Document AI can convert pages into structured fields, but extraction is only one step in a business process. A reviewer still needs to understand where a value came from, what uncertainty remains and whether the result is appropriate for the next action. A review screen that displays only clean-looking fields can hide the mistakes it was meant to catch.
Start with the decisions the extracted data supports. Filing a document for search, matching an invoice to an order and authorizing payment carry different consequences. The workflow should route uncertainty according to those consequences, rather than apply one confidence threshold to every field.
Define fields and review rules together
For each field, record its meaning, expected format, source location and downstream use. Distinguish a missing value from a value the model could not confidently read. Do not convert an empty total to zero simply to satisfy a required database column.
Amazon Textract's guidance discusses confidence thresholds in relation to the sensitivity of a use case. Microsoft's Document Intelligence transparency note discusses the system's capabilities and limitations. Use these as inputs to testing, not as proof that a score has the same meaning across providers or document types.
Worked example: processing supplier invoices
Imagine a hypothetical distributor extracting supplier name, invoice number, date and total. The invoice number is clear, but the total appears next to a handwritten correction. The review screen highlights the relevant page region and shows both the extracted value and its uncertainty.
The reviewer confirms the number from the original, records a correction reason and submits the updated fields. The system retains the original extraction alongside the approved version. It also checks whether the supplier and invoice number already exist, because an accurate extraction can still describe a duplicate submission.
An arithmetic check compares line totals, tax and the final amount under the business's approved rules. A mismatch creates an exception, rather than encouraging the reviewer to choose whichever number makes the form pass. Payment authorization remains a separate controlled step.
Make review efficient without hiding uncertainty
Place the document and fields side by side, with keyboard navigation for repeated work. Prioritize fields that failed a validation or require explicit confirmation. Let reviewers indicate that a document is unreadable or of the wrong type; forcing a guessed value creates misleading training and audit data.
Avoid displaying a single overall score as though it certifies the complete record. A high-confidence supplier name does not compensate for an uncertain account number. Show the reason a case entered review and the actions needed to release it.
Preserve corrections as useful evidence
Record the model or extraction configuration version, source document identifier, changed fields and reviewer identity. Protect this history according to the document's sensitivity. Routine logs should not contain complete documents or private field values.
Corrections can identify recurring failures, such as one supplier's layout or a particular scan condition. They are not automatically approved training data. Decide who may use them for model improvement, and review whether the correction itself is accurate before adding it to an evaluation set.
Measure quality and operational effort
Track field-level errors on representative samples, review rate, review duration and unresolved exceptions. Break results down by document type, supplier layout and image quality. An average accuracy figure can conceal a category that requires constant manual repair.
Pilot with a clear fallback to the existing process. Expand only when the reviewed output is reliable and the total handling effort is acceptable. The useful outcome is an evidence-backed record that a person can inspect and trust for its intended purpose, rather than a faster path to an unexplained value.
Primary references
Related reading
Evaluate extraction and review workflows with our AI solutions team.