Beyond stuck in the middle: Application context and role integration of automated leadership in traditional organizations

Abstract

Artificial intelligence (AI) is transforming traditional organizational structures, challenging established leadership paradigms, and reshaping human–machine collaboration in the workplace. As automated leadership agents gain prominence, understanding their expected role and contextual application becomes crucial. Based on 24 qualitative interviews with leaders (31 h total) and three focus groups with employees (4.5 h), this study explores the role integration and application context of automated leadership agents in traditional organizations. Our findings reveal that automated leadership agents are expected to assume multiple roles, such as algorithmic authority, collaborative co-leader, automated leader-assistant, and obedient operator, within the same organizational environment and are positioned in middle management. These role expectations introduce a high risk of role conflict and ambiguity. The findings contribute to a more nuanced understanding and offer practical guidance for traditional organizations aiming to integrate automated leadership agents successfully.