volume reinforces this. Every shipment requires this again, which means a small time saving per document multiplies across hundreds or thousands of shipments per month. Where volume is high and the work repeats, the chance that automation pays for itself is greatest.
Compliance scores a 2, which actually works in favour here: a CMR is a legal document with fixed mandatory fields. There is little room for interpretation about what should be in it, which makes it easier to check the correctness of the output than for documents where one must assess for oneself what is compliant.
Judgment scope stands at 4, and that immediately makes the picture more nuanced. Not every shipment is standard. A dangerous substance, an exceptional customs destination, a customer with a deviating delivery condition: this requires a check that goes beyond taking over data. Anyone who sets up automation here without recognising those exceptions risks incorrect documents going out the door just as quickly as correct ones.
The error costs at 3 belong with this. An incorrect CMR can lead to a refused shipment, an incorrect packing slip to a wrong delivery to the customer. That is not catastrophic, but not negligible either. Hence the most workable setup today is not full takeover, but automatic generation with a human eye on deviations: the system compiles the document, an employee approves or rejects what falls outside the standard pattern.
Creativity scores a 1, which makes sense: there is nothing to formulate or devise, only to structure. That makes the task particularly suited to automation, but it also means there is little room to work smarter using AI language models. This is rule-based work, not writing work.
The technology that fits here is RPA: software that takes over data and assembles documents according to fixed rules. Not generative AI producing free text, but a script that pulls order data from the TMS or ERP and places it in the correct document format. That works as long as two conditions are met: the document formats are standardised, and there is a link with the order data. Without that link, someone keeps manually retyping, and without a standard format, the layout has to be rethought for each exception.
At a company that mainly ships standard shipments within the EU, with a limited number of customers and fixed delivery conditions, the takeover is large: almost all documents follow the same pattern. At a freight forwarder that works internationally with varying customs requirements, dangerous substances and customer-specific arrangements, the share of automatable work is lower, and a larger part remains with the employee who recognises the exceptions.
This dependency on standardisation is the same logic that applies to processing purchase invoices and to registering a goods receipt note in the system: the more structured the source, the larger the share that can be done without human hands. Further along in the logistics process, the picture is different again, such as with assigning warehouse locations or picking and packing goods, where physical actions set a different limit.
This is not a statement about who performs this task today or should continue to perform it. What an organisation does with the freed-up hours falls outside this analysis; decisions that affect personnel have their own legal requirements and should not be assessed here.
The eight axes used here -- from structuredness to compliance -- form the fixed basis of every task assessment. How that assessment exactly works is explained on a dedicated page, for those who want to see where the scores come from. For those who want to look beyond this one task: which work in logistics AI can take over provides an overview of that.
For freight documents, the following applies: the structured, high-volume part lends itself to automatic generation, the exceptions still require a checking eye. How much of your document flow falls into the first part and how much into the second differs per company and depends on how uniform your shipments are.
The free quickscan gives a first indication of that: twelve questions, no account needed, with an estimate of the share of hours in your profile that AI can take over today. The full work scan, which breaks down the work of an entire company into tasks and calculates it into fte capacity, is still under construction.
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Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.