When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations
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 () 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
Keywords
Cite
@article{arxiv.2607.15944,
title = {When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations},
author = {Jose Manuel de la Chica Rodriguez and Jairo Rodriguez Arias and Spyridon Chouliaras},
journal= {arXiv preprint arXiv:2607.15944},
year = {2026}
}