THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE CHOICE OF MEASURES FOR ANTI-CRISIS MANAGEMENT AT UKRAINIAN ENTERPRISES

The instability of the business environment in Ukraine, intensified by military risks, fluctuations in global markets, and internal structural challenges, increases the likelihood of crisis situations at enterprises. Traditional anti-crisis management methods often rely on retrospective data and expert judgments, which may be insufficient under conditions of rapid change. Artificial intelligence is becoming an important tool that enhances the analytical capacity of management systems by providing predictive assessments, scenario modeling, and automated recommendations. The integration of artificial intelligence into decision-making processes allows enterprises to detect crisis signals earlier, evaluate possible intervention strategies, and choose measures that are most suitable for stabilizing the financial and operational state of the organization. The study of the role of artificial intelligence in the selection of anti-crisis management measures is therefore a relevant and strategically significant task. The goal of this research is to analyze the influence of artificial intelligence on the selection of anti-crisis management measures at Ukrainian enterprises and to determine how AI-based analytical systems change the approaches to diagnosing crises, forming response strategies, and ensuring business continuity. The study applies methods of system analysis, comparison, logical structuring, and synthesis of research results. The empirical base includes academic publications, analytical reports, and case materials on the implementation of artificial intelligence in enterprise management. Key indicators of crisis states are examined, such as liquidity reduction, profitability decline, supply chain disruptions, loss of market share, and operational inefficiency. The research also considers the role of data quality, digital maturity, and management culture in the successful application of artificial intelligence. The research identifies several ways in which artificial intelligence affects the choice of anti-crisis measures. First, AI-based forecasting models enable early detection of negative trends by processing large volumes of structured and unstructured data, including market signals, financial reports, and behavioral patterns of consumers and partners. Second, artificial intelligence supports scenario modeling, allowing enterprises to evaluate the consequences of alternative actions such as cost restructuring, diversification of suppliers, digital modernization, or temporary downsizing. Third, AI systems contribute to the optimization of resource allocation by ranking available measures according to expected efficiency and risk levels. Fourth, artificial intelligence enhances the objectivity of decisions by reducing the influence of personal biases and managerial overconfidence. Fifth, digital platforms facilitate real-time monitoring of the implementation of anti-crisis measures, enabling timely adjustments. Despite these advantages, Ukrainian enterprises face difficulties in implementing AI-driven anti-crisis systems. These include insufficient digital infrastructure, limited financial resources, lack of qualified specialists, and insufficient development of data governance policies. Artificial intelligence significantly strengthens the analytical and strategic foundations of anti-crisis management at Ukrainian enterprises. It improves early warning capabilities, supports rational decision-making, and increases adaptability. However, the effectiveness of artificial intelligence in anti-crisis systems depends on organizational readiness, management support, quality of data, and integration with existing business processes. Further research should address the development of sector-specific models of AI-supported anti-crisis strategies, evaluation of the impact of AI on managerial responsibility and trust, and formation of national standards for ethical and transparent use of artificial intelligence in enterprise management.