MINI REVIEW article

Front. Big Data

Sec. Data Analytics for Social Impact

Using Artificial Intelligence to Improve Governance and Public Services in Africa

  • Extraordinary Professor, Department of Financial Governance, University of South Africa, Pretoria, South Africa

The final, formatted version of the article will be published soon.

Abstract

African governments continue to face persistent challenges in delivering efficient, transparent, and inclusive public services due to institutional constraints, rapid population growth, and fragmented administrative systems. At the same time, accelerating digital transformation and expanding data ecosystems create new opportunities for governance reform. This study examines the role of Artificial Intelligence (AI) in enhancing public sector performance across welfare targeting, healthcare delivery, tax administration, and urban governance. Drawing on a structured narrative literature review, the paper develops a conceptual framework that conceptualises governance outcomes as a function of data availability, AI capability, institutional capacity, and human oversight. The findings suggest that AI can improve service delivery by enabling predictive decision-making, reducing administrative inefficiencies, and enhancing targeting accuracy. However, these benefits depend on the alignment between technological adoption and institutional readiness, as weak governance systems may amplify risks such as bias, exclusion, and accountability gaps. The study concludes that AI must be embedded in inclusive, context-sensitive governance strategies to support sustainable development outcomes in Africa.

Summary

Keywords

Africa, artificial intelligence, governance, Public services, sustainability

Received

27 March 2026

Accepted

20 May 2026

Copyright

© 2026 Mhlanga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: David Mhlanga

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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