Evaluating the Potential of Artificial Intelligence in Improving Business Intelligence in Lusaka: A Case Study of Airtel Zambia
DOI:
https://doi.org/10.59413/ajocs/v7.i5.11Keywords:
Artificial Intelligence, Business Intelligence, Customer Service, AI Chatbot, Personalisation, Airtel Zambia, Telecommunications, Organisational ReadinessAbstract
Artificial Intelligence (AI) is increasingly being integrated into customer-service and Business Intelligence (BI) environments, but evidence on the readiness and customer-service implications of such integration in Zambia remains limited. This study evaluated the potential of AI to improve BI for customer service at Airtel Zambia in Lusaka. The study was guided by the Technology Acceptance Model, the Technology-Organisation-Environment framework and the DeLone and McLean Information Systems Success Model. A mixed-method case-study design was adopted. Quantitative data were obtained from 52 Airtel subscribers through a structured questionnaire, while contextual evidence was obtained from one Airtel Zambia IT Director. The achieved subscriber sample represented 52.5% of the intended 99 respondents. Descriptive statistics were used for subscriber responses, while the key-informant response was analysed thematically and compared with subscriber evidence. The findings showed that subscribers viewed the accuracy of customer-service information and the usability of Airtel's digital platforms positively, but perceptions were weaker regarding the use of previous interactions and personalised recommendations. Support for AI-enabled service was stronger: 78.4% of valid respondents preferred WhatsApp communication, 78.4% would use an AI chatbot if it provided instant responses and 81.6% wanted usage-based bundle recommendations. The IT Director confirmed the use of BI systems and data analytics in customer-service decisions, while reporting that infrastructure and AI-related expertise were only partially capable of supporting AI. Management support and funding were reported as available and overall organizational readiness was rated as ready. The study concludes that Airtel Zambia has a meaningful foundation for AI-enabled customer service, but implementation should be phased and accompanied by stronger infrastructure, AI skills, data governance, human escalation and customer-visible personalisation.
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References
Airtel Africa plc. (2025). Annual report and accounts 2025. Airtel Africa plc.
Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), Artificial Intelligence Applications and Innovations (pp. 373–383). Springer. DOI: https://doi.org/10.1007/978-3-030-49186-4_31
Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051 DOI: https://doi.org/10.1016/j.chb.2018.03.051
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. DOI: https://doi.org/10.2307/41703503
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 DOI: https://doi.org/10.1080/07421222.2003.11045748
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data: Evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Følstad, A., & Skjuve, M. (2019). Chatbots for customer service: User experience and motivation. Proceedings of the 1st International Conference on Conversational User Interfaces, 1–9. https://doi.org/10.1145/3342775.3342784 DOI: https://doi.org/10.1145/3342775.3342784
GSMA. (2024). The mobile economy Sub-Saharan Africa 2024. GSMA.
Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9 DOI: https://doi.org/10.1007/s11747-020-00749-9
International Telecommunication Union. (2024). AI for good: Artificial intelligence and telecommunications. ITU.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420 DOI: https://doi.org/10.1509/jm.15.0420
Luger, E., & Sellen, A. (2016). Like having a really bad PA: The gulf between user expectation and experience of conversational agents. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 5286–5297. https://doi.org/10.1145/2858036.2858288 DOI: https://doi.org/10.1145/2858036.2858288
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2019). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16, 547–578. https://doi.org/10.1007/s10257-018-0397-6 DOI: https://doi.org/10.1007/s10257-017-0362-y
Ministry of Technology and Science. (2024). National artificial intelligence strategy 2024–2026. Government of the Republic of Zambia.
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1 DOI: https://doi.org/10.6028/NIST.AI.100-1
Nicolescu, L., & Tudorache, M. T. (2022). Human-computer interaction in customer service: The experience with AI chatbots: A systematic literature review. Electronics, 11(10), 1579. https://doi.org/10.3390/electronics11101579 DOI: https://doi.org/10.3390/electronics11101579
OECD. (2019). Recommendation of the Council on Artificial Intelligence. OECD.
Olujimi, P. A., & Ade-Ibijola, A. (2023). NLP techniques for automating responses to customer queries: A systematic review. Discover Artificial Intelligence, 3, 20. https://doi.org/10.1007/s44163-023-00065-5 DOI: https://doi.org/10.1007/s44163-023-00065-5
Pentina, I., Guilloux, V., & Milkovich, M. (2023). Consumer–machine relationships in the age of artificial intelligence: Systematic literature review and research directions. Psychology & Marketing, 40(8), 1593–1614. https://doi.org/10.1002/mar.21853 DOI: https://doi.org/10.1002/mar.21853
Republic of Zambia. (2021a). Data Protection Act No. 3 of 2021. Government Printer.
Republic of Zambia. (2021b). Electronic Communications and Transactions Act No. 4 of 2021. Government Printer.
Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics and data science: A managerial perspective (4th ed.). Pearson.
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO.
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022 DOI: https://doi.org/10.1016/j.jbusres.2019.09.022
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119 DOI: https://doi.org/10.1108/JOSM-04-2018-0119
Wixom, B. H., & Watson, H. J. (2010). The BI-based organization. International Journal of Business Intelligence Research, 1(1), 13–28. DOI: https://doi.org/10.4018/jbir.2010071702
Zambia Information and Communications Technology Authority. (2024). 2024 annual market report for the ICT sector. ZICTA.
Zambia Information and Communications Technology Authority. (2025). ICT statistics and market performance reports. ZICTA.
Zambia Statistics Agency. (2022). 2022 census of population and housing. Government of the Republic of Zambia.
Airtel Zambia. (2025). Annual report 2025. Airtel Networks Zambia PLC.
Atkinson, A., & Messy, F.-A. (2012). Measuring financial literacy: Results of the OECD/International Network on Financial Education pilot study. OECD Working Papers on Finance, Insurance and Private Pensions, No. 15.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. DOI: https://doi.org/10.2307/30036540
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Huang, M.-H., & Rust, R. T. (2020). Engaged to a robot? The role of AI in service. Journal of Service Research, 23(2), 155–172. DOI: https://doi.org/10.1177/1094670517752459
World Bank. (2021). World development report 2021: Data for better lives. World Bank.
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Copyright (c) 2026 Lucas Bubblegum Ngulube, Dr. Mukwalikuli Mundia (Author)
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