From Retained Evidence to Organizational Action: The Attention-Conversion Bottleneck in AI-Mediated Customer Feedback

This paper develops a conceptual framework for understanding the gap between the retention of customer feedback evidence and its effective conversion into organizational action in AI-mediated environments. It examines the attention-conversion bottleneck: the organizational and computational mechanisms through which customer feedback is collected, retained, interpreted, prioritized, and translated into decisions and operational responses. The paper conceptualizes how evidence may remain available within organizational information systems without receiving sufficient attention or producing meaningful action. It explores the roles of AI-mediated processing, evidence accessibility, attention allocation, organizational decision-making, and accountability in shaping this conversion process. The proposed framework aims to contribute to research at the intersection of artificial intelligence, customer feedback management, organizational learning, and strategic management. It is intended to support further theoretical development and future empirical investigation. Publication status: Preprint. This manuscript has not been accepted or published by a journal or conference.