The workflow diagram says “human approval” before every AI-assisted output. In practice, one person faces hundreds of items by late afternoon. If reviewing each item properly is impossible, the label does not create meaningful oversight.
A review queue is an operational system. Its capacity, information and authority need to match the decisions people are expected to make.
Define the decision the reviewer owns
Specify what approval means: factual correctness, policy compliance, suitability for a particular audience or something else. Avoid asking one person to certify every aspect of an output without the relevant expertise.
NIST’s AI Risk Management Framework provides a broader basis for managing AI risks. For a specific workflow, translate governance intentions into a concrete decision, a responsible role and a route for unresolved cases.
Show the evidence beside the output
A reviewer should be able to inspect the input, relevant source material and important changes without opening a dozen unrelated systems. Make uncertainty visible. An unsupported claim hidden inside fluent text should not be easier to approve than to investigate.
For example, an internal document-summary tool might show the proposed summary beside the source passages. The reviewer can then check whether qualifications survived. This is a workflow example, not a guarantee that displaying citations makes the summary correct.
Measure the work before increasing volume
Observe how long a careful review takes across ordinary and difficult cases. Include escalation and correction time. Compare incoming volume with actual capacity instead of planning around the fastest straightforward item.
If the queue grows, reduce scope, improve the inputs or add suitable capacity. Do not solve the problem simply by shortening review time until approval becomes a reflex. High-consequence outputs may require a stricter process or exclusion from automation.
Give reviewers permission to stop
Provide clear reject and escalate options, along with a person who can resolve them. Record the reason for a correction in a way that supports later improvement. Reviewers need enough authority to challenge the system, including when an output looks plausible but cannot be verified.
Sample completed work and examine recurring failure patterns. A queue that is always empty may indicate excellent inputs—or hurried approval. The useful measure is whether the process catches consequential problems and leads to better decisions, not merely how quickly items acquire a green tick.