Navigating power dynamics in the public sector through AI-driven algorithmic decision-making

Abstract

Public sector institutions are under increasing pressure to deliver greater public value through disruptive technologies, despite ongoing pressures. In response to evolving technological change and an abundance of information, many public sector organisations have adopted Artificial Intelligence (AI) to improve decision-making and generate social value. While AI's role in public administration is gaining attention, little is known about how its use alters internal power dynamics. This research uses a qualitative case study approach, drawing on 30 semi-structured interviews with operational managers and various analysts in a large public institution to explore how AI influences power relations. Findings reveal that AI use creates tensions among operational managers, organisation-wide analysts and the increasingly influential hybrid/in-house analysts who possess both technical and institutional expertise. The study presents and empirically validates the AI Power Enactment Framework and introduces the AI Power Matrix, providing policymakers with a structured tool to evaluate AI projects. These insights can inform targeted funding strategies and capacity building, helping to lessen dependence on hybrid analysts and enhance the success of AI implementation in the public sector.