AI in telco – the catalyst for scaling digital business

AI in telco – the catalyst for scaling digital business The shift to agentic AI and zero-touch operations The Red Hat advantage: any model, any accelerator, anywhere Four AI use cases delivering immediate telco value 1. Autonomous networks: from automation to autonomy 2. Intent-driven energy and cost reduction 3. Hyper-personalized customer experience 4. Real-time vendor management & SLA governance Navigating the three core challenges of AI adoption About the author Beatriz Ortega More like this The nervous system gets a soul: why sovereign cloud is telco’s real second act Refactoring isn’t just technical—it’s an economic hedge Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share If you aren't currently architecting for AI, you are part of a rapidly dwindling minority. By 2026, the pivot is no longer optional: AI has moved from a peripheral tool to the primary engine for transforming digital businesses, slashing operational complexity and driving revenue growth. The industry is moving beyond passive chatbots toward agentic AI. While traditional AI provides insights, generative AI makes suggestions, now agentic AI provides action. In a practical telco context, this means autonomous agents capable of navigating complex workflows: identifying network bottlenecks, cross-referencing customer SLAs, and triggering resource re-allocations via TM Forum open APIs. This transition enables the zero-touch operations essential for efficiency, reliability and scale of 5G and edge networks. A major shortcoming of early adoptions is the creation of AI islands – disconnected models that solve niche problems but cannot communicate with each other. Red Hat advocates for a more modular and interconnected strategy using open standards and a mesh architecture, where specialized micro-agents communicate through universal protocols like the model context protocol (MCP) and agent-to-agent (A2A) frameworks.