Navigating Power Dynamics in the Public Sector through AI-driven Algorithmic Decision-Making

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 increasingly influential “hybrid”/in-house analysts with both technical and institutional expertise. The study introduces and empirically validates AI Power Enactment Framework and proposes the AI Power Matrix, offering policymakers a structured tool to evaluate AI projects. These insights can guide targeted funding strategies and capacity building to reduce overreliance on hybrid analysts and better support AI implementation success in the public sector.

URI

https://bradscholars.brad.ac.uk/handle/10454/20461

Version

Published version

Citation

Mahroof K, Weerakkody V, Hussain Z, et al (2025) Navigating Power Dynamics in the Public Sector through AI-driven Algorithmic Decision-Making. Government Information Quarterly. 42(3): 102053.

Link to Version of Record

https://doi.org/10.1016/j.giq.2025.102053

Type

Article