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Paper ID:IJSREM64543

A Strategic Performance Analysis of AI-Based Chatbot Services in Digital Customer Support at Bharti Airtel Limited

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64543

Volume:

10 |

6 |

Jun 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64543

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Shloka Kumbalwar

Shloka Kumbalwar

Department of Business Management, SBJITMR, Nagpur

S.B. Jain Institute of Technology, Management and Research, Nagpur

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Prof. Prashant Sawarkar

Prof. Prashant Sawarkar

Department of Business Management, SBJITMR, Nagpur

S.B. Jain Institute of Technology, Management and Research, Nagpur

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Abstract

This study examines the strategic performance of AI-based chatbot services within the digital customer support framework of Bharti Airtel Limited. As telecommunication companies increasingly rely on automation to manage high volumes of customer interactions, evaluating the real-world effectiveness and user perception of these AI tools is essential. To assess this, primary data was gathered from active Airtel users through a structured questionnaire analysing usage patterns, satisfaction metrics, and areas for improvement. The empirical findings indicate a generally positive baseline of customer satisfaction, with the majority of users expressing contentment or neutral stances regarding their digital support experiences. However, detailed feedback highlights clear operational bottlenecks that must be resolved to optimize performance. Users emphasized a critical need to enhance response accuracy and reduce lag times, alongside demands for expanded menu options and features. Additionally, the feedback points to a necessary shift toward seamless transitions between automated chat and human agents, as well as minor refinements to the interface design. Ultimately, this paper bridges the gap between technological implementation and user reality, offering practical, strategic recommendations for Bharti Airtel to refine its AI support architecture, minimize conversational errors, and balance automated efficiency with genuine customer satisfaction.

KEYWORDS

AI Chatbots Customer Support Bharti Airtel Limited User Satisfaction Telecom Automation.

Cite This Article

Shloka Kumbalwar, Prof. Prashant Sawarkar, "A Strategic Performance Analysis of AI-Based Chatbot Services in Digital Customer Support at Bharti Airtel Limited," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-9, Jun 2026. https://doi.org/10.55041/IJSREM64543

Full Article/Conclusion

The integration of artificial intelligence into modern telecommunications has fundamentally reshaped how service providers manage customer relations. This study empirically evaluated the strategic role, operational performance, and user satisfaction associated with the AI-based chatbot services of Bharti Airtel Limited. By analyzing feedback from a diverse, digitally active demographic predominantly composed of students and salaried professionals, the research highlights the critical balance between technological efficiency and human expectations. Operationally, Airtel’s chatbot achieves its primary objective of high responsiveness. An overwhelming 81.6% of respondents classified the system's response speed as fast, with 63.3% rating it as extremely quickly. This immediate availability effectively eliminates traditional customer service wait times, providing a swift entry point for subscribers seeking support. Furthermore, this rapid engagement translates directly into corporate efficiency and high user approval, as evidenced by the 76.6% of users who reported being satisfied or highly satisfied with their overall chatbot experience. This strong positive sentiment successfully led to the rejection of the null hypothesis, confirming that the chatbot serves as a highly significant and effective digital support tool for the vast majority of consumers. However, the research also uncovers vital areas where technological refinement is necessary. While the chatbot excels at routine, transactional tasks like balance inquiries, its utility is limited when faced with intricate customer grievances. This is clearly reflected in user feedback, where a substantial 33.33% of respondents prioritized the need for improved response accuracy, and another 20.00% called for a more seamless handover mechanism to human agents. These findings indicate that while speed is highly appreciated, it cannot completely substitute for conversational depth and precise contextual understanding. In conclusion, Bharti Airtel’s AI chatbot has proven to be a highly successful strategic asset in optimizing routine digital customer support and driving high baseline satisfaction. To bridge the remaining gap between basic automated efficiency and premium customer care, the next evolutionary step for Airtel lies in enhancing the chatbot's natural language processing capabilities and establishing a frictionless fallback system to human support. By addressing these specific operational bottlenecks, Airtel can ensure that its drive toward digital automation continues to strengthen long-term customer trust and brand loyalty.

References

[1]      Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445. [2]      Alghizzawi, M., Almagharbeh, M., & Al-Ramahi, A. (2025). AI chatbots improving customer-brand engagement through responsiveness, personalization, and perceived informativeness. Journal of Digital Marketing & Engagement, 14(1), 88–104. [3]      Almagharbeh, M. (2024). Role of AI chatbots in enhancing customer satisfaction and customer loyalty in emerging markets. International Journal of Customer Relationship Marketing and Management, 15(3), 1–19. [4]      Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users' satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, Article 101473. https://doi.org/10.1016/j.tele.2020.101473 [5]      Butt, A., & Ahmed, R. (2023). AI chatbot innovation: Leading toward consumer satisfaction, electronic word of mouth and continuous intention. Journal of Telecommunications and the Digital Economy, 11(4), 142–161. https://doi.org/10.18080/jtde.v11n4.735 [6]      Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552–568. https://doi.org/10.1016/j.jbusres.2022.03.023 [7]   Kawadkar, Hemraj. 2026. “AI Adoption in Accounting and Finance: Impact on Organizational Performance”. Asian Journal of Economics, Business and Accounting 26 (1):421-28. https://doi.org/10.9734/ajeba/2026/v26i12151. [8]      Cheng, Y., & Jiang, H. (2022). How do AI-driven chatbots impact customer experience? Evaluating the roles of speed, accuracy, and interactivity. Journal of Interactive Advertising, 22(2), 115–131. [9]      Hussain, S., & Khan, M. A. (2025). The balancing act of automation: Analyzing functional efficiency versus conversational depth in corporate chatbots. International Journal of Information Management, 72, Article 102610. [10]  Khan, S., Singh, G., & Singh, N. (2023). Turning wait time into real-time: The role of mobile customer care interfaces in transforming telecom subscriber behavior. Indian Journal of Marketing Analytics, 11(2), 45–58.

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Publication Date

6/5/2026

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