Enhancing Customer Complaint Management through AI-Based Business Process Improvement
DOI:
https://doi.org/10.30812/matrik.v25i2.5825Keywords:
Artificial Intelligence, Business Process Analysis, Business Process Improvement, Complaint Handling, Customer Service AutomationAbstract
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.
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References
[1] P. Fettke and C. Di Francescomarino, “Business Process Management and Artificial Intelligence: Literature Survey and Future
Research,” KI - K¨unstliche Intelligenz, vol. 39, no. 2, pp. 67–79, Jun. 2025, https://doi.org/10.1007/s13218-025-00891-y.
[2] A. R. Teixeira, J. V. Ferreira, and A. L. Ramos, “Optimization of Business Processes Through BPM Methodology: A Case
Study on Data Analysis and Performance Improvement,” Information, vol. 15, no. 11, p. 724, Nov. 2024, https://doi.org/10.
3390/info15110724.
[3] W. Jin, N. Wang, L. Zhang, X. Tian, B. Shi, and B. Zhao, “A Review of AI-Driven Automation Technologies: Latest Taxonomies,
Existing Challenges, and Future Prospects,” Computers, Materials & Continua, vol. 84, no. 3, pp. 3961–4018, 2025,
https://doi.org/10.32604/cmc.2025.067857.
[4] Y. Vaillant, E. Lafuente, and F. Vendrell-Herrero, “Automation, augmentation, or dual AI strategies for superior product line
performance: The functional subsidiarity challenge,” International Journal of Production Economics, vol. 295, p. 109931, May
2026, https://doi.org/10.1016/j.ijpe.2026.109931.
[5] P. Gomes, L. Verc¸osa, F. Melo, V. Silva, C. B. Filho, and B. Bezerra, “Artificial Intelligence-Based Methods for Business
Processes: A Systematic Literature Review,” Applied Sciences, vol. 12, no. 5, p. 2314, Feb. 2022, https://doi.org/10.3390/
app12052314.
[6] J. Dioses and L. Cordova, “A Survey of Process Mining for Customer Management,” in CITIIC 2023, vol. 83, no. 1. MDPI,
Jan. 2025, p. 7, https://doi.org/10.3390/engproc2025083007.
[7] C.-N. Wang, T. T. B. C. Vo, H.-P. Hsu, Y.-C. Chung, N. T. Nguyen, and N.-L. Nhieu, “Improving processing efficiency through
workflow process reengineering, simulation and value stream mapping: A case study of business process reengineering,” Business
Process Management Journal, vol. 30, no. 7, pp. 2482–2515, Nov. 2024, https://doi.org/10.1108/BPMJ-11-2023-0869.
[8] J. Yang, Y. Blount, and A. Amrollahi, “Artificial intelligence adoption in a professional service industry: A multiple case study,”
Technological Forecasting and Social Change, vol. 201, p. 123251, Apr. 2024, https://doi.org/10.1016/j.techfore.2024.123251.
[9] J. Tang, Y. Liu, K.-y. Lin, and L. Li, “Process bottlenecks identification and its root cause analysis using fusion-based clustering
and knowledge graph,” Advanced Engineering Informatics, vol. 55, p. 101862, Jan. 2023, https://doi.org/10.1016/j.aei.2022.
101862.
[10] M. Amissah and S. Lahiri, “Modelling Granular Process Flow Information to Reduce Bottlenecks in the Emergency Department,”
Healthcare, vol. 10, no. 5, p. 942, May 2022, https://doi.org/10.3390/healthcare10050942.
[11] L. Pufahl, F. Zerbato, B. Weber, and I. Weber, “BPMN in healthcare: Challenges and best practices,” Information Systems, vol.
107, p. 102013, Jul. 2022, https://doi.org/10.1016/j.is.2022.102013.
[12] R. Choudhary and N. Riaz, “A business process re-engineering approach to transform business process simulation to BPMN
model,” PLOS ONE, vol. 18, no. 3, p. e0277217, Mar. 2023, https://doi.org/10.1371/journal.pone.0277217.
[13] M. Rosemann, J. V. Brocke, A. Van Looy, and F. Santoro, “Business process management in the age of AI – three essential
drifts,” Information Systems and e-Business Management, vol. 22, no. 3, pp. 415–429, Sep. 2024, https://doi.org/10.1007/
s10257-024-00689-9.
[14] H. Zhao, Z. Song, and Z. Cai, “Should AI or human agents handle customer complaints? Exploring the impact of agent type
and complaint response type on recovery outcomes,” Journal of Business Research, vol. 202, p. 115805, Jan. 2026, https:
//doi.org/10.1016/j.jbusres.2025.115805.
[15] B. Li, L. Liu, W. Mao, Y. Qu, and Y. Chen, “Voice artificial intelligence service failure and customer complaint behavior:
The mediation effect of customer emotion,” Electronic Commerce Research and Applications, vol. 59, p. 101261, May 2023,
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