The Challenge
A mid-sized Polish distribution company was spending over 40 hours per week manually processing supplier invoices — extracting data, matching purchase orders, and entering records into their ERP system. Errors were common, payment delays were costing supplier relationships, and the finance team was overwhelmed.
The Solution
We designed and implemented a three-stage AI automation pipeline:
Stage 1: Intelligent Document Capture
Using a fine-tuned document extraction model, we built a system that reads incoming PDF invoices (from email and a shared drive) and extracts key fields with 97% accuracy:
- Invoice number, date, and due date
- Supplier name and VAT number
- Line items with quantities and prices
- Total amounts
Stage 2: Automated PO Matching
The extracted data feeds into a matching engine that cross-references each invoice against open purchase orders in the ERP. Matched invoices are auto-approved. Exceptions (unmatched or out-of-tolerance) are flagged for human review.
Stage 3: ERP Integration
Approved invoices are automatically pushed into the ERP system via API, eliminating all manual data entry for standard cases.
The Results
After 8 weeks from kickoff to full production:
| Metric | Before | After |
|---|---|---|
| Weekly processing hours | 40h | 8h |
| Error rate | 4.2% | 0.3% |
| Average processing time per invoice | 12 min | 2.5 min |
| On-time payment rate | 78% | 97% |
The finance team now spends 80% less time on invoice processing and focuses on analysis and supplier relationship management.
Key Takeaways
- AI automation in finance is mature and reliable for document-heavy processes
- Human oversight is still essential for exception handling
- Integration with existing ERP systems is achievable without replacing them
- ROI timeline: under 6 months for a team of this size