Nowadays, customer service in telecommunications companies is often characterized by long waiting times and impersonal responses, leading to customer dissatisfaction, increased complaints, and higher operational costs. This study aims to optimize the customer service process through the implementation of a Generative AI Voicebot, developed using the SCRUMBAN methodology, which comprises seven phases: Objectives, To-Do Tasks, Analysis, Development, Testing, Deployment, and Completion. An experimental design was used with an experimental group and a control group, selecting a representative sample of 30 customer service processes for each evaluated indicator. The results showed a 34.72% reduction in the average time to resolve issues, a 33.12% decrease in service cancellation rates, and a 97% increase in customer satisfaction. The implications of this research suggest that the use of Generative AI In Voicebots can transform support strategies in service companies. In conclusion, the implementation of the Generative AI Voicebot has proven effective in significantly reducing resolution time and markedly increasing customer satisfaction. Future research is recommended to further explore the SCRUMBAN methodology and extend the use of Generative AI Voicebots in various business contexts.

Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. Artificial Intelligence Applications and Innovations, 373–383. https://doi.org/10.1007/978-3-030-49186-4_31

Ansari, S., Debo, L., Iravani, S. M. R. (2024). Scheduling policies to minimize abandonment costs in infomercial call centers. IISE Transactions, 1–17. https://doi.org/10.1080/24725854.2024.2331583

Antineskul, E., Kovalev, V., Magasumov, A. (2023). Retention of provider clients with variable quality of communication services. In Science and Global Challenges of the 21st Century – Innovations and Technologies in Interdisciplinary Applications, 708–724. https://doi.org/10.1007/978-3-031-28086-3_65

Arora, J., Roy, S., Sharan, V., et al. (2023). An interactive Voicebot using RASA framework for migrant workers. In Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development (ICIDSSD 2022), 24-25 March 2022, New Delhi, India. https://doi.org/10.4108/eai.24-3-2022.2319009

Burgos-Medina, F., Tinoco-Condor, K., Gamboa-Cruzado, J. (2021). Sistema Web para la Gestión de Citas en Centros de Atención Psicológica: Un Caso de Estudio. Revista Ibérica de Sistemas e Tecnologías de Información, E45, 458-473.

Casazola Cruz, O. D., Alfaro Mariño, G., Burgos Tejada, J., et al. (2021). La usabilidad percibida de los chatbots sobre la Atención al cliente en las organizaciones: Una Revisión de la literatura. Interfases, 14(014), 184–204. https://doi.org/10.26439/interfases2021.n014.5401

Chandra, S. (2020). Virtual Bank Assistance: An AI Based Voice Bot for better Banking. International Journal of Research, 9, 177–184. https://doi.org/10.13140/RG.2.2.21535.10405

Dávila, Y., García, H. A., Barreras, G., et al. (2023). Reduction in Call Abandonment and Waiting Time in the Call Center Fraud Division at a Financial Institution in Puerto Rico. Proceedings of the IISE Annual Conference & Expo 2023, 1-6. https://doi.org/10.21872/2023IISE_1369

Dikareva-Brugman, A., Guyt, J. Y., Konuș, U. (2023). The impact of forced and reinforced channel migration strategies on Churn: Evidence from a quasi-natural experiment. Journal of Interactive Marketing, 59(1), 19–41. https://doi.org/10.1177/10949968231173885

Diware, P. R., Kolte, P. K., Patil, M. G., et al. (2021). A review on AI based chatbot with virtual assistant. International Journal of Interdisciplinary Innovative Research & Development (IJIIRD), 6(Special Issue 1).

Gamboa-Cruzado, J., Carbajal-Jiménez, P., Romero-Villón, M., et al. (2022). Chatbots for customer service: A comprehensive systematic literature review. Journal of Theoretical and Applied Information Technology, 100(19), 5587–5598.

Gauthier, E., Wade, P. S., Moudenc, T., et al. (2022). Proof-of-Concept of a Voicebot Speaking Wolof. In Proceedings of the 29th Conference on Natural Language Processing, vol. 1, main conference, 403–412. Avignon, France.

Griol, D., Molina, J. M., Callejas, Z. (2019). Developing multimodal conversational agents for personalized attention in telecommunication services. Expert Systems with Applications, 122, 163–183. https://doi.org/10.1016/j.eswa.2018.12.020

Hoyer, W. D., Kroschke, M., Schmitt, B., et al. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, 57–71. https://doi.org/10.1016/j.intmar.2020.04.001

Huang, M. H., Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9

Iparraguirre-Villanueva, O., Obregon-Palomino, L., Pujay-Iglesias, W., et al. (2023). Agente inteligente para la gestión de incidencias. RISTI - Revista Ibérica de Sistemas e Tecnologías de Información, 51, 99–115. https://doi.org/10.17013/risti.51.99-115

Lara Gavilánez, H. R., Naranjo Peña, I. E., Arteaga Yaguar, E. R. (2021). Propuesta de Mejora para reducir los tiempos de espera mediante un Modelo Matemático-computacional de líneas de Espera. Ecuadorian Science Journal, 5(2), 83–99. https://doi.org/10.46480/esj.5.2.124

Loaiza, W. E., Guatumillo, E. L., Jiménez, W. R. (2020). Impacto de un chat conversacional en la atención al cliente de las empresas de servicios de la provincia de Tungurahua. Uniandes EPISTEME. Revista digital de Ciencia, Tecnología e Innovación, 7(2), 177-192.

