THE ROLE OF ARTIFICIAL INTELLIGENCE IN IMPROVING THE EFFICIENCY OF BUSINESS PROCESSES: A COMPARATIVE ANALYSIS

Abstract

The purpose of the study: The purpose of the article is to make a comparative analysis of artificial intelligence (AI) methods for improving the efficiency of business processes in retail and logistics, as well as to develop practical recommendations for their implementation. The study aims to determine the optimal approaches to integrating AI technologies to ensure operational excellence in the context of global digitalization of the economy and increasing requirements for the speed and adaptability of business solutions. Methods and approaches: The study applies a comprehensive methodological approach, including a systematic analysis of scientific publications of 2024-2025, a comparative analysis of the effectiveness of AI methods, a case study analysis of practical examples, content analysis to identify key trends, and synthesis to formulate recommendations. The criteria for evaluating efficiency were reduced operating costs, increased process speed, decision-making accuracy, scalability, and level of automation. Results: Three key AI methods are identified: Robotic Process Automation (RPA), Intelligent Robotic Process Automation (IRPA), and predictive analytics. RPA reduces costs by 30-50% and speeds up routine processes by up to 10 times. Predictive analytics increases the accuracy of demand forecasting by 25-30%, optimizing inventory management. IRPA provides up to 100% accuracy for complex tasks. The integrated application of methods creates a synergistic effect, increasing efficiency by 15-25%. Scientific novelty: For the first time, quantitative performance indicators of RPA, IRPA, and predictive analytics in retail and logistics are systematized, which contributes to the informed choice of AI solutions for different types of business processes. Practical significance: Recommendations for the implementation of AI: RPA for order processing automation, predictive analytics for demand optimization, and IRPA for warehouse operations. The importance of staff training, adaptive corporate culture, and phased implementation of AI for maximum efficiency is emphasized. Prospects: Further research involves the development of adaptive AI systems for small and medium-sized businesses, analysis of ethical aspects and the long-term impact of AI on the structure of labor markets and the economy.