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Artificial intelligence and public sector transformation: A systematic literature review on applications, governance and acceptability factors

Aug 2026 · Multidisciplinary Reviews · Vol 10, pp. 2027079 · 0 citations · 28 references

TL;DR

A systematic review of the literature published between 2020 and 2025, conducted in accordance with the PRISMA guidelines to ensure methodological rigour and reliability, indicates that the adoption of artificial intelligence in public management control remains a critical issue.

Abstract

The public sector is undergoing a profound digital transformation characterised by the increasing integration of artificial intelligence into its management control practices. Concurrently, the optimisation of public performance and accountability has emerged as a central concern among scholars, particularly within the broader context of digital transition. This period of structural change calls for renewed forms of governance and organisational steering grounded in innovative and adaptive approaches.  Research on artificial intelligence in public administration has developed significantly since the early 2020s, although the effective adoption of these technologies continues to present substantial challenges. This article offers a systematic review of the literature published between 2020 and 2025, conducted in accordance with the PRISMA guidelines to ensure methodological rigour and reliability. The content analysis is based on a corpus of twenty-five selected articles. The findings indicate that the adoption of artificial intelligence in public management control remains a critical issue. AI has contributed to innovation across five principal domains: financial auditing, budget forecasting, operational optimisation, public health, and legal support. These developments open promising avenues for enhancing the quality of public management, particularly through the effective implementation of technological innovations, the strengthening of institutional adaptability and resilience, the development of essential digital competencies, the cultivation of critical engagement with algorithmic systems, and the reinforcement of digital literacy among public officials. Nevertheless, significant ethical challenges persist, including algorithmic opacity, discriminatory bias, and tensions between efficiency and equity. Addressing these concerns requires the establishment of appropriate governance frameworks and the consolidation of broader societal acceptance.

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