An extraction confidence score is not a business validation result. A document can contain a confidently recognized value that is unsuitable for the process, or omit a required field entirely. Check presence, format and business rules alongside the scores returned by the model.
Use approved, nonconfidential samples in a test environment. This guide describes a review design for AI Builder document processing, not a claim that the model has been tested on your documents or can authorize payment without oversight.
Define acceptance before choosing a threshold
List the required fields and the consequences of a wrong value. An internal reference label and a payment destination do not carry the same operational risk. The process owner should define which failures require human review, regardless of model confidence.
Microsoft's document processing flow reference distinguishes extracted values from their confidence outputs. Inspect the fields actually returned by the published model and action version. Do not assume that a single document-wide score summarizes every required value.
Check missing values explicitly
Decide how the flow recognizes an absent or empty field. A condition that evaluates only scores for fields that happen to exist can overlook missing required information. Include that case in the test set before enabling any downstream business action.
Separate recognition from validity
A correctly read account reference can still belong to the wrong supplier. A recognized date can still fall outside the allowed period. These checks require approved business data or review rules; a higher extraction score cannot supply them.
Inspect the actual confidence outputs
Use a controlled run to inspect field and cell scores where available. Microsoft's confidence guidance explains their role in reviewing extraction. Availability and presentation can differ by document model and version, so use the actual output contract rather than assuming every table has a universal aggregate score.
Choose thresholds from representative evidence
Do not adopt an arbitrary number from another organization's example. Compare approved samples with known correct answers, including difficult scans and unfamiliar layouts. Record which mistakes would pass the proposed threshold and which correct results would unnecessarily require review.
Retain the original for review
A reviewer needs the relevant source and extracted value together. Store or reference them through approved access controls. Avoid copying private document content into ordinary email messages or broad technical logs just to explain a low score.
| Validation layer | Question |
|---|---|
| Presence | Is every required value available? |
| Recognition confidence | How uncertain is this extracted item? |
| Format | Does the value match the expected data shape? |
| Business rule | Is the value appropriate for this transaction? |
Route exceptions before creating irreversible effects
Keep the initial output as a proposed record rather than an automatically approved transaction. If a required field is missing or a business check fails, send the item to the authorized review queue. Do not insert a guessed value merely to satisfy the next action's schema.
Test a confidently wrong value
Include a sample where the model reads the visible text correctly but the business data is intentionally unsuitable. The flow should still reject or flag it through the relevant validation. This demonstrates why confidence cannot replace the process's substantive rules.
Test a weak but correct extraction
A low score can still accompany a correct value. The appropriate response may be review rather than deletion. Preserve the proposal and source so a reviewer can accept or correct it without reprocessing unnecessarily.
Measure the review process honestly
Record sample counts, error categories and the acceptance rule used. Do not turn a confidence value into an advertised accuracy percentage or guarantee. A small pilot is evidence about those samples, not a universal quality certification.
Our SharePoint metadata guide can help define the destination fields after validation. Keep extracted proposals distinguishable from reviewed values so staff know which information has been accepted.
When the model changes, repeat the representative acceptance set and inspect its actual outputs. A score threshold that was workable for one version should not silently govern another without review.
Keep the final record compact: required fields, validation rules, review conditions and the sanitized test outcome. The aim is a traceable decision about the document, with confidence used as one signal rather than a substitute for completeness and business judgment.
