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

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.