AI in logistics already works where it hurts most: reading and validating the paperwork behind every shipment. What it does today, what it doesn't and how to evaluate it.
AI in logistics makes headlines with autonomous vehicles, robotic warehouses and demand forecasting. But for most freight forwarders, customs brokers and logistics operators, the real change is happening somewhere far less glamorous: the inbox. Every day, hundreds of documents arrive there with data someone has to read, understand and type into another system.
That is where artificial intelligence already works reliably today, with a measurable return. This article covers what AI can do in the logistics back office, where its limits are and how to evaluate a solution before putting it to work on your documents.
Leaving the experimental aside, three capabilities are already used in production and reinforce each other. On their own they save time; together they change how the team works.
An AI model trained on industry documents identifies and extracts the relevant fields from a BL, an AWB, a CMR, a carrier invoice or a customs declaration, even when every supplier uses a different format and even when the document arrives scanned. It's the foundation for everything else, and it's what K-Scan does.
Extracting the data is half the job. The other half is checking it: does the amount match the agreed rate? Does the package count match the shipment file? Is a mandatory document missing? AI flags what doesn't fit so the team only reviews that, with the reason already identified.
Identifying what kind of document has arrived, which shipment file it belongs to and which person or system it should go to, without anyone opening the email to decide. It sounds minor, but for a team receiving documents through several channels it's one of the biggest unmeasured time sinks.
94% of invoices processed automatically, with no manual intervention.
The same technology applies differently depending on the department. These are the most common uses at freight forwarders and importers:
AI opens shipment files from the BL, AWB or CMR received, without retyping consignee, ports, packages or weights. It also catches mismatches between the booking and the transport document before they become a problem at destination.
It extracts data from the commercial invoice and packing list to prepare the declaration, and checks that the shipment file has every required document before clearance. The result is less back-and-forth with operations and less risk of an error that forces an amendment.
It reads transport supplier invoices and reconciles them against rate and shipment file. It's the use case with the easiest return to measure: every invoice that goes through on its own is time saved, and every surcharge caught is money not lost.
It reconciles invoice, purchase order and landed cost without the intermediate spreadsheet, so clean data reaches the ERP and the real cost of each operation is known without waiting for month-end.
Picture a normal morning in the operations team. In come arrival notices, BLs from several shipping lines, customers' commercial invoices, the odd scanned CMR and emails that bundle attachments from different shipment files.
Without AI, someone opens each email, decides what each document is and which shipment file it belongs to, and copies the relevant data into the TMS. If a document arrives in a new format or is badly scanned, it takes longer. When things get busy, less gets checked, and errors surface days later.
With AI, each document is classified and read on arrival. Its data is matched to the right shipment file by reference and compared with what's already in the TMS. The team receives a short list: documents that couldn't be matched, data that doesn't agree and data that's missing. Everything else is already loaded.
The work doesn't disappear. It changes nature, from typing to deciding, and that's exactly the part that needs the team's experience.
Many companies tried template-based OCR years ago and were left disappointed. The difference with a current AI engine isn't subtle, it's practical:
In practice, this decides whether automation covers your top ten suppliers or all of them, including the small ones that send poorly standardized documents.
It pays to be honest so you don't buy hype. A good implementation starts from knowing what the technology won't solve:
Demos always work. What matters is how the solution behaves with your real documents and how it fits into your systems. These four criteria help separate what works from what only looks like it does:
Before handing customer documents to any AI system, it's worth clarifying with the vendor where they're processed and stored, how long they're kept and whether they're used to train models that then serve other customers. Also what audit trail remains (which data point came from which document, who validated it and when) and what happens when the AI isn't sure: whether it flags the data for review or accepts it without warning.
The regulatory framework points the same way. The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) sets transparency, human oversight and risk management obligations proportionate to how AI is used. A serious vendor should be able to explain plainly how it meets them.
A first document AI project needs less than you might think: a real sample of documents from the chosen process, clarity on which system the data must reach, and someone from the team to define what counts as an exception and who resolves it. With that, a scoped implementation is measured in weeks; at Kaiona, between 8 and 10.
The best way to see AI in logistics is as extra capacity: the same team handles more shipments, with fewer errors, applying its experience where it's actually needed. It's one piece of a broader strategy we explain in logistics digitalization: what it is and where to start, and its most immediate impact is usually seen in automated invoice processing.
.png)
