A reviewer needs enough information, authority, and technical competence to reach a different conclusion from the AI system. If personnel routinely approve recommendations without examining the underlying evidence, human review can become little more than an electronic signature.

This weakness often appears gradually. As users gain confidence in a system, review times shorten, acceptance rates increase, and explanations become standardized. Those patterns do not prove inadequate oversight, but they deserve attention when the human decision is intended to function as a meaningful control.

Evidence of effective review can include instances where personnel requested additional information, identified questionable inputs, corrected outputs, escalated uncertainty, or rejected recommendations. The organization should also be able to explain what reviewers are expected to examine before accepting an output.

During inspection, records showing thoughtful intervention can carry considerably more weight than thousands of approvals that provide no evidence of what the reviewer actually evaluated.

A very low override rate deserves evaluation rather than automatic interpretation as evidence that the AI system is performing exceptionally well. The organization should determine whether the system is genuinely producing reliable recommendations or whether users have gradually become reluctant to challenge it.

The distinction can be tested. Review selected accepted decisions retrospectively and ask qualified personnel to assess the underlying evidence without relying on the original AI recommendation. Examine whether unusual outputs received deeper review, whether users understand known system limitations, and whether disagreement rates differ significantly between departments, sites, or experience levels.

Inspection friction develops when personnel are described as an important safeguard but records show near universal acceptance with little evidence of independent assessment.

Low override rates can be entirely appropriate. The defensible position comes from demonstrating why they are low and showing that personnel remain capable and willing to intervene when the recommendation does not withstand technical or quality scrutiny.

The decision should be stopped or qualified when the input data cannot support the intended GMP conclusion. A technically functioning model cannot compensate for missing, outdated, incorrectly mapped, or contextually misleading information.

This becomes particularly important with systems drawing information from multiple sources. An AI recommendation may appear coherent while relying on an incomplete deviation history, an incorrect equipment identifier, missing laboratory results, or data captured before a relevant process change. The output itself may provide little indication that important context is absent.

Human oversight therefore needs to include attention to input sufficiency where the risk warrants it. Reviewers should understand which source information materially affects the decision and recognize circumstances where additional verification is necessary.

If a significant decision is later questioned, "the AI processed the available data correctly" provides a weak defense when the organization had no effective control for determining whether the available data were adequate in the first place.

Oversight can deteriorate through familiarity, workload pressure, increasing confidence in the system, staff turnover, or gradual expansion of how the AI is used. None of those changes necessarily alters the formal approval workflow, which is why declining effectiveness can remain unnoticed.

An operational warning sign is when the original review expectations no longer match actual practice. Users may begin relying on summarized outputs instead of source information, reviews may become faster, exceptions may receive less scrutiny, or the system may start supporting decisions beyond the conditions originally evaluated.

Periodic assessment should therefore examine actual reviewer behavior and decision quality, not simply confirm that required approvals occurred. Useful evidence includes review depth, intervention patterns, recurring errors, near misses, user feedback, and cases where additional investigation changed the initial recommendation.

A stable procedure does not demonstrate stable oversight. Effective HITL controls need evidence that human involvement continues to provide the level of scrutiny the control was designed to achieve.