The decision
Extracted text is a candidate record, not an approved business event. Decide which fields matter, how they can be checked and when a person must review them. A confidence score is useful evidence from an extraction system; it is not a substitute for business validation.
A worked example
For an illustrative invoice intake, compare line totals, currency, supplier identifier and invoice number. Show the reviewer the original page beside the extracted fields, highlighting the relevant region. A familiar supplier name with an unexpected bank detail should follow a separate verification process rather than being accepted because the text was read clearly.
Alternatives worth weighing
Template rules may suit consistent documents; learned extraction can cover varied layouts but needs representative evaluation. Human review can focus on exceptions after measured performance supports that decision. Begin with broader review when errors could trigger costly downstream actions.
Where the plan breaks
Do not evaluate only clean PDFs supplied by the implementation team. Include scans, rotated pages, missing fields, duplicates and unfamiliar layouts. Keep the source document, extraction version and corrections connected so a disputed record can be traced without searching unrelated folders.
Design the review screen around a disputed field
For the illustrative invoice flow, start with a small set of representative documents selected by the finance operations owner. Include an invoice with a decimal comma, a missing currency, a repeated invoice number and a total that does not match its lines. Record the expected field values and the action each discrepancy should trigger. This is a test collection, not a claim that the system has reached a particular accuracy level. Keep training examples separate from the cases used to evaluate a release.
The reviewer should see the source region, extracted value, validation issue and proposed correction together. They should be able to reject the document or leave a field unresolved instead of inventing a value merely to clear the queue. An override needs a reason and an accountable user. Preserve the original extraction as well as the accepted correction so that a later investigation can distinguish model output from human decisions.
Keep extraction, approval and posting separate
Assign each document a stable reference and track received, extracted, awaiting review, approved and posted states. Retrying extraction should not create a second payable record. Choose the duplicate rule with the business owner: an invoice number may need the supplier identity as part of its key, and a corrected invoice may require an explicit replacement relationship. File names alone are poor evidence of a new business event.
When the extraction service is unavailable, keep the intake record and show its pending state. Provide a controlled manual path for genuinely urgent work, with the same duplicate and approval checks. Releasing a model or rule update should rerun known difficult cases, including previously corrected fields. The operations owner decides which exception blocks posting; the development team implements that rule. Reading a bank detail correctly must never be treated as independent verification that the account is legitimate.
Before you commission the work
Who approves a record? Which mismatch blocks posting? How does a corrected field improve future evaluation? Agree those controls during AI integration and keep payment or other consequential actions outside unreviewed extraction.