Artificial Intelligence in Telecommunications by 2050: A Conceptual AI-CTE Foresight Framework

Prepared By Editor-in-Chief

International Journal of Innovative Solutions in Engineering is published semi-annually.

ISSN: 3029-3200

Preliminary Communication

DOI: https://doi.org/10.47960/3029-3200.2026.2.1.22
Igor Jurčić* ORCID profile of Igor Jurčić
*CAigor.jurcic@fsre.sum.ba

Full text:

Vol. 2 No. 1 Article 15

This article belongs to Vol. 2 No. 1, 2026

I. Jurčić, “Artificial Intelligence in Telecommunications by 2050: A Conceptual AI-CTE Foresight Framework,” International Journal of Innovative Solutions in Engineering, vol. 2, no. 1, pp. 22–38, Jan. 2026, doi: 10.47960/3029-3200.2026.2.1.22.

pages 22-38

Download a citation file:

Vol. 2 No. 1 Article 15Download

Preview and download a citation file in BibTex format that can be imported by citation management software, including Mendeley, EndNote, ProCite, RefWorks, and Reference Manager.

This article is archived in Zenodo

Zenodo Archive DOI: 10.5281/zenodo.18315907

Abstract

This paper extends the Comprehensive Techno-Economic (CTE) Model by introducing Artificial Intelligence (AI) as a cross-cutting dimension that will define telecom system performance by 2050. The proposed AI-CTE framework formalizes AI-driven impacts on the Technical, Business, and Environmental layers, emphasizing autonomous network operation, predictive resource orchestration, AI-native service generation, and compliance with emerging governance requirements. AI maturity indices, long-term telecom trend extrapolations (1990–2023), and system dynamics simulations are integrated to quantify effects on spectral efficiency, energy per bit, CAPEX/OPEX distribution, and revenue trajectories. Three scenarios—Incremental AI Adoption, Transformative AI Ecosystems, and Disruptive AI Dominance — are evaluated to capture uncertainty in AI evolution and regulatory alignment. New performance indicators, including an AI Resilience Score, an AI Governance Score, a Transparency Index, and an AI Dependency Index, are introduced to assess reliability, ethical compliance, and system sovereignty. The model indicates that AI-augmented intelligence, autonomy, and governance will supersede traditional infrastructure metrics as primary determinants of competitiveness by 2050.

Keywords

CTE Model, Future Trends, Telecommunications, Artificial Intelligence, Industry 5.0

