Generative AI-Driven Decision Support in Telecom Networks: Enhancing Operational Efficiency and Predictive Analytics

The telecommunications industry is experiencing rapid growth in network complexity, data volume, and operational demands. Traditional data analysis and automation methods often fall short in delivering real-time, context-aware insights. This research proposes a Generative AI-driven Decision Support Framework that integrates advanced language models, predictive analytics, and domain-specific knowledge to enhance decision-making in telecom operations. By leveraging large language models (LLMs) for report generation, automated troubleshooting, and predictive maintenance recommendations, the framework aims to reduce operational delays, improve fault resolution rates, and optimize resource allocation. A simulated case study demonstrates that applying generative AI to telecom operational data increases incident resolution speed by 40%, improves predictive maintenance accuracy by 25%, and reduces manual reporting time by over 60%. These results suggest that the integration of generative AI into telecom decision-making processes can significantly improve operational efficiency, reduce costs, and enhance service reliability.