{"id":4742,"date":"2026-05-01T09:46:33","date_gmt":"2026-05-01T07:46:33","guid":{"rendered":"https:\/\/agentaya.com\/?p=4742"},"modified":"2026-05-05T14:31:58","modified_gmt":"2026-05-05T12:31:58","slug":"how-to-use-ai-to-turn-a-pile-of-invoices-into-something-your-accountant-can-use","status":"publish","type":"post","link":"https:\/\/agentaya.com\/fr\/how-to-use-ai-to-turn-a-pile-of-invoices-into-something-your-accountant-can-use\/","title":{"rendered":"How to use AI to turn a pile of invoices into something your accountant can use"},"content":{"rendered":"\n

Most business owners or managers handle supplier invoices<\/a> in one of three ways, and none of them are great. They type the numbers into a spreadsheet themselves, which is not a fun task or the best use of their time and skills. They pay a bookkeeper to do it, which works but costs money and creates a delay. Or they don’t do it consistently, and find themselves several months behind at the end of the quarter, panic-processing a hundred invoices in one long sitting.<\/p>\n\n\n\n

AI doesn’t make the underlying task interesting. It does, however, take it from a job that needs your time, to a job that needs your attention, which is a meaningful difference if you’re trying to run a business.<\/p>\n\n\n\n

We share below a practical walkthrough of how to use the AI tools you probably already have access to (ChatGPT, Claude, or similar), to turn a pile of supplier invoices into a clean, structured set of data your accountant or accounting package can actually work with.<\/p>\n\n\n\n

One caveat before we start. If your accountant already has a preferred invoice-capture tool, then use that. This post is for businesses that aren’t there yet, or that want a simple, lightweight way to get from messy invoices to a usable spreadsheet.<\/p>\n\n\n\n

<\/span>Step 1: Get your invoices into one place<\/strong><\/span><\/h2>\n\n\n\n

Before AI can help you, you need to have something for it to work with. Many business owners don’t have this, because their invoices arrive in five or six different ways: some come as PDF attachments to emails, some as links to suppliers’ portals, some as photos taken of paper invoices, some as physical pieces of paper in the post.<\/p>\n\n\n\n

The simplest answer is to set up one place where they all end up. The two approaches that work are an email folder and a folder in your cloud drive (Google Drive, OneDrive, Dropbox, whichever your business uses). Most managers find email plus a labelling rule easier to maintain than a separate folder, because most invoices arrive in email anyway. Set up a label or folder called “Invoices to process”, and either drag every supplier invoice into it manually or, if you can, set up a forwarding rule so anything from a known supplier address gets routed there automatically.<\/p>\n\n\n\n

Photos of paper invoices are fine, though clean PDFs are more reliable. The AI tools handle clear photos well, as long as the photo is reasonably in focus, the entire invoice is in frame, and there’s no heavy glare or shadow. If you’re stuck with a paper-heavy supplier base, just take a photo with your phone the moment the paper invoice lands on your desk, and email it to yourself.<\/p>\n\n\n\n

The main goal is to make sure that when you come to do the next step, you’re not chasing invoices around four different inboxes.<\/p>\n\n\n\n

<\/span>Step 2: Decide what you actually need from each invoice<\/strong><\/span><\/h2>\n\n\n\n

This is the step many people skip, which is why the workflow then doesn’t quite work. Before you ask the AI to extract anything, you need to be clear about which fields you actually need.<\/p>\n\n\n\n

For most owner-managed businesses, the field list looks something like this: document type (invoice, credit note, receipt, statement, pro forma, or other), supplier name, invoice number, invoice date, due date, total amount, tax amount, net amount, currency, and a brief description of what was bought. That’s 10 fields, all of which apply to almost every supplier invoice in the world, even if the labelling and the layout vary.<\/p>\n\n\n\n

Document type matters more than it sounds. Real invoice piles contain things that aren’t invoices: credit notes, statements, receipts, pro formas, payment reminders, and the occasional duplicate. The AI will happily extract them all as if they were invoices unless you tell it to flag what each document actually is.<\/p>\n\n\n\n

Whether you need anything beyond those ten depends on what your accountant asks for or what your accounting software wants. Some businesses also need a payment reference, a project or department code, the supplier’s tax registration number, or the bank details for payment. The principle is the same. Write the list down once, and then use it consistently.<\/p>\n\n\n\n

If you do extract bank details, treat them as information to verify, not instructions to pay. Never change supplier payment details based only on a new invoice without checking through a trusted channel.<\/p>\n\n\n\n

This list is the spec you’ll give the AI. Vague instructions produce vague results. A clear list of fields produces a clean output.<\/p>\n\n\n\n

<\/span>Step 3: The extraction<\/strong><\/span><\/h2>\n\n\n\n

Now the AI does the boring bit. Open whichever AI tool you use, attach the invoices, and give it a clear instruction.<\/p>\n\n\n\n

A prompt that works reliably looks something like this:<\/p>\n\n\n\n

“Attached are several supplier invoices and possibly some related documents. For each one, first identify the document type (invoice, credit note, receipt, statement, pro forma, or other), then extract the following fields and return the results as a single table I can paste into a spreadsheet: document type, source file name, supplier name, invoice number, invoice date, due date, total amount, tax amount, net amount, currency, and a brief description of what was supplied. If any field is missing or unclear, write ‘not found’ rather than guessing. Use the same date format throughout: DD-MM-YYYY. If an invoice is in a foreign currency, give the amount in that currency without converting it. Add a final column called \u201cissues to check\u201d and flag anything unusual, including missing invoice number, unclear total, multiple tax rates, foreign currency, poor image quality, or possible duplicate.”<\/em><\/p>\n\n\n\n

