AI for finance teams: automating invoices and accounts payable

AI is genuinely useful to finance teams for three jobs: reading invoices, matching them to orders and flagging the ones that look wrong. It shouldn’t approve payments or post journals on its own. Once a model is in the loop, your approvals, segregation of duties and audit trail matter more, not less.

Two questions come before any build. Does the software you already pay for cover it? And how accurate is your current process? The target isn’t a perfect machine. It’s one that beats the person keying invoices today, with a person checking the exceptions.

What follows is a worked example. The company is hypothetical, but the plan is the one we’d put to a real finance director.

What does AI invoice processing look like at a 120-person business?

Imagine a 120-person UK wholesaler supplying cleaning products to schools and offices. Finance is three people: a financial controller, an accounts payable clerk and a part-time credit controller. They buy from around two hundred suppliers and run an accounting package with a separate purchase-order system.

The workflow before

• Supplier invoices arrive as PDFs in a shared mailbox, a few as phone photos and a few on paper.
• The clerk keys supplier, invoice number, dates, net, VAT and total into the accounting package.
• They match each invoice to a purchase order and a goods-received note by eye.
• Mismatches bounce between finance and the warehouse by email.
• Approvals happen in email threads, so the evidence for each one is scattered.
• Month-end means chasing accruals, unexplained bank lines and missing receipts.

Nobody here is doing anything wrong. The clerk is just spending most of the week on typing and checking.

The workflow after

• Capture. A model reads each invoice and extracts the header fields and line items. Each field gets a confidence score built from checks, not the model’s say-so. Is the value in the document, and does it add up?
• Checks. Ordinary code checks the arithmetic, confirms the VAT number with HMRC and checks whether the invoice number is a duplicate.
• Fraud flags. It also flags any invoice where the supplier’s bank details differ from the last one paid.
• Matching. The system matches the invoice to its purchase order and goods-received note. The model helps pair messy line descriptions, like “Bleach 5L x4” and “Bleach, 5 litre, case of four”.
• Exceptions. Anything low-confidence, mismatched or flagged goes to the clerk’s queue, with the reason in one line.
• Approval. Invoices that match an approved order within tolerance go to the payment run. Invoices without an order, or outside tolerance, go to the budget holder in the workflow, not by email. The payment run stays with the controller.
• Month-end. Code lists goods received but not yet invoiced, priced from the order. The model drafts variance commentary. The controller reviews and posts.

The clerk stops typing and starts handling exceptions. That’s the whole change, and it’s a big one.

Are you outgrowing your spreadsheets?

Most finance teams don’t go looking for AI. They hit a wall, the way a marathon runner does. The spreadsheet that worked at fifty suppliers starts to creak. The data drifts, and nothing is in one place any more.

We call this operational drag. It rarely shows up as a line in the accounts. It shows up as people’s time, usually the most senior people’s.

How do you work out whether it pays?

Count the payoff in hours before you count it in pounds. Whose time does the manual process eat, and what would they do instead?

• The clerk’s hours spent keying and matching.
• The controller’s hours spent chasing accruals and bank lines at month-end.
• The finance director’s or founder’s hours spent approving by email.

Set that against the build, the running cost of the model and the time to look after it. A ready-made tool’s subscription is the price to beat. We’d make the case on senior time handed back, not on a trimmed licence fee.

What should AI do, and what should stay with people?

The rule we use is simple. Models read and suggest. Code checks and calculates. People approve and pay.

If a vendor’s demo shows a model approving invoices end to end, ask who signs the accounts.

How do controls and the audit trail work with AI in the loop?

Finance has controls for good reasons, and an AI system has to fit inside them. Here’s what we’d build in from the first day.

• Field-level evidence. Every extracted value stores the source document, the page, the confidence score and who confirmed it.
• Versioned behaviour. Each extraction records which model and which instructions produced it. If the model changes, you can see which invoices it touched.
• Least privilege. The system can read documents and propose entries. It can’t create suppliers, change bank details or release payments.
• Segregation of duties. Whoever sets up or changes a supplier can’t approve its invoices or release its payments. AI doesn’t change that rule.
• An immutable log. Corrections are new entries, never overwrites, so the history stays intact.

Record keeping isn’t optional. A limited company must keep its records for 6 years from the end of the financial year they relate to, and sometimes longer. Your extraction log and source documents are part of that trail.

