Document Intelligence: from paper to ERP data

In many manufacturing businesses, the administration team spends a large part of its time processing documents. Invoices arrive as PDFs in the inbox. Delivery notes are scanned or entered on receipt. Suppliers’ quality certificates are printed and filed, or end up in a shared folder where nobody can find them. Consignment notes move between logistics partners in five different formats. It is work that must be done, work nobody wants to do, and work that has not fundamentally improved for years.

Something has changed over the past eighteen months. Not through a single breakthrough, but through a combination of OCR that has become fundamentally more reliable and LLMs that understand document context in a way rule-based software never could. For manufacturing and logistics SMEs, that means a large category of work can be routed to AI, with human review only for exceptions.

What Document Intelligence actually is

The term is used loosely. What we mean by it, and what works in production environments, is a pipeline with three steps.

First, the document is read while preserving its structure. Tables remain tables. Addresses, amounts, dates, order numbers, and supplier details are recognised. Scans and messier PDFs can often be processed too, although document quality remains the key factor for reliability.

Next, the fields are interpreted. A system needs to understand that “subtotal excluding VAT”, “net amount”, and “amount before VAT” can in practice be the same field. This works better when the model is validated against your own document types and suppliers.

Finally, the result is connected to your process. A PO number is compared with an open order. Quantities are checked. VAT totals are recalculated. When there is doubt, the document goes to an exceptions queue. When confidence is sufficient, it is prepared for processing in your system.

What it delivers in practice

For businesses where this works well, the gains mainly come from less manual data entry, shorter turnaround times, and better control over exceptions. Invoices, delivery notes, and CMR documents no longer need to be retyped line by line; they are prepared for review and processing.

The exact return varies greatly by situation. Document quality, supplier variation, ERP configuration, volume, and the level of control you want determine how much straight-through processing is justified. We therefore do not give general percentages, but provide an estimate based on your own figures.

For an administration team, this usually does not mean “people out”, but capacity returned to work that deserves more attention and creates value: supplier management, customer questions, exceptions, and process improvement. That is the scenario that realistically works for SMEs in the Netherlands, where labour shortages are often the problem, rather than overcapacity.

Where it does not work

Document Intelligence is not a cure-all, and an honest assessment is essential to set realistic expectations.

Poorly scanned documents remain difficult. You will never fully automate the processing of a crumpled delivery note with faded text. A person needs to look at it, and you should design that into your process instead of fighting it.

Highly unusual document structures in niche sectors require extra configuration and validation. Standard purchase invoices are often ready for automation sooner than a specific food safety certificate from a supplier with a unique layout found nowhere else. Those documents may still be suitable, but require more examples, tighter checks, and sometimes a longer pilot.

Documents with compliance requirements need an audit trail. Not “this is roughly how we do it”, but an actual documented chain in which every automated decision can be traced back to its input. We include that trail as standard, but it increases implementation complexity, especially in pharmaceuticals, food, and financial sectors.

What a realistic implementation looks like

In a typical project lasting six to eight weeks, we see roughly this pattern.

Weeks one and two are discovery. We map your document flow: which types arrive, in what volumes, from which suppliers, and into which ERP fields. We take the top seven suppliers by invoice volume because they usually represent 70 to 80 percent of the total.

Weeks three to five cover model development and integration. We train the extraction model on your historical data, build the API connection to your ERP, and set up a staging environment where we run alongside your current process. That parallel run is crucial: you see the comparison between human processing and the model, on your own data, before you commit.

Weeks six and seven are UAT and write-back. Your team validates the exceptions flow, we fine-tune for the patterns we see under production-like load, and we enable write-back to the ERP for the high-confidence categories.

Week eight is go-live, with human supervision of every automated posting for the first two weeks. After that, it becomes sampling.

Where we would start ourselves

If we were in your position and wanted to test whether this works for your business, we would not start with a major ERP replacement or a platform choice. We would start with the AI Readiness Scan, which maps in 10 minutes which of your document flows offer the greatest return from automation, and what realistic ROI you can expect at your specific scale and in your sector.

Then we would start small, with no more than two document types and the top five suppliers, and scale only once those work. The good thing about document processing is that you can prove the business case for each use case before putting the next one live.

For manufacturing and logistics SMEs, this is currently the clearest route to concrete AI impact. No generative magic, no demoware. Practical work that simply gets done, allowing your team to spend its time on something more useful.

Read more about our approach to Back Office & Operations or visit the broader Manufacturing & Logistics sector page for the full context.

A note on how this was made: our content is AI-assisted. We use AI for research and drafting a first concept. A human reviews and edits the text and takes responsibility for everything we publish. Read how we make our content.