提出一套防止过度自动化的人类保留协议，保护组织长期竞争力。
When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations
- 设计五阶段决策流程，量化角色级系统性风险
- 发现传统方法会误自动化的四类关键角色
- 适合金融等高风险行业的自动化治理决策
标准自动化投资回报率忽略了四类系统性风险——隐性知识流失、韧性下降、监管暴露和社企资本损耗，这些都会影响组织长期表现。PHP-AIO（AI优化组织中人类保留协议）是一种五门限递进式决策协议，通过角色层面量化这些未定价的系统性风险，并生成可审计的自动化决策。一个闭式自动化债务度量 $ρ(P)$ 形式化了多步骤流程中角色决策的累积效应；其预警仅在监管要求的人类在环锚点介入后才被解除。在典型内部岗位的模拟分析中，PHP-AIO 产生了与传统成本效益分析不同的结果：自动化、增强、混合与保留。阈值敏感性分析表明，在三类代表性案例中，门限决策对至少14%的上偏扰动具有鲁棒性。
原文摘要 · Abstract (English)
Standard automation ROI misses four categories of systemic risk -- tacit knowledge erosion, resilience reduction, regulatory exposure, and socio-institutional capital degradation -- that affect long-term organizational performance. PHP-AIO (Protocol for Human Preservation in AI-Optimized Organizations) is a five-gate sequential decision protocol with a final composite check that quantifies these unpriced systemic risks at the role level and produces auditable automation decisions. A closed-form automation-debt measure ($ρ(P)$) formalises how role-level decisions accumulate across multi-step processes; its warning is neutralised only by a regulator-mandated human-in-the-loop anchor. Applied to stylised profiles of representative internal roles, PHP-AIO produces distinct outcomes -- automate, augment, hybrid, and preserve -- for candidates that standard cost-benefit analysis would uniformly automate. Threshold sensitivity analysis confirms the gate decisions are robust to upward perturbations of at least 14% in three of four representative cases. Keywords: AI governance, automation decision, human oversight, tacit knowledge, organizational resilience, financial services
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