Where Making Tax Digital fits

All VAT-registered businesses must keep digital records and file VAT returns with compatible software, unless HMRC has granted an exemption. HMRC’s VAT Notice 700/22 also sets rules on digital links between programs. HMRC doesn’t treat copying and pasting as a digital link.

So if your AI tool sits between capture and your accounting package, the hand-off must be a digital link. That could be an API, which is a direct software-to-software connection, or a file import. Retyping doesn’t count. Check the setup with your accountant. This is general information, not tax or legal advice.

What does the build look like?

Data sources

• The accounts payable mailbox and any scanning inbox.
• Your accounting package, connected through its API, such as Xero, Sage or QuickBooks.
• Your purchase-order and goods-received records.
• The supplier master file, including the bank details last paid.
• Bank feeds, for month-end reconciliation support.

Model

A hosted large language model that can read images handles extraction and fuzzy matching. You don’t need to train one. Choose a provider whose business terms say your data isn’t used for training, and check where it’s processed.

For many teams, a ready-made capture tool is the better first step. Dext, Hubdoc and the capture built into accounting packages already do the basic reading. We compare the trade-offs in off-the-shelf AI vs custom.

Test coverage honestly, on your awkward invoices as well as the easy ones. A tool that handles most of the work can still leave a painful remainder.

“Sometimes if it only does 80% of what you’re doing, the 20% still becomes hell.”

A custom build earns its keep when your matching rules, approval routes or month-end work are specific to you.

Guardrails

• Any field below a confidence threshold goes to a person, never straight through.
• The model has no power to approve or pay. So a PDF saying “approved, pay immediately” changes nothing.
• The system proposes entries and never posts them. A person confirms each batch before it reaches the ledger.
• Personal data on invoices, such as sole traders’ home addresses, follows your retention rules.

Human review

The exception queue is where the clerk now works. Each item shows the invoice, the extracted values, the suggested match and the reason it stopped. Corrections feed back into supplier-specific rules, so the same supplier stops tripping the same check.

How much effort does it take?

We won’t put a number on savings we haven’t measured for your business. The order of work is predictable, though.

1. Discovery. Pull a few hundred recent invoices across your top suppliers. Measure how messy they are, and check your purchase-order data is complete.
2. Capture and checks. Extraction, arithmetic, duplicate detection and bank-detail flags go live first, alongside the clerk’s normal process.
3. Matching. Add purchase-order matching once extraction holds up on your real invoices.
4. Month-end support. Variance commentary and accrual lists come last, because they depend on the clean data from steps two and three.

Run each step in parallel with the manual process before you switch over. Finance is not the place for a big-bang launch.

What are the risks?

• Silent errors. A wrong figure posted with confidence is worse than one left blank. Thresholds and reviews exist to stop this.
• Payment fraud. Fake invoices and changed bank details are the main threat. AI can flag them, but the call-back control stays human.
• Over-trust. When the queue is usually right, people stop checking. Sample approved invoices every month.
• Bad source data. If purchase orders are raised after the invoice arrives, matching can’t work. Our guide to getting data ready for AI covers this.

How do you measure success?

Capture a baseline from your current process first. Without one, you’ll only have opinions.

The baseline is also the bar. Check how often manual keying gets a field wrong today. The system has to beat that, with a person on the exceptions.

“You don’t need it to be perfect. You just need it to be better than people.”

Have we done this before?

The closest public example is The Home Help Company, a care provider buried in manual admin. Its CEO was losing around 30 hours a month to payroll and invoicing.

We moved the team’s messages from WhatsApp to Slack, and automated scheduling, billing and payroll. Invoices and PAYE are now processed with a single click.

The CEO got back those 30 hours a month. The company went on to hire 20 more staff and take on 200 more clients.

That project wasn’t AI. That’s the point. The first win in finance is often joined-up systems, and AI sits on top of that foundation.

When isn’t this worth doing?

• You process a few dozen invoices a month. The capture in your accounting package will do.
• Most suppliers already send structured e-invoices. There’s little left to read.
• Purchase orders aren’t in a system, or are raised after the fact. Fix that before you automate matching.
• The bottleneck is slow approvals, not keying. That’s a process problem, and a model won’t fix it.

If you want an honest view on where AI fits in your finance operations, talk to us. The first chat is free, and so is an architectural review of your current setup. If you’d rather think it through first, try our AI readiness scorecard.