Artificial Intelligence as a Catalyst for Telecommunications Industry Transformation

Abstract

Artificial intelligence (AI) is rapidly reshaping the telecommunications industry by transforming network operations, customer engagement, security management, and digital service innovation. While AI adoption has accelerated significantly in recent years, the industry continues to face substantial organizational, technological, and regulatory barriers that constrain the realization of fully AI-native operating models. This thesis provides a comprehensive state-of-the-art analysis of AI transformation in the telecommunications sector, focusing on industry-wide adoption patterns, structural maturity differences, and the strategic challenges and opportunities associated with large-scale deployment. The study first establishes a theoretical and regulatory foundation by examining the functional role of AI in telecommunications, alongside the ethical and legal constraints imposed by data protection, privacy, and emerging AI governance frameworks. It then analyzes current global and European adoption trends across key operational domains, including network operations, customer experience, fraud detection, cybersecurity, and internal process automation. Particular attention is given to structural differences across operator types, ownership models, and market environments that shape the speed and scope of AI adoption. Building on these insights, the thesis synthesizes industry-wide AI capabilities and maturity using a multi-level analytical framework that highlights persistent asymmetries between advanced and experimental application domains. It further explores emerging strategic directions, including AI-driven B2B and platform-based offerings as well as the growing role of generative AI in customer service, content generation, and process automation. Finally, the thesis identifies key transformation barriers related to legacy infrastructures, fragmented data architectures, workforce capability gaps, governance limitations, and regulatory uncertainty. By integrating technological, organizational, and institutional perspectives, this thesis contributes to a structured understanding of how AI is reshaping the telecommunications industry and clarifies the conditions under which operators can transition from localized AI applications toward enterprise-wide, AI-enabled transformation.