When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations
Multiagent SystemsArtificial IntelligenceComputers and SocietyHuman-Computer Interaction
Summary
The authors explain that traditional ways of measuring the benefits of automation miss important long-term risks like losing hidden knowledge, hurting how well an organization can bounce back, facing more regulation, and weakening social connections inside the company. They introduce PHP-AIO, a step-by-step method that checks and measures these hidden risks when deciding whether to automate a job. Their approach gives different recommendations—automate fully, add human help, mix both, or keep human-only—while showing these decisions can stay reliable even if some assumptions change. This method helps organizations make better, safer automation choices beyond just immediate cost savings.
Authors
Jose Manuel de la Chica Rodriguez, Jairo Rodriguez Arias, Spyridon Chouliaras
Abstract
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