Nokia has launched new agentic artificial intelligence (AI) capabilities to improve broadband network operations, customer experience and fibre deployment efficiency globally.
The president of Fixed Networks at Nokia, Sandy Motley, said this in a statement on Tuesday.
‘Agentic AI’ refers to AI systems that can act autonomously to pursue goals – not just answer questions, but plan, make decisions, use tools, and execute multi-step tasks with minimal human intervention.
Ms Motley noted that the AI-powered capabilities were integrated across Nokia’s Altiplano, Corteca and Broadband Easy platforms to help telecom operators tackle fibre and Wi-Fi challenges.
She said the solutions would support broadband network design, planning, deployment and operations, while improving operational efficiency and customer experience.
According to her, the capabilities are developed from insights gathered across more than 600 million broadband lines deployed globally.
Ms Motley said the telecom industry was projected to invest about $6.2 billion in agentic AI by 2030 as operators increasingly adopted autonomous and self-optimising network systems.
She explained that the AI agents would help operators reduce operational costs, improve helpdesk efficiency and proactively detect network faults before customers experience disruptions.
Ms Motley added that the system could increase first-contact help desk resolution rates above 50 per cent, qualify network incidents within five minutes, and reduce repeat field visits to homes and construction sites by 50 per cent.
She said the AI tools also supported technicians with text, voice and image-based guidance during installations, while computer vision technology validated work quality and built digital replicas of fibre-to-the-home (FTTH) networks.
“AI helps telecom operators retain customers, improve helpdesk and engineering efficiency, and connect more homes faster,” Ms Motley said.
Commenting, Partner and Principal Analyst at Appledore Research, Grant Lenahan, said Nokia’s approach aligned with critical industry requirements such as autonomous control systems, structured data models and open application programming interfaces (APIs).
Mr Lenahan noted that vendors with large-scale operational experience and deep domain expertise were better positioned to deliver reliable AI-driven network automation outcomes.