Automated Customer Support Systems in Service Companies: Analysis of AI Implementation

Abstract

This study analyzes the potential of integrating artificial intelligence (AI) into customer support systems within service companies. The research is based on a review of the technological foundations of AI solutions, including natural language processing (NLP, NLU), interactive systems (chatbots, virtual and voice assistants), business process automation, and analytical tools for predictive modeling. Through statistical analysis and case studies from various industries—such as the banking sector, retail, and the implementation of the Pega platform—the study identifies key performance changes, including reduced response times, an increase in first-contact resolution rates, improved customer satisfaction, and lower service costs. The study presents an integrated model for evaluating the effectiveness of AI implementation, offering recommendations for optimizing customer support automation. By addressing a scientific gap, this research combines technical, economic, and ethical aspects of AI applications in customer service. The findings will be of interest to service company executives, customer support specialists, and IT directors seeking to enhance customer interactions through AI-driven automation. Additionally, the study provides valuable insights for analysts and researchers in digital transformation, examining the impact of artificial intelligence on business processes and user experience.