Trust Breakdown in AI-Driven Organizations: A Mechanism-Based Explanation
Keywords:
artificial intelligence, organizational trust, trust breakdown, human–AI interaction, digital transformationAbstract
Artificial intelligence (AI) is increasingly embedded in organizational decision-making, promising enhanced efficiency, objectivity, and performance. However, growing evidence indicates a paradox in which the advancement of AI systems is accompanied by declining trust among organizational members. Existing research on trust, artificial intelligence, and organizational behavior remains fragmented, limiting its ability to explain how AI-driven systems reshape trust dynamics in complex work environments. This study addresses this gap by developing a mechanism-based conceptual framework that explains how structural characteristics of AI systems, including opacity, autonomy, and unpredictability, trigger psychological processes that lead to trust breakdown. Drawing on interdisciplinary insights, the framework identifies key mediating mechanisms such as cognitive uncertainty, perceived loss of control, identity threat, and fairness ambiguity, which translate system features into behavioral outcomes including distrust, resistance, and disengagement. The study further highlights the moderating role of organizational conditions, including transparency, leadership support, and human–AI interaction quality. By integrating previously disconnected research streams, this article advances a process-oriented understanding of trust in AI-driven organizations and provides a foundation for future empirical research and practical interventions in managing trust under digital transformation.
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