What does automated order entry in transport look like in practice?
A customer sends an order. Sometimes via EDI, but more often as a PDF attachment or as text in an email. Normally, a staff member opens that message, reads the loading and delivery address, the weight, the number of parcels, the reference, and the requested date, then re-keys it all into the TMS. At twenty orders a day, that is manageable. At a hundred, it becomes a full-time job. With automated order entry using AI, an AI Worker takes over the keying: the system reads the incoming message, extracts the relevant fields, and creates a draft order. The planner sees the order already filled in and only needs to review and approve. The groundwork is done; the decision stays with the person.
Why does standard EDI not fully solve this problem?
EDI is valuable for large shippers that support it. But the average transport company has dozens of clients: from large businesses with a full EDI setup to small and medium-sized companies sending a homemade PDF template. That mix is not going away. You cannot require all your customers to implement EDI, especially when low barriers to entry are part of the relationship. So there will always be a group of orders processed manually. That group is exactly the bottleneck. Automated order entry with AI addresses that remaining group without requiring any changes on the customer's side.
What is the difference from AI email processing?
Email processing is one part of the broader picture. Automated order entry covers the entire incoming order channel: emails, PDF attachments, portals, and sometimes even screenshots. The AI recognises the document or message regardless of format and converts the content into structured data that flows into the TMS. Email processing focuses specifically on email as a channel. Order entry is broader and connects seamlessly to the planner's workflow, including the step where a staff member reviews the approved result and corrects it where needed.
When does it work well, and when does it not?
Automated order entry works well when incoming documents have a recognisable structure, even if the template varies by customer. Fixed fields, repeat customers, documents with a clear layout: those are the ideal conditions. It becomes more difficult when orders are written entirely in free text, where the customer describes what needs to happen in prose without any structure. That requires a different approach or additional effort. To be straightforward: not every order flow is suited to full automation. Sometimes partial automation is already a significant step forward: the AI fills in the most common fields and the exceptions go to a staff member. That is still far less manual work than doing everything by hand.
What do you need to get started with AI order entry in transport?
The barrier is lower than most companies expect. You do not need a data platform, a large IT department, or years of preparation. What you do need: a clear picture of the five to ten most common order formats you receive, a representative set of historical documents to test against, and a planner who reviews the output critically in the first few weeks and flags where the AI goes wrong. Integration with the existing TMS typically runs via an API or file exchange. We build this as an AI Worker that runs on top of the existing system. You do not need to replace the core system to make this work. If the volume or complexity is greater, a fully redeveloped TMS may offer more value, but that is a separate decision.
Automated order entry in transport: what is being searched internationally
We see that outside the Netherlands, more and more transport companies are searching for 'automated order entry transport'. The question is the same: how do you stop manual re-keying without forcing all your customers to change anything on their end? The answer is also the same. An AI layer that processes the incoming flow regardless of format, and prepares the result for human review. This is not a future prospect. It is already running in production at companies dealing with exactly that mix of structured and unstructured orders.