人工审核分配应基于风险调整并优先排序
最大化自动化占比已不再是目标；取而代之的是，应将有限的人工审核产能优先配置于干预后预期价值最高的决策上，并采用基于风险调整的边界——即潜在错误影响越大，所要求的置信度越高。
支持这项说法
由此得出的边界是风险调整后的动态边界，而非固定阈值：错误潜在影响越大，自主决策所需的置信度也应越高。
原始摘录
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.
上下文
最大化自动化工作占比已不再是核心目标。关键问题转为：在一个不断扩大的待办任务池中，哪一部分最值得投入人工注意力？我们认为，在金融工作流中，该边界可通过量化方式确定——即以人工复核成本对标其提升决策预期价值所带来的收益。我们以应付账款信用催收工作流为驱动案例构建该框架，并展示如何据此将有限的人工复核资源分配至预期价值最高的干预环节。
原始上下文
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.