Abstract: Artificial intelligence adoption in organizations fails at rates approaching 80%, despite substantial investment and strategic priority. This article synthesizes findings from a real-world qualitative study tracking AI implementation in a software development firm to reveal how organizational members develop four distinct trust configurations—full trust, full distrust, uncomfortable trust, and blind trust—each triggering different behavioral responses that fundamentally shape AI performance and adoption outcomes. Unlike previous research assuming use/non-use as the primary behavioral outcome, this analysis demonstrates that organizational members actively detail, confine, withdraw, or manipulate their digital footprints based on trust configurations, creating a vicious cycle where biased or asymmetric data degrades AI performance, further eroding trust and stalling adoption. The article offers evidence-based interventions addressing both cognitive trust (through transparency, training, and realistic expectation-setting) and emotional trust (through psychological safety, ethical governance, and leadership emotional contagion), while highlighting the critical insight that organizational culture alone cannot guarantee AI adoption success. Organizations must develop personalized, trust-configuration-specific strategies that recognize the intricate interplay between rational evaluation and emotional response in technology adoption.