Employee performance evaluations have been subject to a lot of criticism and organizations are now leveraging artificial intelligence (AI) to enhance and maximize the efficiency and accuracy of these performance evaluations. Although organizations assume that AI-driven performance evaluation systems will enhance traditional performance evaluation systems, a growing body of research documents the phenomenon of algorithm aversion, the human tendency to discount algorithm/computer generated advice more heavily than human advice. Using an employee performance evaluation setting, I conduct an experiment to examine how managers will resolve differences between two contradictory judgments, their own judgment and an AI's judgment. I find that in addition to algorithm aversion and an individual's attitude towards technology, the performance evaluation measures (objective or subjective), and more importantly, the consequence of the decision on the employee strongly influenced the manager's reliance on AI. Specifically, managers resolved conflict between AI and their own decision by relying on decisions that were in the employee's favor. The study contributes to existing research on the adoption of AI and management accounting research.