An Exploratory Study on AI Adoption Challenges in A Telecom Organization

  • Authors

    https://doi.org/10.14419/qtb53h86

    Received date: June 25, 2025

    Accepted date: August 1, 2025

    Published date: August 4, 2025

  • Adoption of AI; Customer Experience; Digital Literacy; Regulatory; Telecom

  • Abstract

    This is an empirical study conducted in two large organizations within the ‎telecommunication sector. One of the organizations is providing telecom ‎wireless services, and the other is in the manufacture of various devices such as ‎handphones, set-top-boxes, telecommunication tower cores, routers, and the like. ‎The study has used a qualitative research method, namely the grounded theory ‎method by Corbin and Strauss. Semi-structured interviews were conducted with ‎the leadership team members who decided to adopt AI technologies, and a focus ‎group discussion (FGD) with the middle management team who were responsible ‎for implementation. The respondents in the FGD included data engineers, data ‎scientists, software developers, and end users. The organisations have ‎implemented more than 150 AI applications. The key finding is that ‎organisations need to be focused on data readiness, leadership drive, digital ‎literacy across the workforce, technology infrastructure, overcoming resistance ‎to change, and regulatory norms compliance. The study provides insight into ‎how the barriers were overcome, and the outcome was growth in customer base ‎and average revenue per user. The study can be expanded to include ‎organisations within the domain of manufacturing and the service sector.

