Implementation of artificial intelligence in public administration: Prospects and challenges of the digital era
Borys Dziundziuk & Viacheslav Dziundziuk
Abstract
The study aims to examine the opportunities and risks associated with the integration of artificial intelligence (AI) into public administration processes. For this purpose, the advantages and disadvantages of using AI were analysed. The study employed a qualitative comparative analysis of policy and legal documents from Austria, Belgium, Denmark, Finland and Ukraine to identify patterns of AI integration in public administration. Three forms of AI deployment are identified by the analysis: technocratic, participative and adaptive. These models each represent varying degrees of institutional readiness and governance maturity. The results show that although AI improves decision-making, efficiency and transparency, scattered data systems, uneven institutional coordination and restricted interoperability frequently impede its practical application. The study presents the Administrative AI Readiness Model, a conceptual framework that connects the adoption of AI's technological, ethical and human-capital aspects. Limitations include the use of secondary data, findings that are interpretive rather than empirical and the fact that regulatory developments are provisional. In spite of this, the study advances understanding of how ethical and institutional factors influence responsible AI governance in government. Points for practitioners Artificial intelligence (AI) enables automation of routine tasks in public administration, improving service delivery. AI integration enhances data-driven decision-making and resource allocation in government. Implementation challenges include cybersecurity, privacy protection and ethical considerations.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 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.