How to Design Human-AI Oversight That Avoids Approval Fatigue in Enterprise Systems

A new guide on enterprise agentic AI warns that routing every AI action to a human for approval creates 'rubber stamping' rather than genuine oversight, as reviewers quickly succumb to fatigue and approve requests without scrutiny. The recommended approach uses confidence and impact thresholds to triage outputs, so only genuinely uncertain or high-stakes actions reach human reviewers. Presenting reviewers with concise diffs instead of full documents is suggested to cut review time from minutes to seconds, making sustained attention more realistic. An LLM-based auditor can monitor random samples of automated outputs daily, automatically raising human-review rates if quality scores decline. Human corrections and approvals are also treated as labeled training data, feeding back into evaluation pipelines to continuously improve the system.