To enhance the quality and efficiency of information processing and decision-making, automation based on artificial intelligence and machine learning has increasingly been used to support managerial tasks and duties. In contrast to classical applications of automation (e.g., within production or aviation), little is known about how the implementation of automation for management changes managerial work. In a work design frame, this study investigates how different versions of automated decision support systems for personnel selection as a specific management task affect decision task performance, time to reach a decision, reactions to the task (e.g., enjoyment), and self-efficacy in personnel selection. In a laboratory experiment, participants (N = 122) were randomly assigned to three groups and performed five rounds of a personnel selection task. The first group received a ranking of the applicants by an automated support system before participants processed applicant information (support-before-processing group), the second group received a ranking after they processed applicant information (support-after-processing group), and the third group received no ranking (no-support group). Results showed that satisfaction with the decision was higher for the support-after-processing group. Furthermore, participants in this group showed a steeper increase in self-efficacy in personnel selection compared to the other groups. This study combines human factors, management, and industrial/organizational psychology literature and goes beyond discussions concerning effectiveness and efficiency in the emerging area of automation in management in an attempt to stimulate research on potential effects of automation on managers’ job satisfaction and well-being at work.