Across two essays, I explore how artificial intelligence (AI) applications can help businesses automate customer service with deep learning-driven natural conversation and improve employee performance with work supervision. I apply machine learning methods such as audio analytics and text mining, as well as field experiments to explore these new AI-driven capabilities in customer service and employee supervision automation. Substantively, this research tackles emerging business questions regarding how AI applications can assist customer purchases and employee job performance. In Essay One, I apply two experiments to investigate when and how AI voicebots work or struggle in persuading customers relative to human agents. In Experiment 1, I apply audio analytics to extract agents’ voice features (i.e., pitch, amplitude, and speed) and speech content (i.e., selling adaptivity). My analyses suggest two distinct routes to explain how agents’ speech patterns account for their performance. Analyses in Experiment 2 demonstrate that relative to human agents, AI bots could backfire and lead to worse performance when the customer persuasion task is more complex. In my second essay, I explore the coexistence of performance improvement and employee resistance to AI supervision. Specifically, I develop a novel two-by-two field experiment, which randomly assigns the AI or human supervision entity and discloses the entity or not, to separate the economic gain from negative reactance to AI. In addition, I uncover the underlying mechanism by identifying employees’ subjective bias to the AI feedback quality and heightened fear of job replacement once they know the supervision entity is AI rather than human managers. I propose two strategies to alleviate employees’ resistance to AI supervision. / Business Administration/Marketing