RESEARCH ON THE INTEGRATION OF ARTIFICIAL INTELLIGENCE, INFORMATION TECHNOLOGIES AND ONTOLOGICAL APPROACH IN THE PROCESS OF MANAGEMENT DECISION-MAKING

Abstract

In the current conditions of global digital transformation and rapid complication of economic relations, traditional management paradigms demonstrate limited effectiveness, which makes the search for new methodological approaches to strategic decision-making relevant. The article is aimed at solving the fundamental problem of the gap between the high speed of data processing by modern technologies and the need for a deep value-based understanding of the consequences of managerial influence in the architecture of large enterprises. The purpose of the study is the theoretical justification and development of system mechanisms that provide synergy between the analytical capabilities of artificial intelligence and a higher integrative level of strategic thinking based on an ontological approach. The research methodology is based on system analysis, conceptual modeling and an axiological approach, which allowed integrating the philosophical understanding of the integrity of business processes with mathematical optimization methods. The authors of the article conceptualized three levels of integration of technological and intellectual resources, which include the stages of query formation, algorithmic validation and cyclic self-learning of the organization. The key scientific achievement is the development and mathematical description of an integral indicator of ontological stability of a management decision. This toolkit allows you to transform dry statistical data into strategic knowledge, taking into account not only direct economic benefits, but also indicators of social stability and ethical compliance with corporate goals. The practical value of the results obtained lies in the possibility of their direct implementation in the activities of large corporate structures, especially in the energy, fintech and retail sectors, where the level of automation is the highest. The proposed system of indicators and algorithms for assessing the stability of decisions can be used to improve the quality of risk management and strategic planning in conditions of uncertainty.