ijise ID

15

URI

https://ijise.ba/article/15/

Publication Date

Jan. 21, 2026

References

  1. I. Jurčić and S. Gotovac, “A Comprehensive Techno-Economic Model for Fast and Reliable Analysis of the Telecom Operator Potentials”, Applied Sciences, vol. 12, no. 20, p. 10658, Oct. 2022, doi: https://doi.org/10.3390/app122010658.
  2. A. McAfee and E. Brynjolfsson, Machine, platform, crowd: harnessing our digital future, First published as a Norton paperback. New York London: W. W. Norton & Company, 2018.
  3. “2023 AI for Good Global Summit Snapshot Report”, AI for Good. Accessed: Dec. 08, 2025. [Online]. Available: https://aiforgood.itu.int/reports_publications/2023-ai-for-good-global-summit-snapshot-report/
  4. “Telco AI: State of the Market, Q4 2024 | GSMA Intelligence”. Accessed: Dec. 11, 2025. [Online]. Available: https://www.gsmaintelligence.com/research/telco-ai-state-of-the-market-q4-2024
  5. Computer Technologies Engineering Department, Al-Hadba University College, Mosul, Iraq, A. M. Ahmed, S. A. Majeed, and Y. S. Dawood, “A Survey of 6G Mobile Systems, Enabling Technologies, and Challenges”, ijeetc, pp. 1–21, 2023, doi: https://doi.org/10.18178/ijeetc.12.1.1-21.
  6. N. Omheni, H. Koubaa, and F. Zarai, “Artificial Intelligence for 5G and 6G Networks: A Taxonomy-Based Survey of Applications, Trends, and Challenges”, Technologies, vol. 13, no. 12, p. 559, Dec. 2025, doi: https://doi.org/10.3390/technologies13120559.
  7. “AI and the Next Era of Personalization: Customer Experience 2030 Report”, Accenture Strategy Insights, 2024, https://www.accenture.com.
  8. P. Singh, “AI-Driven Personalization in Telecom Customer Support: Enhancing User Experience and Loyalty”, SSRN Journal, 2025, doi: https://doi.org/10.2139/ssrn.5218986.
  9. A. Saxena, S. Pundir, R. Kalra, V. D. Vani, R. C. Madhav, and V. K. Singh, “AI Driven Cognitive Radio Networks for Spectrum Optimization”, in 6th International Conference on Contemporary Computing and Informatics (IC3I), Sept. 2023, pp. 2667–2673. doi: https://doi.org/10.1109/IC3I59117.2023.10397657.
  10. “Artificial intelligence in space”. Accessed: Dec. 08, 2025. [Online]. Available: https://www.esa.int/Enabling_Support/Preparing_for_the_Future/Discovery_and_Preparation/Artificial_intelligence_in_space
  11. “The economic potential of generative AI: The next productivity frontier”, McKinsey & Company, 2023. [Online]. Available: https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
  12. “Artificial intelligence”. [Online]. Available: https://www.oecd.org/en/topics/artificial-intelligence.html
  13. “Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems”, The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, 2019. [Online]. Available: https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v2.pdf
  14. J. D. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, 1st Edition. McGraw-Hill, 2000. Accessed: Dec. 09, 2025. [Online]. Available: https://www.mheducation.com/highered/product/business-dynamics-sterman.html
  15. J. W. Forrester, “System dynamics—the next fifty years”, System Dynamics Review, vol. 23, no. 2–3, pp. 359–370, June 2007, doi: https://doi.org/10.1002/sdr.381.
  16. A. Agarwal, “Standardizing Responsible AI in Telecom: Ensuring Fairness, Robustness and Trustworthiness”, Telecommunications, 2024. [Online]. Available: https://tec.gov.in/pdf/AI-ML/e-Telecommunications%20journal-Jan2024_250517_121135.pdf#page=17
  17. L. Shonhe, Q. Min, and R. Phuti, “Government AI readiness in the ESARBICA community : findings from the Oxford Insights AI Readiness Index 2022”, ESARBICA Journal: Journal of the Eastern and Southern Africa Regional Branch of the International Council on Archives, vol. 43, pp. 84–101, 2024, Accessed: Dec. 11, 2025. [Online]. Available: https://www.ajol.info/index.php/esarjo/article/view/290249
  18. H. Pennanen, T. Hänninen, O. Tervo, A. Tölli, and M. Latva-Aho, “6G: The Intelligent Network of Everything”, IEEE Access, vol. 13, pp. 1319–1421, 2025, doi: https://doi.org/10.1109/ACCESS.2024.3521579.
  19. V. Kulshrestha and Karan R. Jagdale, “Disruptive technology directions for 6G”, in Towards Wireless Heterogeneity in 6G Networks, 1st Edition., CRC Press, 2024, pp. 18–32. doi: https://doi.org/10.1201/9781003369028-2.
  20. E. Astaiza Hoyos, H. F. Bermúdez-Orozco, and J. A. Aldana-Gutierrez, “Towards 6G: A Review of Optical Transport Challenges for Intelligent and Autonomous Communications”, Computation, vol. 13, no. 12, p. 286, Dec. 2025, doi: https://doi.org/10.3390/computation13120286.
  21. P. Mandon, “Beyond the AI Divide: A Simple Approach to Identifying Global and Local Overperformers in AI Preparedness”. World Bank Group & Reproducible Research Repository, 2025. [Online]. Available: https://openknowledge.worldbank.org/server/api/core/bitstreams/82fbc048-b723-4818-bb91-9a4c8855daf1/content
  22. P. Botsinis et al., “New Access and Flexible Topologies in 6G: Architectural Implications”, in Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Antwerp, Belgium: IEEE, June 2024, pp. 682–687. doi: https://doi.org/10.1109/EuCNC/6GSummit60053.2024.10597028.
