By Claudio Righetti, Austral University; Matías Torchinsky, Intraway
Artificial intelligence (AI) has emerged as a transformative force across industries, reshaping operations, enhancing efficiencies, and driving innovation. Generative AI (GenAI) stands out in this landscape for its unique ability to autonomously create content, generate insights, and optimize processes. This technological advancement represents not only a paradigm shift but also a significant opportunity for any industry; the telecommunications industry is not the exception.
Communication Service Providers (CSPs) operate in a dynamic environment where competition is fierce, consumer expectations are rising, and margins are becoming increasingly tight, so operational costs are in the spotlight and need continual optimization. GenAI offers CSPs more than just a tool for innovation—it presents a strategic avenue to reduce operational expenditures (OPEX), accelerate operational and business processes and help to create new revenue streams. By harnessing GenAI technologies effectively, CSPs can automate routine tasks, personalize customer interactions, predict consumer behavior, and optimize network management, among other applications.
The adoption of GenAI is not merely about integrating new technology; it represents a fundamental shift towards more agile and data-driven business models. Through intelligent automation and predictive analytics, CSPs can streamline operations, enhance (even rethink) their product services (Fraud/Assurance), optimize their internal processes (reduce costs), and discover new revenue streams, which leads to sustainable growth in the digital era.
Currently, AI, particularly GenAI, is at the peak of expectations. This has sparked a race in academia and industry to develop new large language models (LLM), various applications, and debates on the need for models to be open source. This technical paper presents a framework for GenAI, outlining its current scope and limitations. From this foundation, we will explore GenAI’s current applications, the distinctions between closed-source and open-source alternatives, and its limitations. Specifically, we explore the strategic deployment of GenAI within the cable industry in Latin America.