Madhusudhan, H. S., Gupta, P. (2024). Federated learning inspired antlion based orchestration for edge computing environment. PLOS ONE, 19(6). https://doi.org/10.1371/journal.pone.0304067

Noga, T. (2023). The use of Chatbots and voicebots by public institutions in the communication process with clients. Scientific Papers of Silesian University of Technology. Organization and Management Series, 2023(174), 69–79. https://doi.org/10.29119/1641-3466.2023.174.6

Ouf, S., Mahmoud, K. T., Abdel-Fattah, M. A. (2024). A proposed hybrid framework to improve the accuracy of customer churn prediction in Telecom Industry. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-00922-9

Paolino, L., Lizcano, D., López, G., et al. (2019). A multiagent system prototype of a tacit knowledge management model to reduce Labor Incident Resolution Times. Applied Sciences, 9(24). https://doi.org/10.3390/app9245448

Paschen, J., Kietzmann, J., Kietzmann, T. C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing, 34(7), 1410–1419. https://doi.org/10.1108/JBIM-10-2018-0295

Pawlik, L., Plaza, M., Deniziak, S., et al. (2022). A method for improving bot effectiveness by recognising implicit customer intent in contact centre conversations. Speech Communication, 143, 33–45. https://doi.org/10.1016/j.specom.2022.07.003

Plaza, M., Kazała, R., Koruba, Z., et al. (2022). Emotion recognition method for call/contact centre systems. Applied Sciences, 12(21), 10951. https://doi.org/10.3390/app122110951

Plaza, M., Pawlik, L. (2021). Influence of the Contact Center Systems Development on Key Performance Indicators. IEEE Access, 9, 44580–44591. https://doi.org/10.1109/access.2021.3066801

Ribeiro, H., Barbosa, B., Moreira, A. C., et al. (2024). Customer experience, loyalty, and churn in bundled telecommunications services. Sage Open, 14(2). https://doi.org/10.1177/21582440241245191

Rohit, K., Shankar, A., Katiyar, G., et al. (2024). Consumer engagement in Chatbots and voicebots. A multiple-experiment approach in online retailing context. Journal of Retailing and Consumer Services, 78, 103728. https://doi.org/10.1016/j.jretconser.2024.103728

Ruiz-Rodríguez, V., López-Trujillo, A., Gamboa-Cruzado, J., et al. (2022). Aplicación de Sistemas Web para la Gestión de Pedidos en Restaurantes: Un Estudio de Caso. Revista Ibérica de Sistemas e Tecnologías de Informação, E54, 1–14.

Saputro, B., Ma’mun, S., Budi, I., et al. (2021). Customer churn factors detection in Indonesian postpaid telecommunication services as a supporting medium for preventing waste of IT components. IOP Conference Series: Earth and Environmental Science, 700(1). https://doi.org/10.1088/1755-1315/700/1/012015

Singh, P., Agrawal, R., Singh, K. K. (2023). Maximizing user retention with machine learning enabled 6G channel allocation. International Journal of Information Technology, 15(4), 2225–2231. https://doi.org/10.1007/s41870-023-01249-z

Srisushma, J., Vijaya, R. (2023). Simplifying banking services using voicebot. Journal of Population Therapeutics & Clinical Pharmacology, 30(17), 2087-2098. https://doi.org/10.53555/jptcp.v30i17.2938

Sudharsan, R., Ganesh, E. N. (2019). Churn rate prediction in Telecommunication Systems. International Journal of Engineering and Advanced Technology, 8(6), 4720–4725. https://doi.org/10.35940/ijeat.f9225.088619

Tapia-Guarnizo, J. L., Campoverde-Molina, M. A. (2019). Análisis de Gestión de incidencias de tecnologías de la información. Caso de estudio: Hospitales generales coordinación zonal 7 - salud. Polo del Conocimiento, 4(7), 119-148. https://doi.org/10.23857/pc.v4i7.1027

Ticona Gregorio, H. I. (2022). Aplicación de Lean Six sigma para mejorar El Subproceso de reparación de averías en enlaces de comunicaciones. Industrial Data, 25(1), 205–228. https://doi.org/10.15381/idata.v25i1.22194

Tran, D. C., Nguyen, D. L., Hassan, M. F. (2020). Development and testing of an FPT.AI-based voicebot. Bulletin of Electrical Engineering and Informatics, 9(6), 2388–2395. https://doi.org/10.11591/eei.v9i6.2620

Valenzuela Salazar, N. L., Buentello Martínez, C. P., Gomez, L. A., et al. (2019). La Atención Al Cliente, El Servicio, El Producto y el precio como variables determinantes de la satisfacción del cliente en Una Pyme de Servicios. Revista GEON (Gestión, Organizaciones y Negocios), 6(2), 18–24. https://doi.org/10.22579/23463910.159

Voelskow, V., Meßner, C., Kurth, T., et al. (2023). Prospective mixed-methods study evaluating the potential of a voicebot (CovBot) to relieve German health authorities during the COVID-19 infodemic. Digit Health, 9. https://doi.org/10.1177/20552076231180677

Waheed, M. A., Mannai, L. A., Khudadad, H., et al. (2024). Assessment of Qatar’s health care community call center efficacy in addressing COVID-19 pandemic health care challenges: Cross-sectional study. JMIR Formative Research, 8. https://doi.org/10.2196/42753

Zallman, L., McCarron, C., Silva, L. (2019). Implementation of a Navigation Center to Improve Patient Access. The Journal of Medical Practice Management, 35(2), 72-75.