4 agentic AI use cases for telco

4 agentic AI use cases for telco 4 cutting-edge use cases L3 customer support assistant for network operations Intelligent autonomous radio assistant for spectrum efficiency Agentic AI-powered customer experience analytics Autonomous intelligent network with event-driven automation The technology powering the transformation Beyond AI: The broader Red Hat portfolio ecosystem Red Hat as the AI platform partner at India Mobile Congress 2025 The adaptable enterprise: Why AI readiness is disruption readiness About the authors Atul Deshpande Rob McManus More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share It’s the peak traffic hour on a busy weekday evening. A common occurrence for a typical telecommunication (telco) service provider. Millions of subscribers are streaming videos, playing games, and making calls. Suddenly, a network outage occurs in a key metropolitan area. Level 3 (L3) operations team at the service provider would begin troubleshooting the incident. They would manually sift through logs across multiple domains (radio access network, core, transport) and race against time to restore services. Hours, sometimes days, could pass before the incident was fully resolved, leading to frustrated customers, lost revenue, and reputational damage. How different would the situation have been if artificial intelligence (AI) was involved? An agentic AI-driven network would detect anomalies in real time, identifying the root cause and resolving the issue autonomously before customers even noticed a problem. Operations teams would be able to move from a reactive posture to a proactive one, focusing instead on strategic innovation and customer experience. Building AI-driven autonomous intelligent networks The telco industry is at a pivotal moment. With 5G fully deployed, and 5G advanced and 6G on the horizon, networks are more complex and data-intensive than ever. Manual approaches to network deployment and operations simply cannot keep up with the scale of current and future demand.