Human review belongs at decisions whose ambiguity or consequence exceeds the evidence available to the system, and the workflow should show the reviewer the source, proposed action, alternatives, and downstream effect.
Classify decisions by evidence and consequence
Teams often add a generic approval step near the end of an automation and discover that the reviewer cannot tell what changed. Review works when it sits beside a specific decision. Exact identifier matches, schema validation, format conversion, and arithmetic can usually proceed automatically. Conflicting sources, uncertain entity matches, unusual amounts, and irreversible external actions deserve an explicit hold.
The NIST AI Risk Management Framework playbook emphasizes defined roles, documented human oversight, and monitoring appropriate to risk. In a practical workflow, the buyer defines the decisions that require approval and the evidence a reviewer must see. The implementation then routes only those cases, which keeps the queue small enough for a person to use.
Build a review object that supports a decision
A review object should show the source excerpt, proposed normalized value, confidence or rule result, relevant alternatives, downstream destination, and effect of approval. The reviewer should be able to approve, correct, reject, or request more information. Each choice needs a reason field when the decision changes data or authorizes an external action.
The workflow must preserve the decision as an event rather than overwriting the proposed value. That distinction lets the team later ask whether failures came from extraction, normalization, review, or posting. It also prevents a person from appearing to have approved information that was changed after the review screen closed.
Measure the queue under real operating conditions
A supervised pilot should include ordinary cases and exceptions. Measure how many items stop, how long review takes, which fields cause repeated holds, and whether the displayed evidence is sufficient. A queue that sends half the batch to a person may still be useful, but its operating cost and staffing requirement must be visible before production.
Reality Contact, LLC implements the routing, review state, and evidence display from buyer-approved rules. The buyer remains responsible for policy, permissions, and each business decision. The service does not replace the people authorized to approve payments, customer records, legal filings, employment actions, or other consequential changes.
Where the service stops
Reality Contact, LLC implements and tests the technical workflow using buyer-authorized systems, but does not choose business policy, approve consequential decisions, supply production credentials, or authorize writes on the buyer's behalf. The buyer approves the mappings, review rules, access, retention terms, and test results, then processes a bounded live batch through the supervised workflow. This is technical implementation based on buyer-supplied rules and authorized access, and it does not replace legal, financial, security, privacy, or professional advice. The buyer controls credentials, source rights, business rules, retention decisions, reviewer authority, and every production release.
Sources: NIST AI RMF playbook.