Typing out incoming invoices eats up hours every week. How Document AI reads, checks and posts your documents, and what matters when you put it into practice.
In many companies, every incoming invoice first goes through manual work: open it, read off the figures, type them into the system, match them against the order, forward it. This is the kind of routine that eats up hours every week and gets no one anywhere. This is exactly where Document AI comes in, and it is often the first process we recommend.
Document AI reads the contents of a document, such as the invoice number, amount, supplier and line items, and transfers them into your system in a structured form. Unlike a rigid template, it also handles different layouts, because every supplier designs its invoices differently.
A typical automated invoice intake runs in just a few steps:
People no longer type things out; they only make decisions in the cases that genuinely call for one. That is the difference between fast work and unnecessary work.
Invoice processing meets every condition for a good first step: it happens often, follows clear rules, and mistakes are expensive, from a missed early-payment discount deadline to a duplicate payment. That is why automation pays off particularly quickly here.
It is important to keep the human at the right point. A serious solution does not post blindly, but sets thresholds above which a check happens. The clean connection to your accounting system, whether DATEV or another, matters just as much, so that no new island is created. Equally important is how exceptions are handled: what happens with an unknown invoice, a missing order reference, an odd amount? A good solution has a clear path for these cases instead of simply coming to a halt. Only that turns a demonstration into a process that holds up in everyday use.
Which tools are suitable depends on the use case. For some guidance, see our tool comparison, and our use cases show further workflows.
The benefit arises in two places. First, time: what previously cost minutes of manual work per document now runs in the background. With several hundred invoices a month, that quickly adds up to days. Second, errors: no transposed digits when typing, no duplicate payment, no missed early-payment discount deadline. The second point in particular is often underestimated, because individual errors are expensive and hard to trace.
Part of a clean solution is that every invoice is stored in a traceable and unalterable way, together with a log of who approved what and when. This is not just tidiness, but a prerequisite for the automation to meet the requirements of proper bookkeeping.
With invoice processing too, the narrow start pays off. Instead of capturing every document from every supplier right away, you begin with a handful of suppliers whose invoices arrive frequently and follow a clear pattern. Once that runs reliably, further suppliers and document types are added, such as credit notes or reminders. That way you see a result early, and the solution grows on real cases rather than on assumptions. Closely tied to this is the data quality in the background. An agent matches invoices against your master data, and the matching only runs smoothly when suppliers and accounts are cleanly maintained there. It is therefore worth taking a look at the master data before the start and cleaning up obvious duplicates or gaps. This preliminary work is unspectacular, but it determines how many cases ultimately go through without a query.
Yes. Unlike rigid templates, Document AI handles different layouts and reads the relevant fields even when every supplier structures its invoices differently.
No, and that is intentional. Clear cases go straight through; the ones that stand out go to a person for approval. You set the thresholds above which a check is required.
As a rule, yes. A clean connection to your existing accounting system is part of putting this into practice, so that you avoid double data entry and no new software island is created.
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In a free initial assessment, we look at your document flow and tell you what can be automated and what it saves.
