Keywords
Dynamic Capabilities
Abstract
The integration of artificial intelligence into organizational decision-making has been widely theorized as a mechanism for enhancing organizational learning—the processes through which organizations acquire, interpret, and act on information to improve performance. Yet a growing body of evidence raises a disturbing counter-narrative: rather than building dynamic organizational capabilities, the algorithmic delegation of judgment may be accelerating a form of procedural senescence—the systematic erosion of organizational knowledge, interpretive capacity, and adaptive expertise that leaves organizations more brittle and less capable of responding to novel challenges. This paper develops a comprehensive analysis of the dual potential of AI in organizational learning, arguing that the outcome—whether AI enables organizational capability building or hastens organizational forgetting—depends critically on how AI systems are designed, deployed, and governed. Drawing on three foundational references and twelve supplementary citations spanning organizational learning theory, dynamic capabilities, algorithmic management, and skill erosion research, this study examines the mechanisms through which AI systems can either enhance or undermine the three sub-processes of organizational learning: knowledge acquisition, knowledge interpretation, and behavioral adaptation. The analysis reveals that the metacognitive calibration failures documented by the MIRROR benchmark, the structural constraints on AI auditing identified by the Verification Tax, and the practical demands for explainability in human resource analytics illuminate the conditions under which AI deployment accelerates rather than arrests organizational learning decline. The paper concludes by proposing a Learning-Preserving AI Deployment framework that organizations can use to ensure that AI investment generates genuine organizational capability rather than its erosion.
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