AI Document Processing: Getting Invoices Out of PDFs and Into Systems

Someone in your company retypes numbers from PDFs every week. Here is what it takes to stop, and when it is genuinely worth it.

Supplier invoices, delivery notes, timesheets, purchase orders: documents arrive as PDFs and scans, and someone retypes the important numbers into an accounting system. It is dull, error-prone and, at any real volume, expensive.

What changed

Traditional OCR read characters and required a template per supplier layout — brittle enough that most projects died maintaining templates. Modern extraction understands the document: it finds the total, the tax and the reference wherever they sit, on layouts it has never seen. That single change is what makes this worth revisiting if you looked at it three years ago.

Realistic accuracy

• Clean digital PDFs: extraction of standard fields is highly reliable, in the high nineties.
• Good scans: still strong, with occasional confusion on similar-looking values.
• Phone photos of crumpled paper: usable but needs review, and lighting matters more than resolution.
• Handwriting: unreliable for anything financial. Do not build a process that depends on it.

Where it pays

Do the arithmetic before building. Documents per month, multiplied by minutes of manual handling, gives you the labour cost. Compare against setup plus per-document processing cost. Below roughly a hundred documents a month, the honest answer is usually that a person does this faster than you can implement it — and we will say so.

• 100+ documents a month before it pays
• 2-5min manual handling per document
• 1 confidence threshold routing to human review

The part people forget

Extraction is the easy half. The value only appears when the extracted data lands in the accounting system, matched against a purchase order, with exceptions surfaced to the right person. A pipeline that produces a spreadsheet someone then imports by hand has automated the interesting part and left the tedious one.