  • References

    1. Peter, N., & Intelligence, R. S. A. (2021). A modern approach. Pearson Education.
    2. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company. https://doi.org/10.1080/15228053.2014.943094.
    3. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. HBR’s 10 Must Reads, 67.
    4. Bessen, J. (2018). AI and jobs: The role of demand (No. w24235). National Bureau of Economic Research. https://doi.org/10.3386/w24235.
    5. Brock, J. K. U., & von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelli-gence. California Management Review, 61(4), 110–134. https://doi.org/10.1177/1536504219865226.
    6. Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018). Skill shift: Automation and the future of the workforce. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/future-of-work/skill-shift-automation-and-the-future-of-the-workforce. Down-loaded on 5th May 2025.
    7. Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Jour-nal of Information Management, 68, 102588. https://doi.org/10.1016/j.ijinfomgt.2022.102588.
    8. Ansari, S. F., & Gunta, S. K. (2025). Augmented analytics for democratizing data insights. https://doi.org/10.17148/IARJSET.2025.12451.
    9. Cao, G., Duan, Y., & Edwards, J. S. (2025). Organisational culture, digital transformation, and product innovation. Information & Management, 62(4), 104135. https://doi.org/10.1016/j.im.2025.104135.
    10. Adnan, M. (2025). Identifying and prioritising artificial intelligence use cases for business value creation. https://adnanmasood/identifying-and-prioritizing-artificial-intelligence-use-cases-for-business-value-creation-1042af6c4f93. Downloaded on 5th May 2025.
    11. NVIDIA. (2024). State of AI in telecommunications: 2024 trends. https://resources.nvidia.com/en-us-ai-in-telco/state-of-ai-in-telco-2024-report. Downloaded on 3rd May 202.
    12. Pandita, D., & Ray, S. (2018). Talent management and employee engagement – A meta-analysis of their impact on talent retention. Industrial and Commercial Training, 50(4), 185–199. https://doi.org/10.1108/ICT-09-2017-0073.
    13. van der Vlist, F., Helmond, A., & Ferrari, F. (2024). Big AI: Cloud infrastructure dependence and the industrialization of artificial intelligence. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241232630.
    14. Enemosah, A. (2025). Enhancing DevOps efficiency through AI-driven predictive models for continuous integration and deployment pipelines. Inter-national Journal of Research Publication and Reviews, 6(1), 871–887. https://doi.org/10.55248/gengpi.6.0125.0229.
    15. Yanamala, A. K. Y., & Suryadevara, S. (2024). Navigating data protection challenges in the era of artificial intelligence: A comprehensive review. Re-vista de Inteligencia Artificial en Medicina, 15(1), 113–146. https://redcrevistas.com/index.php/Revista. Downloaded on 20th May 2025.
    16. Collins, A., Hamza, O., Eweje, A., & Babatunde, G. O. (2024). Challenges and solutions in data governance and privacy: A conceptual model for telecom and business intelligence systems. https://doi.org/10.54660/.IJMRGE.2024.5.1.1064-1081.
    17. Strauss, A., & Corbin, J. (1990). Basics of qualitative research (Vol. 15). Sage Publications.
    18. Patton, M. Q. (2014). Qualitative research and evaluation methods: Integrating theory and practice. Sage Publications.
    19. Kvale, S., & Brinkmann, S. (2009). Interviews: Learning the craft of qualitative research interviewing (2nd ed.). Sage Publications.
    20. Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903.
    21. Charmaz, K. (2014). Constructing grounded theory (2nd ed.). Sage Publications.
    22. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.
    23. Alessi, D. (2024). Tech transformations: A qualitative exploration of data analytics integration in technology-driven enterprises (bachelor’s thesis). Haaga-Helia University of Applied Sciences. Downloaded on 2nd May 2025.
    24. Chandra, C. M. B. (2024). From silos to synergy: Delivering unified data insights across disparate business units. International Journal of Innovative Research in Computer and Communication Engineering, 12(11). https://doi.org/10.15680/IJIRCCE.2024.1211002.
    25. Ali, O., Murray, P., Al-Ahmad, A., & Tahat, L. (2024). An integrated framework for addressing the challenges and strategies of technology adoption: A systematic review. Emerging Science Journal, 8(3), 1215–1242. https://doi.org/10.28991/ESJ-2024-08-03-025.
    26. Potla, R. T. (2022). Scalable machine learning algorithms for big data analytics: Challenges and opportunities. Journal of Artificial Intelligence Re-search, 2(2), 124–141. Accessed on 3rd May 2025.
    27. Maddula, S. S. (2018). The impact of AI and reciprocal symmetry on organizational culture and leadership in the digital economy. Engineering Inter-national, 6(2), 201–210. https://doi.org/10.18034/ei.v6i2.703.
    28. Ekwueme, F., Areji, A., & Ugwu, A. (2023). Beyond the fear of artificial intelligence and loss of job: A case for productivity and efficiency. Qeios. https://doi.org/10.32388/3BWNXG.
    29. Yusuf, S. O., Abubakar, J. E., Durodola, R. L., Ocran, G., Paul-Adeleye, A. H., & Yusuf, P. O. (2024). Impact of AI on continuous learning and skill development in the workplace: A comparative study with traditional methods. https://doi.org/10.30574/wjarr.2024.23.2.2439.
    30. Kinkel, S., Baumgartner, M., & Cherubini, E. (2022). Prerequisites for the adoption of AI technologies in manufacturing–Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375. https://doi.org/10.1016/j.technovation.2021.102375.
    31. Braun, G., Rikala, P., Järvinen, M., Hämäläinen, R., & Stahre, J. (2024). Bridging skill gaps–A systematic literature review of strategies for industry. Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning, 687–696. https://doi.org/10.3233/ATDE240209.
    32. Santra, S., Kukreja, P., Saxena, K., Gandhi, S., & Singh, O. V. (2024). Navigating regulatory and policy challenges for AI-enabled combination de-vices. Frontiers in Medical Technology, 6, 1473350. https://doi.org/10.3389/fmedt.2024.1473350.
    33. Sargiotis, D. (2024). Ethical AI in information technology: Navigating bias, privacy, transparency, and accountability. Advanced Machine Learning and Artificial Intelligence, 5(3), 1–14. https://doi.org/10.33140/AMLAI.05.03.03.

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  • How to Cite

    Sohoni, S., Pawar, P., & Raut, U. (2025). An Exploratory Study on AI Adoption Challenges in A Telecom Organization. International Journal of Basic and Applied Sciences, 14(4), 123-129. https://doi.org/10.14419/qtb53h86