Why Organizations Fail Before AI Fails: A Process Theory of Organizational Internalization, Enabling Governance, and AI Survivability
TL;DR
A bounded constructs, observable mechanisms, and longitudinal research designs for examining why organizations sometimes lose an AI-supported capability while the underlying technology continues to operate are offered.
Abstract
Technical operation is a weak test of whether artificial intelligence (AI) has become an organizational capability. An AI system can remain accurate, available, and institutionally visible while its outputs lose influence over routines, decision rights, and managerial judgment. Yet existing research on adoption, implementation, AI capability, and risk governance offers limited insight into why technically sound AI systems may gradually lose organizational consequence after deployment. This conceptual paper locates that problem in organizational internalization. It distinguishes technical implementation from operational, structural, and cognitive embedding and develops Organizational AI Survivability as the continuity of an AI-supported capability under disruption. Enabling governance links models to routines, accountability, interpretation, and learning; cumulative governance realignment explains how those links are repaired across successive episodes of leadership change, restructuring, regulatory change, and model drift. Five propositions organize the argument as a temporal sequence from embedding to continuity or fragility. The resulting process theory shifts explanation away from deployment as an endpoint and toward the governance work through which organizations repeatedly reproduce accountable alignment. It offers bounded constructs, observable mechanisms, and longitudinal research designs for examining why organizations sometimes lose an AI-supported capability while the underlying technology continues to operate.