Artificial intelligence in public administration: a comprehensive literature review on opportunities, challenges and strategic implementation

Benkirane Kawtar & Benazzi Khadija

International Journal of Technology Intelligence and Planning2025https://doi.org/10.1504/ijtip.2025.150735article
AJG 1
Weight
0.50

What the paper says

Artificial intelligence (AI) is reshaping public administration through applications such as predictive policing, fraud detection, and chatbots. This literature review examines AI's integration in the public sector, focusing on opportunities and challenges in adopting technologies like machine learning, natural language processing, robotics, and big data analytics. Key benefits include improved efficiency, data-driven decision making, cost reduction and enhanced transparency. However, challenges persist, including ethical and privacy concerns, data governance, technological integration, and workforce skills. Drawing on case studies in healthcare, public safety, smart city management, and social services, this review outlines strategic measures for effective AI adoption, including clear policies, investment in data infrastructure, cross-sector collaboration, and robust ethical frameworks. It offers a concise foundation for understanding AI's impact on public administration.

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https://doi.org/https://doi.org/10.1504/ijtip.2025.150735

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@article{benkirane2025,
  title        = {{Artificial intelligence in public administration: a comprehensive literature review on opportunities, challenges and strategic implementation}},
  author       = {Benkirane Kawtar & Benazzi Khadija},
  journal      = {International Journal of Technology Intelligence and Planning},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtip.2025.150735},
}

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Artificial intelligence in public administration: a comprehensive literature review on opportunities, challenges and strategic implementation

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.