Short answer: in accounting, AI takes over the retyping, not the decisions. Four jobs pay off most: entering purchase documents from PDFs and photos, matching bank statements to invoices, debt reminders, and recurring client questions about documents. The threshold where it's worth starting is roughly 600 documents a month. Below that, it's usually cheaper to use what your accounting software already has.
Four jobs that pay off most
All four share the same trait: they repeat every day, they can be written down like a recipe, and it's clear when the result is right.
- 1. Entering purchase documents. An invoice arrives as a PDF or a photo from a phone. The system pulls out the supplier, amount, VAT, date and line items, assigns the chart of accounts by your rules and enters it into the software. This is the single biggest win in accounting.
- 2. Matching bank statements. Every program can import a statement. The trouble isn't the import, it's that each payment has to be linked by hand to a specific invoice, especially when it's paid in parts, the amounts don't match or the payment reference is written in free form. That linking is exactly what's worth handing off.
- 3. Debt reminders. The system sees who's late and sends a reminder itself, in the right tone and at the right time, leaving escalation to a person. This one usually pays off not in time saved but in money coming back faster.
- 4. Client questions about documents. At accounting firms this is a pain of its own: “did you get my invoices”, “how much do I owe this month”, “when is the declaration due”. Same questions, same answers, and they chop the day into pieces.
In accounting you automate the fingers, not the thinking. Deciding how to book an unusual case was and stays the accountant's job.
Where AI gets accounting wrong
I'm writing this part because staying quiet about errors in systems that touch money is dishonest. AI makes mistakes here, and fairly predictably:
- Poor-quality documents. A crumpled receipt photographed at an angle in bad light, and recognition quality drops.
- New suppliers with an unusual format. The first few times, the system may miss where on that form the amount excluding VAT is and where the amount including it is.
- Unclear payment references. When a client pays a round sum for three invoices and writes nothing, there's no correct answer to work out. Someone has to know it.
So a well-built system is made to flag the cases it isn't sure about itself and pass them to a person instead of quietly entering a guess. In practice it looks like this: 85-90% of documents go through on their own, the rest are lined up in a queue for review. Review is part of the system, not an add-on. And anyone promising 100% accuracy with no human involved has either never worked with real documents or is lying to you.
What the numbers look like
Take an accounting firm that enters 1,500 documents a month and spends an average of four minutes on each, all checks included. That's 100 hours a month.
| By hand | With AI | |
|---|---|---|
| Working time per month | 100 h | ~15 h of review |
| Cost ($46.89/h + our price) | $4,689 | up to $1,000 |
| Difference per month | ~$3,689 | |
| Per year | ~$44,300 | |
“With AI” is calculated on our model: up to $10 for each hour of AI work, against $46.89 an hour for a person, the average US private-industry employer cost per hour worked, wages plus benefits (BLS, June 2026). You can run your own numbers in the calculator.
And now the other side. If your company enters 150 documents a month, that's about 10 hours. Then the math goes like this: the hours saved are worth about $470, while our monthly minimum is $300 and setup comes on top, so the payback would take years. In that case automating isn't worth it, and I say exactly that. Better to use the document scanning feature your accounting software most likely already has.
Integrations: what actually needs connecting
Lithuanian companies work with different systems: Rivilė in one place, B1, Directo, Finvalda (local accounting programs) or some foreign one in another. The question is always the same: can your software be reached automatically, or only through the screen?
The answer is usually one of three. The software has an interface for receiving data; the data can be handed over as a file the software imports; or there's no way in, and then the system works the way an employee would at the screen. The third option works, but it's slower and more fragile, so it's worth finding out which case is yours before you start.
Add to that the channels documents come in through: email, photos uploaded by clients, e-invoices, bank statements. More channels means more work, and it's the integrations that drive the price, not the “intelligence” itself.
Data and responsibility
In accounting, data is sensitive almost by definition: client invoices, salaries, contracts. Three things worth checking before you start:
- Where the documents are stored and whether they're taken outside the EU.
- Whether the data is used to train the model. That has to be in the contract, not promised verbally.
- Whether you can see what the system did: an action log you can review, including in case of an audit.
I've written this up in more detail separately: AI and GDPR.
Where to start
Start with one channel, not the whole process. In practice this order works best: first, purchase invoices arriving by email from regular suppliers. That's the biggest volume, the fewest exceptions and the most clearly visible result within a couple of weeks.
Once that works, the other channels and more complex cases get connected. How to pick the first process in general, I've written up separately.
Questions about AI in accounting
What can AI automate in accounting?
Four jobs pay off most: entering purchase documents from PDFs and photos, matching bank statements to invoices, debt reminders to clients, and recurring client questions about documents. What they have in common: rule-based retyping, not decisions.
Can AI replace an accountant?
No. AI takes over the mechanical part: entry, matching, reminders. Decisions on accounting policy, tax nuances, advice to the client and responsibility for the declaration stay with a person. In practice the accountant moves from data entry to reviewing and advising.
Does AI make mistakes when entering invoices?
Yes, which is why review is part of the system, not an add-on. Errors most often come from poor-quality photos, unusual document formats and new suppliers. A well-built system flags the cases it isn't sure about itself and passes them to a person instead of quietly entering a guess.
At what document volume does automating accounting pay off?
Roughly from 600 documents a month, if each takes a few minutes. That's about 40 hours of work. For smaller volumes, the ready-made document scanning features that accounting software already has are usually cheaper, or simply changing nothing.