Machine Learning en la Transformación Digital de las Telecomunicaciones: Aplicaciones y Desafíos

Abstract

Digital transformation has revolutionized the telecommunications industry, creating new challenges in process management and organization, as well as impacting network security. In this context, machine learning (ML) has emerged as a key technological solution. This article aims to investigate the influence of machine learning on the digital transformation of telecommunications, analyzing its primary applications and the challenges associated with its implementation. To this end, a narrative review of recent scientific literature was designed and supplemented with classic sources from databases such as Scopus, IEEE, Xplore, and ScienceDirect. The results reveal that Machine Learning significantly aids in improving predictive and optimization processes, network connectivity, and strengthening cybersecurity through real-time anomaly detection. However, the effective implementation of machine learning faces three major limitations: inadequate technological infrastructure, resistance to organizational change, and a shortage of specialized talent. The results demonstrate that machine learning is a key technology for digital transformation, but it depends on a combination of innovation, technological investment, and professional training.