The use of artificial intelligence in managing enterprise service processes

Abstract

Abstract. The relevance of this study is determined by the growing role of service activities in ensuring enterprise competitiveness in B2B markets under conditions of digital transformation. Unified service standards fail to ensure effective interaction with organizational customers due to differences in their economic, technological, and behavioral characteristics. In this context, the implementation of data-driven approaches to service process management based on artificial intelligence becomes particularly significant. The purpose of this article is to substantiate methodological approaches to the application of artificial intelligence in enterprise service process management for the formation of differentiated service standards for organizational customers based on objective analytical data. The study employs methods of analysis and synthesis, systems approach, economic and statistical analysis, cluster analysis, machine learning techniques, and business process modeling within the SmartLube 4.0 digital platform. The information base includes data from CRM, ERP, and IoT systems, as well as results from international indices of digital and institutional readiness. The article substantiates the role of artificial intelligence as a tool for integrating economic, technological, and behavioral customer parameters into a unified analytical support system for service management. A system of AI-based segmentation criteria for organizational customers and a methodology for forming customer clusters based on integral indices are proposed. A model for differentiating service standards according to customers’ strategic significance and service load levels is developed. An algorithm for adaptive updating of service standards using automated monitoring and recalculation of analytical indicators is formulated. The obtained results can be used by enterprises in service-oriented industries for implementing digital customer relationship management platforms, developing intelligent decision-support systems, optimizing service processes, and enhancing the effectiveness of data-driven managerial decisions.