Standard automation ROI analysis often misses four categories of systemic risk: tacit knowledge erosion, resilience reduction, regulatory exposure, and socio-institutional capital degradation, all of which impact long-term organizational performance. To address this, PHP-AIO (Protocol for Human Preservation in AI-Optimized Organizations) is introduced as a five-gate sequential decision protocol, culminating in a composite check that quantifies these unpriced systemic risks at the role level and generates auditable automation decisions.
In this protocol, a closed-form automation-debt measure ($\rho(P)$) formalizes how role-level decisions accumulate across multi-step processes; its warning is neutralized only by a regulator-mandated human-in-the-loop anchor. When applied to stylized profiles of representative internal roles, PHP-AIO yields distinct outcomes — automate, augment, hybrid, and preserve — for candidates that standard cost-benefit analysis would typically automate.
Threshold sensitivity analysis confirms that gate decisions are robust to upward perturbations of at least 14% in three out of four representative cases.
Blogger's Review: This protocol provides a comprehensive decision-making framework for AI automation, emphasizing the irreplaceability of human involvement, especially in complex decision-making and tacit knowledge contexts. By quantifying risks, PHP-AIO effectively enhances organizational resilience while ensuring that the pursuit of efficiency does not come at the cost of human insight.