Optimization of autonomous business processes of industrial enterprises based on predictive artificial intelligence models

Abstract

The relevance of the study is determined by the increasing complexity of production systems, the instability of the external environment, and the growing requirements for efficient resource management at industrial enterprises, which limits the effectiveness of traditional reactive approaches and actualizes the implementation of predictive tools based on AI. The purpose of the study is to substantiate the theoretical and methodological foundations and to develop practical approaches to optimizing autonomous business processes of industrial enterprises using predictive AI models. Methods. The study applies methods of analysis and synthesis to generalize theoretical approaches to the autonomization of business processes, systematization to structure their characteristics and mechanisms of functioning, comparative analysis to evaluate the effectiveness of predictive analytics in production management, as well as logical generalization to develop practical recommendations. Results. The economic essence of autonomous business processes is examined, and the functional role of predictive AI models as a tool of anticipatory management is identified. It is revealed that the integration of predictive models into operational control loops ensures a transition to a proactive model of enterprise functioning, enhancing adaptability and consistency of managerial decisions. It is proven that the use of predictive analytics contributes to reducing operational costs, minimizing downtime, and optimizing the utilization of production capacities. It is established that the effectiveness of AI implementation is constrained by issues of data quality and integration, model interpretability, difficulties of integration into existing information systems, as well as organizational and economic barriers. Practical recommendations are substantiated, including the formation of an integrated data infrastructure, embedding models into key business processes, implementation of closed-loop management systems, and ensuring the adaptability of analytical solutions. Conclusions. It is concluded that the systematic use of predictive AI models enhances the efficiency, flexibility, and resilience of industrial enterprises. Prospects for further research. Future studies are associated with the development of methodological approaches to the quantitative assessment of the effectiveness of autonomous business processes, improvement of AI model interpretability, and investigation of their scalability under conditions of digital transformation.