Accounts payable teams spend an average of 15 minutes per invoice on manual data entry — reading vendor names, matching PO numbers, keying in line items, and verifying totals. For a mid-size company processing 500 invoices per month, that's over 125 hours of repetitive work.
The Manual Invoice Problem
Traditional invoice processing involves receiving a PDF or paper invoice, manually identifying the vendor, typing in each line item, cross-referencing against purchase orders, and entering everything into the ERP. Error rates for manual entry hover around 3-5%, leading to duplicate payments, missed discounts, and audit findings.
How AI Extraction Works
Modern AI document intelligence combines OCR (Optical Character Recognition) with large language models to understand invoice layouts. Unlike template-based systems that break when a new vendor sends a different format, AI extraction adapts to any layout — reading vendor name, invoice number, date, line items with quantities and prices, tax amounts, and totals.
What Gets Extracted Automatically
- Header fields: Vendor name, invoice number, date, due date, PO number
- Line items: Description, quantity, unit price, amount, item codes
- Totals: Subtotal, tax, freight, total amount
- Payment info: Payment terms, remit-to address, bank details
- GL suggestions: AI recommends GL codes based on line item descriptions
Real-World Results
Teams using AI invoice processing report 90%+ straight-through processing rates — meaning invoices are extracted, validated, and ready for approval without human intervention. The remaining 10% are flagged for review due to low confidence scores or math validation failures.
Getting Started
ScanThisText's AP Invoice module extracts data from any invoice format using Azure Document Intelligence + AI enrichment. Upload a single invoice or batch-process up to 20 at once. Results include confidence scores, math validation, and GL code suggestions.