A confidence score is not a release decision

A confidence score can help organize review, but it does not answer whether an output is appropriate to release. Before using one, determine what the score measures, which fields have scores, and how it behaves on your documents.

Microsoft's Document Intelligence guidance distinguishes estimated accuracy from analysis confidence and separates document, field, word, and other confidence measures; it also notes that not all fields return confidence scores (Microsoft documentation). These definitions describe that product, not every application that displays a percentage.

A score might relate to transcription, document similarity, field placement, or another prediction. Ask the provider to define it and explain whether it is calibrated against actual correctness for the proposed use.

For a multi-stage workflow, inspect the stages separately. A correctly transcribed number can be assigned to the wrong field. A correctly extracted field can later be described incorrectly in generated prose. Do not assume a score travels unchanged through the whole process.

As an editorial pilot method, compare scored outputs with a pre-established answer key. Group results by relevant document type and quality, and inspect incorrect outputs even when they received high confidence.

Record fields with no scores. Do not treat “not scored” as low confidence, high confidence, or an automatic pass. Give it an explicit status and a review rule.

Use the importance of the output as well as the model's estimate when designing a review process. A material limit or policy-period error should not be hidden inside a favorable average.

Microsoft recommends considering human review for critical workflows (Microsoft documentation). A universal cutoff for automatically releasing insurance information is not established by that recommendation.

If the organization uses thresholds to route work, document who approved them, what observations support them, and which changes require reevaluation. NIST recommends post-deployment monitoring and change-management processes across the AI lifecycle (NIST AI RMF Core).

Do not borrow a percentage from another product, document set, or vendor demonstration and call it a safety standard. The defensible decision is tied to a defined task, observed performance, and a specified review process.

Next: Read how to count extraction accuracy and use the source review log to record actual review outcomes.