Almost every company we talk to has someone (sometimes half the back office) dedicated to copying supplier invoices by hand: opening the PDF, reading the amount, looking up the supplier in the system, entering each line with its tax, and hoping it all adds up. It's repetitive, rule based work, which is exactly why it's one of the best candidates for AI automation out there.
Here's how we actually do it, step by step, with a real case behind it.
Why invoice entry is a poor use of your team's time
It's not just slow. Entering invoices by hand has three underlying problems:
- It's prone to human error. A mistyped digit, a tax applied to the wrong line, a supplier duplicated under a slightly different name. Small mistakes that, added up, throw the books off.
- It doesn't scale. If you double your purchase volume, you roughly double the admin hours spent typing.
- It ties up skilled people in mechanical work. An accountant's or admin's time is worth more reviewing exceptions than copying amounts.
The good news is that it's a process with clear rules (supplier, amount, tax, account), and that's exactly what AI does well when it's properly integrated into your systems.
The steps to automate it, for real
This isn't theory: it's essentially what we built for a veterinary group that received invoices from dozens of different suppliers every week. You can see the full case here.
Step 1: Centralize the intake
Before automating anything, you need to decide where invoices come in: a dedicated mailbox, a shared folder, or a direct feed from the supplier. If today invoices arrive through five different channels and each one is handled differently, the first job (and sometimes the most important one) is unifying that intake. Without a clear entry point, there's nothing stable to automate.
Step 2: OCR plus AI read and extract the data
Once the invoice is centralized, an OCR engine combined with AI reads the document (whether it's a native PDF or a scanned photo) and extracts the fields that matter: supplier, invoice number, date, taxable base, taxes and total. AI adds something OCR alone can't: it understands the document, not just the text. It can tell a discount apart from a product line, or a 21% VAT rate from a 10% one on the same invoice.
Step 3: Match the supplier and apply their rules
Every supplier has their own invoice format and, sometimes, their own terms. The system matches the invoice against the supplier already registered in your accounting software (or flags it as new, for a one time human review) and applies the rules that apply to them: account code, payment terms, tax type per line.
Step 4: Reconcile taxes line by line, not just the total
This is where a lot of "quick and dirty" automations fall short: they trust the total and stop there. A properly built automation recalculates the tax on each line and checks that the sum matches the invoice total exactly. If a supplier applies an unusual discount or an exempt line, the system catches it instead of assuming every line works the same way.
Step 5: Verify before posting (and don't post if it doesn't add up)
This is the step that separates "automation that saves time" from "automation that saves time but introduces new errors." Before posting the invoice to your accounting system, the process checks the recalculated total against the document's total. If it matches, it posts itself. If it doesn't, nothing gets posted blindly: the attempt is deleted and flagged for human review. We'd rather have an invoice waiting to be reviewed than one that's wrongly posted and goes unnoticed until closing.
In the veterinary group's case, this runs on top of Holded, but the same principle applies to any accounting software or ERP: SAP, A3, Sage, Odoo.
Checklist before automating your invoices
| Question | Why it matters |
|---|---|
| Do you know how many channels your invoices come through today? | Without a unified intake, there's nothing stable to automate |
| Are your suppliers cleanly identified in your system, no duplicates? | A duplicated supplier breaks automatic matching |
| Do you have clear tax rules by product or service type? | AI applies rules, it doesn't invent them: define them first if they don't exist |
| What happens today when an invoice doesn't add up? | Define that exception flow before automating, not after |
| Who reviews invoices flagged as doubtful? | Automation reduces volume, it doesn't remove the need for oversight |
Common mistakes when automating invoices
- Automating before cleaning up your supplier master data. If your supplier records are a mess, the AI inherits that mess.
- Trusting only the invoice total. As mentioned, you need to reconcile line by line, or errors slip through anyway.
- Not defining what happens with exceptions. Automation without a clear flow for "this doesn't add up" ends up creating hidden manual work, which is worse because nobody keeps track of it.
- Treating it as an isolated project. Invoice entry usually connects to purchasing, stock and cash flow. It's worth looking at it within the context of all your data, not as a standalone piece. We wrote about this in why data comes before AI.
What we never automate without oversight
To be transparent: some decisions we always leave to a person. If an invoice doesn't add up, if a supplier is new, or if the amount is well outside what's usual for that supplier, the system doesn't decide on its own, it flags it. AI here doesn't replace accounting judgment, it makes sure that judgment gets applied only where it's actually needed, instead of on the 200 invoices that were routine anyway.
The next step
If someone at your company is still typing invoices by hand, there's probably more than one job hidden in there that can be automated reliably, verifying every cent along the way. We've built this before and we know where the traps are.
You can see how we built it end to end in the veterinary group's case, or check the rest of our services if you want a custom automation for your own process.
How much time does your team spend typing invoices? Tell us about your case and we'll tell you, with no strings attached, whether it makes sense to automate and how we'd do it.
