Trust Configurations in AI Adoption: Analyzing Organizational Dynamics

Abstract

This paper examines the dynamics of trust configurations in AI adoption within organizations and their implications for AI performance and organizational integration. The study investigates how varying degrees of trust influence AI acceptance and use, analyzing five trust states: full trust, full distrust, uncomfortable trust, blind trust, and their organizational consequences. Through qualitative research, incorporating real-life observations and interviews, the study provides a nuanced understanding of trust’s role in AI adoption. Findings indicate that full trust enhances engagement and decision-making, while distrust and blind trust present adoption challenges. The study highlights the importance of balanced trust configurations for optimizing AI integration.