Every invoice ends up somewhere: via email, as a PDF in a folder, as a scan from your phone. And then the same stress often begins: typing in data, choosing accounts, checking VAT, filing the document – and it repeats week after week.
This is exactly where AI automation shows its strengths: it takes over the routine, works through a clearly defined process step by step, and leaves you with a clean result in your accounting system. You don’t have to “be technical” – you just need to understand how the process is structured.
AI automation doesn’t mean “magically solving everything.” It means designing a clear workflow where AI takes over the tasks people would otherwise do manually – for example, reading information from an invoice and returning it in a structured format.
The key is the combination of two elements:
Invoices are tricky because they are not standardized. Every supplier uses different layouts, different wording, different VAT notes – and sometimes multiple currencies.
Typical time drains:
For AI to work reliably, it needs a clearly defined process. Think of it like an assembly line: each station does exactly one thing – and at the end, the invoice is booked and properly stored.
It starts with a simple rule: “Which invoices are new and not yet processed?” Instead of handling everything twice, the process clearly marks invoices (e.g. “imported: yes/no”).
The invoice is loaded from your storage system (e.g. a cloud folder). Then something happens that is standard in good automation setups: an integrity check. This ensures the file is really the correct one and was not corrupted in transit.
Before booking line items, the process answers a central question: What currency is the invoice in – and what is the total amount?
This is crucial because accounting systems often link suppliers and bills to currencies. If something goes wrong here, you end up with messy issues later (wrong exchange rate, wrong supplier, error messages).
Here’s a practical detail where many automations fail: Supplier ≠ just a name. The process ensures the supplier exists in the correct currency. If you receive an invoice in USD, the supplier must also be set up for USD – otherwise you get duplicates or inconsistencies.
Now comes the part that feels like “magic” – but is actually just a clear instruction: “Return the line items as a clean, structured list.”
To make this reliable, the AI receives rules. For example: “If the description contains hosting/server/cloud, book it to account X.” Or: “If it’s reverse charge, VAT is 0.”
The result is a structured list with:
Using the AI data, the process creates the actual booking in your accounting system. Then the original PDF is attached directly to the booking. It sounds simple – but it’s incredibly valuable when you need to review transactions later.
A good automation setup assumes errors will happen. But it ensures that not everything stops. Instead, the individual invoice is marked as an error – including a note about where the issue occurred (e.g. PDF fetch, currency, supplier, AI extraction, attachment).
Because it doesn’t try to do “everything at once.” It’s like a solid recipe: first check the ingredients, then prepare, then cook, then serve. And if one ingredient is missing, you don’t throw away the entire menu – just that one dish.
If you process invoices regularly and don’t want your team stuck in copy-paste tasks, this is a classic low-hanging-fruit process.
You don’t need to be a tech expert. What matters is that the process is clearly defined.
The most important point isn’t the technology – it’s the workflow. If you build a fixed process like Fetch PDF → AI extracts data → System creates booking → Attach document → Log errors cleanly, invoice processing becomes a calm, predictable background task.
