Navigating the Automation–Augmentation Paradox: A Case Study of Artificial Intelligence Integration in Public Management Functions

Abstract The application and utilization of Artificial Intelligence (AI) have increased recently in all organizations, including those in the public sector. From an organizational perspective, AI utilization should generate both economic and social gains. In this context, it is necessary to address the automation-augmentation paradox. Namely, this paradox stems from the question of whether AI replaces humans in performing tasks (i.e., automation) or whether the application of AI necessitates an increase in AI-human interactions (i.e., augmentation). The starting point of the research is the POSDCORB framework, which delineates the core functions of a manager and offers a classical perspective on their administrative responsibilities. AI shall increasingly transform the POSDCORB framework by streamlining management functions and enhancing decision-making accuracy. The main research question is what can be automated using AI for management functions, and what requires augmentation between AI and humans for these functions? To assess AI’s impact on POSDCORB managerial functions in public sector organizations, the study uses both qualitative and quantitative data sources, enhancing validity through data triangulation. The research findings indicate that AI’s impact varies across management functions, with public managers particularly preferring augmentation for strategic planning tasks, coordination, reporting, and potential dynamic financial adjustments. Finally, public managers’ report ongoing tension due to a tendency to switch between automation and augmentation as tasks evolve.