Empowering the Current and Future Telecom Network: AI/ML Based Telecom Network Management and Analysis of Critical Factors

DOI

10.2991/978-94-6239-660-9_22How to use a DOI?

Keywords

Artificial Intelligence; Benefits; Critical Factors for AI/ML Adoption; DOI Theory; Issues and Challenges; Machine Learning; TAM; Telecom Network Management; TOE Framework

Abstract

The telecommunications sector is undergoing rapid transformation with the rise of 5G, IoT, NFV, and Cloud technologies. However, telecom network management remains largely manual, reactive, and fragmented, limiting cost efficiency and scalability. While Artificial Intelligence (AI) and Machine Learning (ML) promise to modernize this landscape through automation and predictive capabilities, the adoption of such technologies remains inconsistent and poorly understood, particularly within telecom organizations.

This research work intends to overcome a critical shortcoming in the prevailing literature which is characterized by insufficient empirical analysis of telecom-specific elements that impact the use of AI/ML technologies. The study incorporates comprehensive model based on three theoretical frameworks: the Technology-Organization-Environment (TOE) framework, the Diffusion of Innovation (DOI) theory, and the Technology Acceptance Model (TAM). By making use of well-structured cross-sectional survey that includes 198 professionals from major telecom operators and vendors, this research work examines the effects of twelve constructs on decision-making processes related to AI/ML adoption.

The findings indicate that constructs such as Compatibility, Relative Advantage, and Managerial Capability constitute the primary determinants influencing the adoption of AI/ML technologies in the management of telecommunications networks. Interestingly, conventional constructs of the Technology Acceptance Model, such as Perceived Usefulness and Perceived Ease of Use, were determined to lack statistical significance, necessitating a critical reassessment of their applicability within complex and infrastructure-heavy contexts. This investigation provides both theoretical and practical implications by enhancing pre-existing adoption frameworks tailored to the telecommunications sector and pinpointing actionable elements for industry participants. This research work can further be extended by including studies incorporating impact of Organization Size, Regulatory Policies and Vendor Ecosystem on the AIML Adoption strategies.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ashish Vaishya
PY  - 2026
DA  - 2026/04/29
TI  - Empowering the Current and Future Telecom Network: AI/ML Based Telecom Network Management and Analysis of Critical Factors
BT  - Proceedings of the International Conference on Management Research (ICMR 2025)
PB  - Atlantis Press
SP  - 444
EP  - 471
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-660-9_22
DO  - 10.2991/978-94-6239-660-9_22
ID  - Vaishya2026
ER  -