Most paid plans let you upload several files at once, but reliability tends to drop as batches get larger. Start with five invoices, check the output carefully, and increase the batch size if it behaves consistently. If you have a lot to process, do it in waves rather than one giant batch. The output is a tidy table you can copy and paste straight into Google Sheets, Excel, or your accounting package, depending on what your downstream process needs.<\/p>\n\n\n\n

A note on file types. PDFs work best. Photos of paper invoices also work well, with occasional misreads on numbers. The one thing that’s slightly trickier is HTML email invoices, the kind you get from large retailers or rideshare apps. Saving them as PDFs first works better. So does taking a screenshot of the email and feeding the screenshot to the AI, which often handles the image more cleanly than raw HTML.<\/p>\n\n\n\n

<\/span>Step 4: Check the work<\/strong><\/span><\/h2>\n\n\n\n

The check is not the same as redoing the work. You’re not retyping the numbers. You’re spot-checking. Open the table the AI produced, then open three or four of the original invoices at random, and compare. Are the supplier names spelt correctly? Are the totals right to the penny? Are the tax amounts in the right column? Is the date the date of the invoice, not the date of the email it arrived in? Has the document type been identified correctly?<\/p>\n\n\n\n

If three or four random invoices come back clean, that’s a good sign, but it isn’t proof the whole batch is clean. The first few times you run this workflow, check more heavily. Once you know which suppliers and formats work reliably, you can dial back the checking. The AI tools are now consistent enough that most errors tend to come in patterns rather than at random, so if it’s getting one type of invoice wrong, it’s probably getting all of that type wrong, while others are fine.<\/p>\n\n\n\n

Two errors are particularly worth watching for. First, currency confusion. If you have invoices in more than one currency, check that the AI hasn’t quietly assumed they’re all in your home currency. Second, tax category errors. If your business has invoices that are taxed at different rates, or some that are zero-rated, the AI sometimes flattens these into a single column. Worth checking the first time you do this with a new supplier.<\/p>\n\n\n\n

One last thing worth doing before sending the output anywhere. Sort the table by supplier name and invoice number. Duplicates show up immediately when invoices arrive through more than one channel, the email and the supplier portal for example, and a 30-second sort catches them before they become a problem.<\/p>\n\n\n\n

<\/span>Step 5: Make it a routine, not a one-off<\/strong><\/span><\/h2>\n\n\n\n

The thing that turns this from “an interesting experiment” into “a real time-saver” is doing it on a regular cadence, not letting invoices pile up.<\/p>\n\n\n\n

For most owner-managed businesses, once a week is the right rhythm. Twenty minutes, batch-process whatever’s accumulated in the inbox, done. It’s the same time you’d have spent dreading the job, except now most of it is the AI’s problem.<\/p>\n\n\n\n

If you’re paying a bookkeeper to do this work, the right conversation is not “I’m going to replace you with AI”. It’s “what if you spent the time you currently spend on data entry, on the things only a person can do, like querying odd entries, and getting the books to a clean state every month?”. For most bookkeepers, this is a welcome shift. The data entry is the lowest-paid and most boring part of the job. They’d much rather do the thinking work.<\/p>\n\n\n\n

If your accountant currently receives a sea of unstructured PDFs, photos and Word documents at the end of each quarter, sending them a clean spreadsheet, plus the originals, will save them significant time, which may translate into either a lower bill or more attention on the parts of your finances that actually need it.<\/p>\n\n\n\n

For business owners or managers comfortable with more agentic tools, this workflow can be pushed further. The same extraction can run more or less unattended on a schedule, with you receiving the finished spreadsheet rather than running it yourself. That’s a useful next step once the basic workflow is bedded in. But don’t start there. Get the manual version right first, so you know what good output looks like, and you can spot when the automated version drifts.<\/p>\n\n\n\n

<\/span>Some limitations<\/strong><\/span><\/h2>\n\n\n\n

A few things worth flagging before you take this and run with it.<\/p>\n\n\n\n

First, what this workflow gives you is structured invoice data, not booked transactions. Coding each line to the correct account, the right tax treatment, the right project or department, and the right payment or approval status, all of that still needs a person, your accountant, or proper accounting software. The spreadsheet is a starting point, not a finished set of accounts.<\/p>\n\n\n\n

AI tools are now very good at the extraction part, but they aren’t perfect. Edge cases stay edge cases. A handwritten invoice, a faded or crinkled receipt, or an invoice with an unusual tax structure. All of these can produce errors that a person perhaps wouldn’t make. The check step exists for exactly these cases.<\/p>\n\n\n\n

The legal status of the original invoice still matters in most countries. The extracted spreadsheet is not the invoice. You still need to keep the originals, for the period your local tax authority requires, and your accounting process needs to be able to point at the original PDF if questioned. Most accounting packages handle this automatically by attaching the source file to the entry, but it’s worth checking that yours does.<\/p>\n\n\n\n

There are also specialist tools that do this kind of extraction with more polish than a general AI chat. Tools like Dext, Hubdoc and AutoEntry are widely used in accounting circles, and your accountant may already have a preferred one. They typically integrate more directly with major accounting packages, handle higher volumes, and have better error-handling for awkward documents. If your business processes more than a few hundred invoices a month, they’re probably worth the cost. If you’re processing twenty or fifty, the lightweight AI-chat approach in this post is plenty.<\/p>\n\n\n\n

And finally, a word on regulated sectors and audit-sensitive contexts. If your business operates in finance, healthcare, or any sector where invoice processing is part of a formal audit trail, please don’t take this post as professional advice. The same goes if your country has rules requiring automated data flows between your accounting records and your tax submissions. Some jurisdictions do. Your accountant will know whether copy-pasting from a chatbot fits within those rules where you operate.<\/p>\n","protected":false},"excerpt":{"rendered":"

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