Why human–AI collaboration fails: An operational contestability framework

Abstract

Human–AI collaboration is becoming central to operational prediction, decision-making, and control. As AI systems become embedded in operational workflows, organizations face a recurring risk: decision support may improve while the practical space for human intervention, accountability, and learning becomes too constrained for meaningful contestation. This article develops an operational contestability framework to explain when and why this risk becomes collaboration failure. The key question is not simply whether humans remain formally “in the loop,” but whether AI-enabled operational decision systems preserve practical room for questioning algorithmic representations, reallocating decision authority, and revising collaboration routines before execution makes intervention costly or infeasible. The framework identifies three driving mechanisms: cognitive asymmetry, dynamic delegation, and meta-learning. These mechanisms progressively constrain what can be represented, who can intervene, and how collaboration routines can be revised. It also specifies two amplifying conditions: cognitive coupling and accountability-defensibility. These conditions raise the operational and justificatory costs of deviating from algorithmic baselines. The erosion of operational contestability unfolds through a recurrent process of Encoding, Habituation, and Closure (EHC): representations and authority rules are built into system design, algorithmic outputs become planning baselines, and those baselines become operational commitments under time pressure, interdependence, and accountability exposure. Generative AI intensifies this process by turning recommendations, explanations, and justifications into portable decision artifacts, thereby accelerating habituation and making closure less visible. The framework reframes human–AI collaboration as an operations management problem: governing decision systems so that representation, authority, and learning remain contestable across repeated decision cycles.

Graphical abstract

Keywords

human–AI collaboration / operational contestability / AI-enabled operations / decision-system governance / generative AI

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