Enhancing Customer Complaint Management through AI-Based Business Process Improvement - Zain Ammar Falih; Deki Satria; Vandha Pradwiyasma Widartha Yasma (MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 2026)
The rapid advancement of digital technology has transformed business process management, particularly in the telecommunications sector, where manual customer complaint handling often causes inefficiencies such as delays, ticket backlog, and human error. The purpose of this study is to investigate how artificial intelligence can enhance the efficiency and effectiveness of customer complaint handling by redesigning workflows through process automation. This study employs a qualitative descriptive approach combined with business process analysis, with data collected through observations, in-depth interviews with 32 participants, and document reviews. NVivo software was used to code interview data, while Bizagi Modeler was used to visualize both the existing and proposed business processes. The results indicate several bottlenecks in the existing complaint handling process, including manual first call resolution activities, inefficient complaint classification, redundant coordination between units, and low customer confirmation rates. To address these issues, the proposed improved process introduces artificial intelligence–based solutions, such as automated first-call resolution, ticket classification using natural language processing, intelligent ticket routing, and automated customer confirmation systems. These improvements are projected to reduce complaint-handling time by 25–40 percent, minimize service-level agreement violations, and optimize resource allocation. This study concludes that integrating artificial intelligence into customer complaint handling processes significantly improves efficiency, accuracy, and service quality, while also supporting organizational digital transformation. Furthermore, the findings make theoretical contributions to the business process management literature and provide practical insights for implementing artificial intelligence–driven automation in large-scale telecommunications environments.