Improving the management of energy sector entrepreneurial structures based on a proactive approach

The purpose of the article is to substantiate the conceptual framework of the mechanism for the proactive management of entrepreneurial structures in the energy sector (MMESES), based on artificial intelligence analytics and predictive modeling, to ensure their competitive and sustainable development. The research methods encompass a comprehensive set of analytical tools, including heuristic and variational analysis, canonical correlations, taxonomy methods for calculating integral rating assessments, spatial-dynamic cluster analysis (hierarchical agglomerative and k-means clustering), as well as econometric modeling using dynamic panel data models with fixed effects, validated by Fisher and Hausman tests. The research results demonstrate that the fundamental transformation of the Ukrainian energy market requires a transition from traditional post-factum reactions to an anticipatory management paradigm. The implementation of the AI analytics block revealed a critical financial state of the analyzed enterprises, with a median financial independence ratio of only 16 percent. Furthermore, predictive analytics using dynamic panel data models through 2026 identified high market volatility, with 43 percent of energy companies prone to migration between clusters under the influence of external shocks. Based on migration trends, four reference strategies were proposed for energy companies: a leadership strategy focused on technological leadership and ecosystem partnerships; a preventive strategy focused on cost optimization and risk management; a challenger strategy focused on digital service development; and a safety strategy focused on financial stabilization. The scientific novelty of the study lies in the development of a two-level proactive management mechanism that integrates AI-driven end-to-end benchmarking with predictive modeling of panel data. Unlike existing approaches, this mechanism transforms reactive management into a system capable of identifying threats at early stages and mathematically justifying strategic decisions based on inter-cluster migration forecasting. The practical significance of the obtained results is determined by energy supply companies’ ability to use the developed panel data models and cluster matrices to forecast strategic migration, justify key target performance indicators, and formulate a comprehensive set of preventive measures necessary for sustainable development in highly turbulent environments. The prospects for further research include adapting the proposed proactive management mechanism for small and medium-sized enterprises in the renewable energy sector and integrating blockchain technologies to enhance the transparency of predictive analytics and anticipatory governance. Keywords: proactive management, AI analytics, energy sector, entrepreneurial structures, predictive models, strategy.