The problem
A mid-sized company was processing approximately eight hundred invoices per month manually. Each invoice required data extraction, validation against purchase orders, coding to the correct accounts and routing for approval. The finance team of three spent an estimated sixty per cent of their time on invoice processing.
The volume was growing with the business, and the team was struggling to keep up. Errors were creeping in — miscoded invoices, missed discounts, delayed payments. The finance manager was spending more time on processing oversight and less on the analysis and business partnering the company needed.
What we built
We built an AI invoice processing assistant that automated the end-to-end invoice workflow:
- Data extraction. The assistant extracted invoice data — amounts, dates, line items, supplier details — regardless of format or layout.
- Validation. Extracted data was validated against purchase orders and business rules. Discrepancies were flagged for review.
- Coding. Invoices were coded to the correct accounts based on the extracted data and historical patterns.
- Routing. Invoices were routed for approval based on amount, department and approval rules.
- Payment preparation. Approved invoices were prepared for payment processing, with all necessary data structured for the payment system.
The results
Four months after deployment:
- Invoice processing time reduced by over eighty per cent — from manual processing of every invoice to exception-only review
- Data entry errors eliminated
- Early payment discounts captured more consistently because processing was faster
- The finance team redirected their time from processing to analysis, forecasting and business partnering
- Processing capacity increased — the same team could handle significantly more volume
How it worked
The assistant did not replace the finance team. It replaced the manual work of processing invoices. The team's role shifted from data entry and validation to exception handling and oversight.
The assistant processed the routine — the standard invoices that matched purchase orders, followed expected patterns and required no special handling. It escalated the exceptions — invoices with discrepancies, unusual amounts, new suppliers — for human review.
What we learned
The most important lesson was that finance automation works best when it handles the volume and flags the exceptions. The team's expertise was wasted on manual data entry. It was far more valuable when applied to the invoices that genuinely required financial judgement.
We also learned that the transition requires careful management of the team's concerns. The team initially worried that automation would make their roles redundant. In practice, their roles became more interesting — they moved from processing to analysis — and their value to the business increased.
For a broader look at how AI supports finance operations, see AI automation for finance. For the assistant category overview, see AI finance assistant.
This case study describes a composite of real implementations. Results vary based on the specific process, team and context.