The system reads a document and fills the customer record. A date looks plausible, so nobody notices it came from the signature page rather than the service-start section.
Before AI-extracted fields update records, validate the meaning, source and destination of important values. Keep extraction, validation and updates distinct so an uncertain reading does not silently become business data.
Define fields precisely
A field called date is insufficient. Specify which date, expected format and what happens if it is absent or contradictory.
For an illustrative service-order workflow, distinguish issue date, signature date and proposed start date. Add source-location requirements where supported so reviewers can find the evidence.
Build field-level checks
Use a worksheet containing:
Destination field and business definition.
Accepted format and permitted values.
Source section or evidence expected.
Missing-value behaviour.
Conflict or ambiguity route.
Required review before update.
A date-format check cannot establish that the date has the right business meaning. Keep those judgements separate. Likewise, a valid-looking customer reference is not proof that the correct customer was identified.
Test difficult documents
Include crossed-out information, multiple addresses, similar reference numbers and revised pages if they occur in your work. Use authorised material and label synthetic cases.
Assess which field failed. A document-level pass can conceal one important error among many correct values. Avoid using an untested confidence score as the sole reason to skip review.
Confirm the target record
The correct value can still go into the wrong record. Define a dependable matching method and an exception route for ambiguous identities. Show reviewers the current value and proposed replacement.
Before retrying a failed update, check whether it completed. Record outcomes so connection problems do not create repeated or conflicting changes.
Keep evidence appropriately
Agree who can inspect sources, how corrections are recorded and how long evidence remains available. These decisions depend on your information-management requirements and the systems involved.
Retain failed examples as controlled checks when the extraction method or document format changes.
Related guidance: guide to mapping the work before changing it.
