DEVELOPMENT OF DECISION-MAKING MODELS AND ARTIFICIAL INTELLIGENCE SYSTEMS FOR ENTERPRISES

Abstract

The article explores the processes of building decision-making models and implementing artificial intelligence systems in enterprises as tools for improving business process management. The purpose of the article is to highlight the specific features of developing decision-making models in business based on artificial intelligence. The study employed general scientific methods of cognition, such as analysis, synthesis, induction, deduction, comparison, generalization, and a systems approach. The findings show that in today’s dynamic business environment, modeling plays a central role in strategic decision-making, helping companies reduce risks and increase management efficiency. Traditional modeling methods, including statistical and linear approaches, remain fundamental tools for analysis and forecasting, but their capabilities are no longer sufficient under conditions of heightened uncertainty. It was found that modern approaches, such as scenario planning and artificial intelligence technologies, offer flexibility, adaptability, and a deeper level of analytics, significantly expanding the boundaries of traditional business modeling. The research reveals that artificial intelligence systems play a key role in transforming decision-making processes by providing tools for analyzing large volumes of data, forecasting trends, identifying hidden correlations, and personalizing customer experiences. These systems are capable not only of processing information in real time, but also of learning from collected data using machine learning algorithms and neural networks.This ability to self-adapt enables AI systems to predict the outcomes of various management decisions, conduct multifactor analysis, and automatically determine optimal action strategies under unpredictable conditions. The study concludes that the use of such systems provides enterprises with a strategic advantage, allowing them to become more flexible, innovative, and competitive. The practical value of the research lies in developing recommendations for implementing intelligent management systems to enhance the adaptability and efficiency of enterprises in the context of digital transformation.