Modern logistics showcases advanced technology: telematics, real-time route optimisation, freight exchanges operating in fractions of a second. And yet, at the very end of this digital chain, the process often hits a wall: a crumpled CMR consignment note that someone still has to manually retype into the system. How do modern data-extraction technologies solve this bottleneck in accounting and forwarding departments?
In transport and logistics, time is not just money — it’s cash flow. Before a transport company can issue an invoice for a completed job, it must have a full set of documents confirming delivery (Proof of Delivery – PoD). In practice, this means waiting for paper documents to make it back to base, or relying on photos sent by drivers. And that’s where the problem starts: photos are often taken in poor lighting, at an angle, and the documents may be dirty or creased.
Why traditional OCR isn’t enough
For years, the industry tried to solve this problem using OCR (Optical Character Recognition) systems. While this technology performs very well with high-quality scans, it often fails in real-world transport conditions.
Traditional OCR relies on so-called templates. The system has to know that, on an invoice, the VAT number is in the top-left corner and the amount is in the middle. All it takes is for a driver to take the photo at a different angle, or for the consignee’s stamp to partially cover the shipment weight, and the system gets confused, throws an error, and the process returns to square one: manual data entry by an office employee (“typing it in”).
This is exactly the stage that puts the brakes on scaling the business. Transport company owners often find that the barrier to expanding the fleet isn’t a lack of orders, but administrative overload and the costs associated with growing the back-office team.
A new generation of extraction: context instead of a template
The answer to the limitations of traditional OCR is Intelligent Document Processing (IDP), which uses artificial intelligence models (including language and vision models). The difference in how it works is fundamental.
Modern algorithms don’t look for data at specific coordinates on a page. Instead, they “read” the document by understanding context, much like a human.
If the system sees a string of digits next to words such as “mass”, “weight”, or the abbreviation “kg”, it understands that this is the shipment weight — regardless of whether the information is at the bottom, the top, or on the back of the document.
The algorithms can digitally “straighten” a photo taken at an angle and remove noise (for example, shadows in a truck cab).
Understanding the structure makes it possible to export clean, structured data (for example, in a universal JSON format) directly via API into TMS (Transport Management System) platforms or accounting software.
The “human in the loop” principle — a human still in the driver’s seat
A lot of myths have grown up around artificial intelligence, and one of the most dangerous is the promise of 100% hands-off automation. In financial and logistics processes — where an error in a single digit on an invoice can lead to legal issues or payment delays — fully autonomous systems carry too much risk.
That’s why professional AI implementations in logistics are built on a human-in-the-loop (HITL) architecture.
How does it work in practice?
Extraction and confidence scoring: The algorithm reads the document and assigns a confidence level to each data point.
Automatic flow (straight-through processing): If the system is 99% confident in the reading (for example, a clear VAT number, a standard invoice), the data goes straight into the finance system without human involvement.
Verification (human in the loop): If the document is badly damaged or the consignee’s handwriting is illegible, the system doesn’t guess. It flags the uncertain field (for example, in red) and sends the task to an employee for verification.
As a result, a forwarder or accounts staff member doesn’t waste time retyping hundreds of standard documents, and their role shifts from “data entry clerk” to an expert who verifies only difficult cases — the so-called edge cases. The human oversees the process, and the machine does the heavy lifting.
Measurable results for transport companies
Implementing IDP-class systems in a logistics company delivers three main, measurable business benefits:
Faster cash flow: The ability to issue an invoice and start the payment process immediately after the driver sends a photo of the CMR consignment note from the road shortens the receivables cycle (DSO – Days Sales Outstanding), often by several days.
Operational scalability: The business can increase the number of jobs handled and expand its fleet without having to increase headcount in administration and accounts in line with that growth.
Error reduction: Eliminating the “human factor” in tedious, repetitive tasks reduces the risk of mistakes such as typos or incorrect amounts, which later require time-consuming corrective invoices.
Automation with human control — the direction of travel for the industry
Document digitisation in transport and logistics is no longer just an innovation — it is gradually becoming a market standard. AI-based technologies are no longer only for global corporations. Thanks to the development of cloud models and open APIs, they are now also accessible to mid-sized and smaller operators. The key to success, however, is not blind faith in automation, but a smart combination of what algorithms can do with human oversight.
Author’s note: The analysis above is based on experience from the Polish transport and logistics market, as well as observations from implementations delivered by the Dokum.ai team, which specialises in automating document processing.
