Finance and admin
AI for invoice and document data extraction
List the fields you retype today, such as supplier, VAT number, invoice number, date, amounts and order reference. Test an AI document tool on 50 real invoices or delivery notes and measure accuracy per field. Send the extracted data to a review screen or spreadsheet, not straight into accounting. First check whether the data already reaches you in structured form through myDATA or e-invoicing.
Effort:
Medium
Time saved:
Roughly 40–70% less retyping on tested documents; measure it
The problem today
Staff retype supplier invoices, delivery notes and CMRs into spreadsheets or the ERP. It is slow, errors creep into amounts and VAT numbers, and month-end turns into a scramble.
How it works, in plain words
It works like a careful clerk who reads each invoice once, copies the key fields into a form, and flags anything that does not add up.
What goes in: supplier invoices, delivery notes or CMRs that still arrive as PDFs, scans or photos, plus your list of the exact fields you need.
What the AI does: it reads the document, finds fields such as supplier, VAT number, invoice number, date and amounts, and checks them against simple rules like totals adding up.
What a person checks: a person reviews every flagged document and spot-checks the rest before anything moves into accounting or the ERP.
What comes out: a clean table or review queue, ready to import, linked to the original document.
It can misread numbers on poor scans or unusual layouts without warning. It also does not replace data that already reaches you through myDATA or e-invoicing.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A family mini-market chain with 3 stores in Corfu
Delivery notes from dozens of local suppliers arrive on paper and are typed into a spreadsheet at night. The owner would first check which suppliers already send structured data, then test a document tool on 50 real delivery notes from the rest. They would measure accuracy per field, especially quantities and amounts, before trusting it.
A 10-person creative agency in Nea Smyrni
Freelancer and media invoices arrive by email as PDFs in many layouts, and month-end is spent retyping them. The agency would route attachments to an extraction step that fills a review sheet with supplier, VAT number, amount and project code. A realistic first target could be cutting retyping time by roughly a third, measured in a two-month pilot.
A 50-person fruit processing plant in Veria
In harvest season, growers' delivery notes and transport documents pile up faster than the office can enter them. The plant would extract grower, date, weight and lot number into a review queue, with any total that does not match sent to a person. They would track entry backlog at the end of each week.
How to do it, step by step
Check what already arrives as structured data. Invoices issued through myDATA or an e-invoicing provider may already give you the fields without any AI. Focus AI on documents that still arrive as PDFs, scans or photos.
Choose 3–5 document types and list the exact fields you need from each. Build a test sheet with the correct values for 50 real documents.
Test one or two tools on this set. Measure accuracy per field, not overall, because a wrong amount matters far more than a wrong address line.
Add validation rules: totals must add up, VAT numbers must have the right format, dates must fall in a sensible range. Anything that fails goes to a person.
Send extracted data to a review queue or staging sheet. A person approves it before it reaches your accounting software or ERP.
Track exceptions by supplier. Ask the ones that cause the most problems for cleaner documents or structured data.
Tools to consider
Microsoft AI Builder in Power Automate: ready-made invoice and document processing inside Microsoft 365. Caveat: Greek documents and unusual layouts need testing and may need a custom-trained model.
Google Document AI: invoice and form parsers on Google Cloud. Caveat: the setup is technical and usually needs a developer or partner.
ChatGPT or Claude: good for one-off extraction from a handful of PDFs into a table. Caveat: not built for volume or audit trails, and it can misread numbers without warning.
Zapier or Make: moves extracted data from email attachments into sheets or accounting tools. Caveat: it adds another service and another point of failure to monitor.
Data protection and GDPR
Supplier invoices are mostly business data, but they can include names of sole traders, employees and private addresses. Delivery notes may hold recipients' names, phone numbers and signatures. Use extraction tools under a data processing agreement and check where documents are stored and for how long. Do not upload documents to free consumer chatbots. Keep the original documents and an audit trail, because tax and accounting retention rules still apply. Limit access to finance staff. If you process large volumes of documents with consumer data, ask your DPO or legal advisor whether a DPIA is needed.
Mistakes to avoid
Sending extracted data straight into accounting without a human check.
Measuring overall accuracy instead of accuracy on amounts and VAT numbers.
Building AI extraction for invoices that already arrive as structured e-invoicing data.