Human review is a feature, not a failure of automation
Ansh PuniaIndependent AI & systems builder
How exception queues and explicit approvals make AI systems safer and more useful.
01Not every item deserves the same confidence
Automation can complete routine, well-supported cases while routing ambiguous or high-impact items for review. Confidence thresholds should reflect business risk rather than a desire to maximise the percentage processed automatically.
02A review queue needs context, not just an alert
The reviewer should see the original input, the proposed action, relevant evidence and the reason the system paused. That allows a quick decision and creates useful feedback for improving the workflow.
03Approvals create operational memory
Recording who approved an action, what changed and when it happened supports accountability and makes recurring exceptions visible. Over time, those records show where a rule should be improved or a manual decision should remain.
Before moving forward, make these visible.
- Risk-based confidence threshold
- Evidence beside every review
- Clear approve, edit and reject actions
- Decision history and feedback loop
AI automation
This thinking is part of how I approach AI automation work—scoped around the business outcome, built in stages you can review, and handed over with the accounts and documentation in your name.
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