Human review allocation should be risk-adjusted and prioritized
Maximizing automation share is no longer the objective; instead, limited human-review capacity should be allocated to decisions where intervention yields the greatest expected value, using a risk-adjusted boundary that increases required confidence as potential error impact rises.
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Extrait original
The resulting boundary is risk-adjusted rather than fixed: as the potential impact of an error increases, the confidence required for autonomous action should increase with it.
Contexte
Maximizing the share of work that is automated is no longer the binding objective. The question is which slice of an expanded universe of work deserves human attention. We argue that in finance workflows this boundary can be located quantitatively, by pricing human review against the expected value of the decisions that review improves. We develop this framework using an accounts-payable credit-recovery workflow as the motivating case and show how it can be used to allocate limited human-review capacity toward the decisions where intervention has the greatest expected value.