The swift adoption of Artificial Intelligence (AI) technologies is transforming call centre operations management, revolutionizing customer service and operational efficiency. This paper explores the impact of AI-driven tools, such as Speech-to-Text (STT) and Text-to-Speech (TTS) systems, on call centre performance and economic outcomes, focusing on the rapidly growing market in Uzbekistan. Drawing on qualitative interviews and quantitative market data, this research highlights how AI improves operational efficiency through customer conversation analysis, error tracking, and sentiment analysis. Additionally, the study provides a statistical forecast on market capture, showing potential growth trends for AI-based startups in Uzbekistan’s telecommunications, EdTech, retail, and other key sectors. With a Serviceable Obtainable Market (SOM) of 1% and a n optimistic growth outlook, this paper argues that AI tools offer considerable strategic value for businesses aiming to enhance customer satisfaction and optimize service operations.
The swift adoption of Artificial Intelligence (AI) technologies is transforming call centre operations management, revolutionizing customer service and operational efficiency. This paper explores the impact of AI-driven tools, such as Speech-to-Text (STT) and Text-to-Speech (TTS) systems, on call centre performance and economic outcomes, focusing on the rapidly growing market in Uzbekistan. Drawing on qualitative interviews and quantitative market data, this research highlights how AI improves operational efficiency through customer conversation analysis, error tracking, and sentiment analysis. Additionally, the study provides a statistical forecast on market capture, showing potential growth trends for AI-based startups in Uzbekistan’s telecommunications, EdTech, retail, and other key sectors. With a Serviceable Obtainable Market (SOM) of 1% and a n optimistic growth outlook, this paper argues that AI tools offer considerable strategic value for businesses aiming to enhance customer satisfaction and optimize service operations.
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Uktamov S.A. | magistrant | Toshkent davlat transport universiteti |
| 2 | Abduqayumova R.A. | PhD candidate | Westminster International University in Tashkent |
| № | Name of reference |
|---|---|
| 1 | S. Kawamura, K. Machida, K. Matsui, D. Sakamoto, and M. Ishii, “Utilization of artificial intelligence in call centers,” 2016. doi: 10.53829/ntr201605fa7 |
| 2 | L. Wang, N. Huang, Y. Hong, L. Liu, X. Guo, and G. Chen, “Voice-based AI in call center customer service: A natural field experiment,” Prod Oper Manag, vol. 32, no. 4, 2023, doi: 10.1111/poms.13953 |
| 3 | Z. Zhang, B. Li, and L. Liu, “The impact of AIbased conversational agent on the firms’ operational performance: Empirical evidence from a call center,” Applied Artificial Intelligence, vol. 37, no. 1, 2023, doi: 10.1080/08839514.2022.2157592 |
| 4 | B. Li and L. Liu, “Does Voice-based AI Improve Call Center Operational Performance? Event Study in a Working Telecommunication Company,” PACIS 2021 Proceedings, 2021 |
| 5 | H.-J. Kim, “Continuance Intention to Use AI Voicebot in Call Center,” JOURNAL OF THE KOREA CONTENTS ASSOCIATION, vol. 23, no. 12, 2023, doi: 10.5392/jkca.2023.23.12.405 |
| 6 | M. A. M. A. Daqar and A. K. A. Smoudy, “THE ROLE OF ARTIFICIAL INTELLIGENCE ON ENHANCING CUSTOMER EXPERIENCE,” International Review of Management and Marketing, vol. 9, no. 4, 2019, doi: 10.32479/irmm.8166 |
| 7 | Y. Xu, C. H. Shieh, P. van Esch, and I. L. Ling, “AI customer service: Task complexity, problem-solving ability, and usage intention,” Australasian Marketing Journal, vol. 28, no. 4, 2020, doi: 10.1016/j.ausmj.2020.03.005 |