Bibliometric Analysis of Artificial Intelligence Development in Customer Service Automation

This study presents a comprehensive bibliometric analysis of scholarly literature on the development of artificial intelligence (AI) in customer service automation, based on data extracted from the Scopus database between 2000 and 2024. Using VOSviewer, the analysis maps the intellectual structure, thematic evolution, and collaborative networks within this rapidly growing research field. Findings reveal that core research themes revolve around customer satisfaction, chatbots, natural language processing, and machine learning—highlighting the shift from back-end AI infrastructure toward user-facing, interactive applications. The overlay visualization indicates a temporal progression, with earlier studies focusing on big data and cloud computing, while more recent works emphasize conversational AI and customer experience. Co-authorship and country collaboration networks show two dominant scholarly communities—one centered in East Asia with a technical focus, and another in Western countries emphasizing service quality and marketing perspectives. Despite the field's growth, gaps remain in cross-regional collaboration, ethical design, and theoretical integration. This study offers valuable insights for researchers, practitioners, and policymakers aiming to advance AI-driven customer service strategies that are both innovative and ethically sound.