THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATED POSTAL SORTING OPTIMIZATION IN THE ECONOMIC DEVELOPMENT OF THE POSTAL SPHERE AND ENTERPRISES

Introduction. The advent of artificial intelligence (AI) has introduced transformative changes across numerous sectors, including the postal industry. As global postal services face increasing demands and challenges, the integration of AI technologies in automatic sorting processes presents an opportunity to enhance operational efficiency, reduce costs, and improve service quality. Traditional sorting methods, while effective in the past, struggle to keep pace with the demands of the digital age. AI, through its advanced algorithms and machine learning capabilities, presents a game-changing solution. By streamlining operations, reducing costs, and enhancing overall efficiency, AI is poised to revolutionize postal sorting processes. This essay delves into the role of AI in optimizing automatic postal sorting processes, highlighting its economic impact on the postal sphere and individual enterprises. The study examines existing literature and employs a mixed-method approach to analyze the benefits and challenges of AI integration. Results indicate significant improvements in efficiency, cost reduction, and customer satisfaction, suggesting a promising future for AI-driven postal services. Purpose. The purpose of this study is to investigate the impact of artificial intelligence on automatic postal sorting processes and to assess how these technological advancements contribute to the economic development of the postal sector and individual enterprises. The study aims to provide a comprehensive understanding of the benefits, challenges, and future prospects of artificial intelligence integration in postal operations. Materials and methods. The study draws on a wide range of literature, including academic articles, industry reports, and case studies, to build a foundational understanding of artificial intelligence applications in postal services. Key topics include the historical development of postal sorting technologies, current AI-driven solutions, and economic analyses of AI implementation. A mixed-method approach is employed, combining quantitative data analysis with qualitative case studies. Quantitative data includes operational metrics from postal services that have implemented AI, while qualitative data is gathered from interviews with industry experts and stakeholders. Results. The study's findings reveal that AI-driven postal sorting systems significantly enhance sorting speed, accuracy, and overall operational efficiency. Cost analysis shows substantial savings in labor and operational expenses. Furthermore, customer satisfaction levels have improved due to faster delivery times and fewer sorting errors. Discussion. Looking ahead, the integration of artificial intelligence in automatic postal sorting is expected to expand, with continuous advancements in machine learning algorithms and robotics. Future research should focus on addressing the remaining challenges, such as data security and system integration, to fully realize the potential of artificial intelligence usage in postal services.