Why the problem persists
Purchase orders, delivery records and invoices often live in different systems. When product codes, units, currency, payment terms or quantities differ, manual review becomes slow and small discrepancies disappear into routine work.
AI is useful here as a document interpreter and exception preparer—not as the final decision-maker. It extracts fields from variable files, checks them against rules, and opens a task instead of paying when information is missing.
A controlled flow
Start with read-only sources. Present every discrepancy with links to evidence, the rule applied and a confidence score. Bank-detail changes, new suppliers, high-value items and low-confidence matches always go to an authorised person.
- Classify the document and link it to a transaction
- Normalise product, quantity, price, tax, currency and terms
- Apply tolerance rules and explain the exception
- Record the reviewer’s accept, reject or correction decision
How success is counted
Not every flagged difference is economic value. Count only an accepted correction, refund, offset or avoided payment confirmed by finance, and never count the same amount twice.
Report time reduction separately. This distinction reduces disputes and prevents inflated ROI claims.