This paper investigates workers' experiences with automated agents (AA) in the workplace and offers actionable recommendations for organizational leaders. A simulation model quantifies and predicts workers' experiences, revealing that lower-efficiency AAs may outperform higher-efficiency ones due to trust's influence on adoption. Lower initial trust can lead to increased AA usage, while stronger emotional responses may foster trust but decrease utilization. The research integrates systems dynamics, management theories, and psychology to advance human-AI interaction understanding.

Ivan Dula, Tabea Berberena, Ksenia Keplinger, Maria Wirzberger