When AI Delegation Becomes Dependence: Administrative Judgment at Risk
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Kookmin University
Abstract
Public organizations can delegate cognitive work to artificial intelligence while losing the capacity to scrutinize or resume that work. When does useful delegation become institutional dependence? This article develops a theory of institutional AI dependence as a task–role–workflow configuration combining substantial delegation of core cognition with insufficient task-required review or recovery capacity. It distinguishes two vulnerabilities: inadequate challenge during AI-supported work and inadequate capacity to continue judgment-intensive work when the system is unavailable. Extensive delegation with adequate distributed expertise and tested recovery constitutes managed delegation. Integrating cognitive offloading, automation, organizational learning, and public-administration research, the article extends Song et al.’s (2026) account of epistemic authority to organizational workflows and distinguishes perceived credibility from procedural authority embedded in defaults and override rules. Seven propositions connect practice displacement, challenge behavior, knowledge retention, subsequent delegation, and responsibility–control alignment. They specify how task-relevant practice, diagnostic feedback, and transferable knowledge shape retained capacity over time. The contribution is a task-relative diagnosis of threatened administrative judgment and a set of testable formation mechanisms. The framework guides public organizations in governing current agentic systems and preparing review and recovery arrangements for more capable AI.
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This theoretical study draws on published literature cited in the reference list. No new empirical datasets were generated or analysed.
This work is licensed under a Creative Commons Attribution 4.0 International License.