• Nokia agentic AI boosts end-user experience, increases operational efficiency and accelerates deployment for home and broadband networks.
• AI you can trust, built on insights and experience from 600+ million broadband lines deployed.
• Open and secure AI agent approach gives telecom providers full strategic control to integrate their own AI tools and data sources.

Nokia  announces new agentic AI capabilities for its fixed network product lines to help drive productivity and operational intelligence across home and broadband networks. Drawing on the expertise from 600+ million broadband lines deployed, Nokia’s agentic AI capabilities help telecom providers tackle fiber and Wi-Fi challenges, from design and planning to rollout and operations. Designed for the cognitive broadband era, Nokia’s AI-enabled fixed networks portfolio boosts end-user experience, increases operational efficiency and accelerates fiber rollout.

The telecom industry is set to invest $6.2 billion in agentic AI by 2030. Agentic AI systems capable of autonomous reasoning and decision-making will be a key driver of the cognitive broadband era, enabling networks to move beyond basic connectivity toward self-optimizing, AI-driven infrastructures.

Nokia embeds AI agents and natural language interaction across its Altiplano, Corteca, and Broadband Easy platforms, enabling telecom providers to modernize operations and reduce costs. Operators can resolve problems proactively, scale operations without adding headcount, and diagnose network issues using automated root cause analysis. The AI agents will make an immediate and tangible difference for operators, including lifting first-contact helpdesk resolution rates above 50%, network incident qualification within 5 minutes, and a 50% reduction in return visits to construction sites and connected homes.

Underpinning this is an open and secure approach that integrates AI agents, live data, and external services while ensuring compliance, data sovereignty, and vendor independence. Operators retain full control and can work with an LLM that best fits the specific use case, use their own interfaces, or connect data sources as they scale AI across their business.

Nokia’s new AI capabilities span the full broadband network lifecycle. They increase productivity across customer care, network engineering & operations and field force teams, while boosting end-user experience:

• An AI assistant with a conversational interface gives technicians and support teams instant access to product knowledge, accelerating training and day-to-day problem solving.
• AI-powered text, voice, and image guidance assists field technicians during surveys and installations, and computer vision technology helps validate the quality of work done and build a live digital twin of the FTTH network.
• Automated diagnostics detect degradations and prevent outages; giving frontline support teams more operational precision and analytical depth.
• A troubleshooting agent improves root cause analysis and speeds up remediation across home and access networks, and uses advanced reasoning to pinpoint faults faster, reduce ticket volume, and increase first-call resolution rates.

“AI makes your end-users less likely to churn, your engineering and helpdesk teams more productive, and your field teams connect more homes more quickly. Nokia’s Agentic AI puts 600+ million lines worth of broadband experience at the fingertips of every field technician, helpdesk agent, and network engineer – and solves problems before the customer is even aware. We’re fundamentally changing how home and broadband networks are deployed and run,” said Sandy Motley, President Fixed Networks, Nokia.

“AI only works with quality data, and when data is AI-ready. Our recent market outlook on AI in network automation underscored that the industry is rapidly moving to build infrastructure capable of enabling powerful, successful, AI. Vendors like Nokia that combine deep domain expertise with real-world scale are best positioned to deliver reliable outcomes. Nokia’s approach reflects many of the right architectural principles, including autonomous control loops, structured data models, and open APIs, which are critical to making automation easy and AI responses accurate,” said Grant Lenahan, Partner and Principal Analyst, Appledore Research.