CLASSIFICATION OF MODERN INTELLIGENT ENTERPRISE MANAGEMENT SYSTEMS

The article is devoted to the classification of modern intelligent enterprise management systems in the context of the rapid development of digital technologies. As of today, artificial intelligence, blockchain, and quantum computing have significantly impacted business processes across various enterprises, creating the need for the systematization and classification of intelligent systems to optimize management decisions. The study examines various classification approaches, including those based on technological foundation, functionality, level of integration, adaptability, field of application, and degree of autonomy. The research emphasizes that intelligent management systems facilitate automation, decision-making based on the analysis of large volumes of data, risk forecasting, and resource optimization, which is particularly relevant in conditions of market instability, military threats, and economic uncertainty. Given the increasing complexity of production processes, enterprises are increasingly relying on intelligent systems to enhance overall efficiency, reduce costs, and ensure resilience to both external and internal changes. The article highlights the strategic role of intelligent management systems in the digital transformation of enterprises and their potential to contribute to achieving sustainable and stable development. Special attention is given to the role of intelligent management systems in ensuring data security and transparency, as well as the importance of realtime data processing for effective managerial decision-making to significantly improve productivity, operational stability, and cost reduction. The classification proposed in the article allows enterprises to systematically assess different types of intelligent systems and select the most effective solutions according to their urgent operational needs. The study underlines the importance of integrating intelligent systems into modern management practices. The obtained results may serve as a methodological basis for decision-making on the implementation of intelligent systems in industrial enterprises, providing a structured approach to enhancing business operational efficiency and strategic development..