AI washing as a knowledge governance failure in organizations

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Purpose Growing evidence suggests that firms increasingly overstate their artificial intelligence (AI) adoption in corporate communications, yet the organizational consequences of AI washing remain underexplored. This study aims to conceptualize AI washing as a knowledge governance failure (KGF) and develops a framework explaining how persistent symbolic AI adoption progressively undermines organizational learning, knowledge validation and capability development. Design/methodology/approach Drawing on the knowledge-based view (KBV) and knowledge governance theory (KGT), this conceptual study develops an integrative framework. An illustrative multiple-case analysis of manufacturing settings is used to demonstrate the proposed governance mechanisms and the evolutionary trajectory of AI washing. Findings AI washing is conceptualized as a sequential KGF process driven by legitimacy pressures. Persistent symbolic AI adoption redirects managerial attention away from substantive capability development, leading to resource misallocation, cognitive decoupling, cognitive debt accumulation, evaluation failure, knowledge validation erosion, and ultimately organizational brain rot. These mechanisms reinforce one another through a self-reinforcing governance deterioration cycle. Practical implications Organizations should address AI washing as a knowledge governance challenge rather than merely a disclosure issue. Aligning AI adoption with organizational learning, knowledge validation and governance mechanisms is essential for sustaining AI-enabled capability development. Originality/value This study extends AI washing research beyond disclosure and legitimacy perspectives by conceptualizing it as a dynamic knowledge governance process. By integrating KBV and KGT, it explains how symbolic AI adoption progressively erodes organizational knowledge systems and provides a theoretical foundation for future empirical research on AI governance.