Abstract

This work describes the challenges far from trivial that product managers in telco are facing, striving for payment ecosystems powered by AI. As telecoms morph into fintech’s, AI and old fangled payment systems have their very own set of unique challenges – ranging from ensuring interoperability and winning user trust to crunching numbers in all but real-time. This paper is based on a mixed-method study that uses data from 458 operating cases of a Tier-1 telecom company. Python is used for preliminary filtering and statistical classification, while Tableau is utilized to visualise the complex multidimensional relationships between system latency and fraud detection accuracy. The tension between stability (linguistic and algorithmic) and dynamic, linguistic decision-making is the subject of investigation. Key findings include: The primary bottleneck is not model sophistication but the architectural constraints of current telecom cores, which were not designed to accommodate high-frequency, low-latency financial transactions. The paper ends with the suggestion that for product management to succeed in this space, feature-centric roadmaps should be replaced with platform-centric strategies that favour architectural modularity and explainable AI as a strategy to retain user trust.

References

• R. E. Balmer, S. L. Levin, and S. Schmidt, “Artificial intelligence applications in telecommunications and other network industries,” Telecommunications Policy, vol. 44, no. 6, Art. no. 101977, 2020.
• X. Guibao, M. Yubo, and L. Jialiang, “The impact of artificial intelligence on communication networks and services,” ITU Journal, vol. 1, no. 1, pp. 33–38, 2018.
• G. Abuselidze and L. Mamaladze, “The impact of artificial intelligence on employment before and during pandemic: A comparative analysis,” Journal of Physics: Conference Series, vol. 1840, no. 1, Art. no. 012040, 2021.
• G. Damioli, V. Van Roy, and D. Vertesy, “The impact of artificial intelligence on labour productivity,” Eurasian Business Review, vol. 11, no. 1, pp. 1–25, 2021.
• A. Braganza, W. Chen, A. Canhoto, and S. Sap, “Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust,” Journal of Business Research, vol. 131, pp. 485–494, 2021.
• U. Lichtenthaler, “Extremes of acceptance: Employee attitudes toward artificial intelligence,” Journal of Business Strategy, vol. 41, no. 5, pp. 39–45, 2020.
• D. Chen, J. P. Esperança, and S. Wang, “The impact of artificial intelligence on firm performance: An application of the resource-based view to e-commerce firms,” Frontiers in Psychology, vol. 13, Art. no. 884830, 2022.
• F. Olan, E. Ogiemwonyi Arakpogun, J. Suklan, F. Nakpodia, N. Damij, and U. Jayawickrama, “Artificial intelligence and knowledge sharing: Contributing factors to organizational performance,” Journal of Business Research, vol. 145, pp. 605–615, 2022.
• V. Pereira, E. Hadjielias, M. Christofi, and D. Vrontis, “A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective,” Human Resource Management Review, vol. 33, no. 1, Art. no. 100857, 2023.
• P. Mikalef and M. Gupta, “Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance,” Information & Management, vol. 58, no. 3, Art. no. 103434, 2021.
• P. Mikalef et al., “Examining how AI capabilities can foster organizational performance in public organizations,” Government Information Quarterly, vol. 40, no. 2, Art. no. 101797, 2023.
• H. Al Naqbi, Z. Bahroun, and V. Ahmed, “Enhancing work productivity through generative artificial intelligence: A comprehensive literature review,” Sustainability, vol. 16, no. 3, p. 1166, 2024.
• P. Wang and H. Ding, “Understanding the impact of explainable artificial intelligence on human-AI trust and decision performance,” Information Processing & Management, vol. 61, no. 4, Art. no. 103732, 2024.