From Pilot to Scale: A Process-Oriented Explanation of Artificial Intelligence Scaling in Tunisian Telecommunications Firms
Authors
PhD Candidate in Management FSEGS, Tunisia (Tunisia)
Associate Professor of Marketing ISG Gabès, Tunisia (Tunisia)
Article Information
DOI: 10.51583/IJLTEMAS.2026.150900164
Subject Category: Artificial Intelligence
Volume/Issue: 15/9 | Page No: 3248-3269
Publication Timeline
Submitted: 2026-10-01
Accepted: 2026-10-06
Published: 2026-10-26
Abstract
Artificial intelligence (AI) initiatives may demonstrate technical feasibility during pilot experimentation without progressing toward sustained organizational deployment. Although existing research identifies AI implementation phases and organizational readiness factors, further understanding is needed of how implementation conditions become differently consequential when organizations attempt to move beyond bounded experimentation. This study examines the pilot-to-scaling transition in Tunisian telecommunications firms through a process-oriented and context-sensitive perspective. Drawing on 11 semi-structured interviews across three telecommunications operators, the study adopts a cross-sectional qualitative design and reconstructs participants' experiences retrospectively. Data were analyzed through an abductive approach informed by Gioia's data-structuring principles, with NVivo matrix coding used as a descriptive analytical aid. Five interconnected dimensions were identified: bounded experimentation and value proving; production exposure and technological interdependence; organizational capability mobilization; local institutional and resource embeddedness; and scaling legitimation and organizational sensemaking. Participants' accounts suggest that movement toward operational deployment makes existing technological and organizational interdependencies more visible and managerially consequential, while requiring sustained capability mobilization and continued organizational justification. The study interprets this transition as an innovation management threshold, understood as a sensitizing interpretive concept rather than a new implementation stage or an established causal mechanism. It further offers a phase-contingent interpretation of implementation conditions and examines how locally embedded institutional and resource conditions enter organizational scaling attempts. The findings highlight the importance of distinguishing pilot readiness from scaling readiness.
Keywords
Artificial intelligence scaling; AI implementation; pilot-to-scale transition; innovation management; telecommunications
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Downloads
References
1. Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS Quarterly, 45(3), 1433-1450. https://doi.org/10.25300/MISQ/2021/16274 [Google Scholar] [Crossref]
2. DiCicco-Bloom, B., & Crabtree, B. F. (2006). The qualitative research interview. Medical Education, 40(4), 314-321. https://doi.org/10.1111/j.1365-2929.2006.02418.x [Google Scholar] [Crossref]
3. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147-160. https://doi.org/10.2307/2095101 [Google Scholar] [Crossref]
4. Fichman, R. G. (2004). Going beyond the dominant paradigm for information technology innovation research: Emerging concepts and methods. Journal of the Association for Information Systems, 5(8), 314-355. https://doi.org/10.17705/1jais.00054 [Google Scholar] [Crossref]
5. Fichman, R. G., & Kemerer, C. F. (1999). The illusory diffusion of innovation: An examination of assimilation gaps. Information Systems Research, 10(3), 255-275. https://doi.org/10.1287/isre.10.3.255 [Google Scholar] [Crossref]
6. Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15-31. https://doi.org/10.1177/1094428112452151 [Google Scholar] [Crossref]
7. Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: Systematic review and recommendations. The Milbank Quarterly, 82(4), 581-629. https://doi.org/10.1111/j.0887-378X.2004.00325.x [Google Scholar] [Crossref]
8. 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 [Google Scholar] [Crossref]
9. Haefner, N., Parida, V., Gassmann, O., & Wincent, J. (2023). Implementing and scaling artificial intelligence: A review, framework, and research agenda. Technological Forecasting and Social Change, 197, 122878. https://doi.org/10.1016/j.techfore.2023.122878 [Google Scholar] [Crossref]
10. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. https://doi.org/10.1016/j.techfore.2020.120392 [Google Scholar] [Crossref]
11. Instance Nationale de Protection des Données Personnelles. (n.d.). Textes: Protection des données personnelles en Tunisie. Retrieved October 1, 2026, from https://www.inpdp.tn/Texte.html [Google Scholar] [Crossref]
12. Instance Nationale des Télécommunications. (2026). Décisions de l’Instance Nationale des Télécommunications. Retrieved October 1, 2026, from https://www.intt.tn/ [Google Scholar] [Crossref]
13. Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes—An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5-20. https://doi.org/10.1007/s12599-020-00676-7 [Google Scholar] [Crossref]
14. Kallio, H., Pietilä, A.-M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954-2965. https://doi.org/10.1111/jan.13031 [Google Scholar] [Crossref]
15. Langley, A., & Abdallah, C. (2011). Templates and turns in qualitative studies of strategy and management. In D. D. Bergh & D. J. Ketchen, Jr. (Eds.), Building methodological bridges (Research Methodology in Strategy and Management, Vol. 6, pp. 201-235). Bingley, England: Emerald Group Publishing. https://doi.org/10.1108/S1479-8387(2011)0000006007 [Google Scholar] [Crossref]
16. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage. [Google Scholar] [Crossref]
17. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434 [Google Scholar] [Crossref]
18. Myers, M. D. (2013). Qualitative research in business and management (2nd ed.). London, England: Sage. [Google Scholar] [Crossref]
19. Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management: Reinventing innovation management research in a digital world. MIS Quarterly, 41(1), 223-238. https://doi.org/10.25300/MISQ/2017/41:1.03 [Google Scholar] [Crossref]
20. Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544. https://doi.org/10.1007/s10488-013-0528-y [Google Scholar] [Crossref]
21. Ramadani, L., & Almaarif, A. (2022). Considering context in information systems research: Understanding the conditions of developing country scholarship. The Electronic Journal of Information Systems in Developing Countries, 88(1), e12200. https://doi.org/10.1002/isd2.12200 [Google Scholar] [Crossref]
22. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press. [Google Scholar] [Crossref]
23. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503-2511. https://mlanthology.org/neurips/2015/sculley2015neurips-hidden/ [Google Scholar] [Crossref]
24. Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22(2), 63-75. https://doi.org/10.3233/EFI-2004-22201 [Google Scholar] [Crossref]
25. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The processes of technological innovation. Lexington, MA: Lexington Books. [Google Scholar] [Crossref]
26. Tracy, S. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837-851. https://doi.org/10.1177/1077800410383121 [Google Scholar] [Crossref]
27. Vial, G., Cameron, A.-F., Giannelia, T., & Jiang, J. (2023). Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal, 33(3), 669-691. https://doi.org/10.1111/isj.12420 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Impact of AI Tools on Student Learning
- Repogent: An Autonomous Multi-Agent System for End-To-End Repository Maintenance
- Trust-Centric Federated Learning Using Blockchain-Enabled Smart Contracts: An Analysis of Data Privacy, Model Integrity, and Decentralized AI Governance
- Can AI Care? Rethinking Ethics, Clinical Integrity, and Professional Responsibility in the Digital Age of Mental Healthcare
- AI-Based Smart Irrigation System Using IoT and Machine Learning for Precision Agriculture
This work is licensed under CC BY 4.0