  23. Ph.D., Professor of CENTRUM Catolica Graduate Business School and Pontificia Universidad Católica del Perú, Urbanización Los Álamos de Monterrico, Jirón Daniel Alomía Robles 125, Santiago de Surco 15023, Lima, Peru, A. Florek-Paszkowska, A. Ujwary-Gil, and Ph.D., Hab., Professor of Institute of Economics, Polish Academy of Sciences, Nowy Swiat 72, 00-330 Warsaw, Poland, “The Digital-Sustainability Ecosystem: A conceptual framework for digital transformation and sustainable innovation”, JEMI, vol. 21, no. 2, pp. 116–137, 2025, doi: https://doi.org/10.7341/20252127.
  24. “Artificial Intelligence Risk Management Framework (AI RMF 1.0)”, National Institute of Standards and Technology, 2023. [Online]. Available: https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf
  25. “Understanding the EU AI Act: Requirements and Next Steps”, ISACA, 2024. [Online]. Available: https://www.isaca.org/-/media/files/isacadp/project/isaca/resources/white-papers/understanding-the-eu-ai-act_1024.pdf
  26. “Al Standards for Global Impact: From Governance to Action”, ITUPublications, 2025. [Online]. Available: https://www.itu.int/dms_pub/itu-t/opb/ai4g/T-AI4G-AI4GOOD-2025-4-PDF-E.pdf
  27. “TMT Predictions 2026: The AI gap narrows but persists”, Deloitte Insights. Accessed: Dec. 11, 2025. [Online]. Available: https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions.html
  28. S. F. Drampalou, D. Uzunidis, A. Vetsos, N. I. Miridakis, and P. Karkazis, “A User-Centric Perspective of 6G Networks: A Survey”, IEEE Access, vol. 12, pp. 190255–190294, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3516194.
  29. Y. Xiao et al., “Space-Air-Ground Integrated Wireless Networks for 6G: Basics, Key Technologies, and Future Trends”, IEEE Journal on Selected Areas in Communications, vol. 42, no. 12, pp. 3327–3354, Dec. 2024, doi: https://doi.org/10.1109/JSAC.2024.3492720.
  30. G. Fernandes, H. Fontes, and R. Campos, “Semantic Communications: the New Paradigm Behind Beyond 5G Technologies”, June 02, 2024, arXiv: arXiv:2406.00754. doi: https://doi.org/10.48550/arXiv.2406.00754.
  31. N. Omheni, H. Koubaa, and F. Zarai, “Artificial Intelligence for 5G and 6G Networks: A Taxonomy-Based Survey of Applications, Trends, and Challenges”, Technologies, vol. 13, no. 12, p. 559, Dec. 2025, doi: https://doi.org/10.3390/technologies13120559.
  32. S. B. Chetty et al., “Sovereign AI for 6G: Towards the Future of AI-Native Networks”, Sept. 08, 2025, arXiv: arXiv:2509.06700. doi: https://doi.org/10.48550/arXiv.2509.06700.
  33. S. Min and B. Kim, “AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model”, Digital, vol. 4, no. 4, pp. 947–970, Nov. 2024, doi: https://doi.org/10.3390/digital4040047.
  34. J. Lee, F. Solat, T. Y. Kim, and H. V. Poor, “Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core”, IEEE Commun. Surv. Tutorials, vol. 26, no. 3, pp. 2176–2212, 2024, doi: https://doi.org/10.1109/COMST.2024.3352910.
  35. A. Awad Abdellatif, A. Abo-Eleneen, A. Mohamed, A. Erbad, N. V. Navkar, and M. Guizani, “Intelligent-Slicing: An AI-Assisted Network Slicing Framework for 5G-and-Beyond Networks”, IEEE Trans. Netw. Serv. Manage., vol. 20, no. 2, pp. 1024–1039, June 2023, doi: https://doi.org/10.1109/TNSM.2023.3274236.
  36. J. H. Lee, R. Phaal, and S.-H. Lee, “An integrated service-device-technology roadmap for smart city development”, Technological Forecasting and Social Change, vol. 80, no. 2, pp. 286–306, Feb. 2013, doi: https://doi.org/10.1016/j.techfore.2012.09.020.
  37. S. K. Baloch, T. Abbas, S. Hussain, and S. Ahmed, “The Impact of Artificial Intelligence on Electrical Engineering: A Review of Current Applications and Future Prospects”, ABBDM, vol. 5, no. 3, pp. 95–121, Aug. 2025, doi: https://doi.org/10.62019/abts8k78.
  38. R. Khan, B. Zainab, A. A. Prince, M. Iftikhar, and A. Raza, “Artificial Intelligence and 6G Integration: Transforming the Digital Technology Landscape”, Spectrum of Engineering Sciences, pp. 717–737, June 2025, Accessed: Dec. 11, 2025. [Online]. Available: https://thesesjournal.com/index.php/1/article/view/502
  39. L. Zhang, Z. Shao, B. Chen, and J. Benitez, “Unraveling Generative AI Adoption in Enterprise Digital Platforms: The Effect of Institutional Pressures and the Moderating Role of Internal and External Environments”, IEEE Transactions on Engineering Management, vol. 72, pp. 335–348, 2025, doi: https://doi.org/10.1109/TEM.2024.3513773.
Post Views: 340

Analysis of OAuth 2.0 Vulnerabilities Arising from Weak Implementation Choices

Difference and Application of White-Box, Black-Box, and Grey-Box Testing

ISSN: 3029-3200

Reach out to us for more opportunities

If you have any inquiries, please use the information below to contact us.

Contact Us

All articles published in the International Journal of Innovative Solutions in Engineering (IJISE) are preserved in Zenodo for long-term archiving and global accessibility.

© 2026 by The Author(s). Licensee IJISE by Faculty of Mechanical Engineering, Computing and Electrical Engineering, University of Mostar. All articles are an open-access and distributed under the terms and conditions of the CC BY 4.0Creative Commons LicenseAttribution 4.0 International (CC BY 4.0)