Abstract
The relevance of this study is determined by the growing scale of crisis phenomena in the public sector, especially under the conditions of military and economic challenges in Ukraine, which require improving the effectiveness of public administration. Modern scientific research focuses on the essence, principles, and mechanisms of anti-crisis management; however, the integration of digital tools, particularly artificial intelligence (AI), remains insufficiently developed. The purpose of the article is to substantiate theoretical foundations and practical approaches to anti-crisis management in the public sector and to analyze the potential of AI technologies for improving decision-making efficiency. The methodological basis of the study includes a systemic approach, methods of analysis and synthesis of scientific sources, comparative analysis, generalization, and structural-logical modeling. It is proven that anti-crisis management should be considered as a continuous process aimed at risk identification, timely response, and the use of crises as opportunities for development. Global trends in AI implementation are analyzed, confirming its growing role in forecasting, risk management, and ensuring the resilience of public systems. The feasibility of applying three groups of AI models –predictive, classification, and clustering – is substantiated, and key data sources for their implementation in Ukraine are identified. It is established that AI contributes to increasing forecast accuracy, optimizing resource allocation, reducing managerial errors, and enabling the transition to a proactive governance model. The study proves that the integration of artificial intelligence into anti-crisis management is a strategic direction for strengthening the resilience of Ukraine’s public sector. The proposed approach ensures early detection of threats, improves the validity of management decisions, and supports the development of an effective early warning system. The results highlight the necessity of developing national digital infrastructure and further implementing intelligent analytical systems in public administration.
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