Nokia Expands Microsoft Partnership to Integrate Data Suite With Fabric for Network Automation

Nokia announced it is expanding its long-term partnership with Microsoft to accelerate network automation. The two companies will develop an intelligent, unified data infrastructure to support telecom operators in delivering faster and more reliable AI-driven operations. The collaboration combines Nokia Data Suite's ready-to-use telecom data products with Microsoft Fabric's unified analytics, governance, and AI capabilities to enable agent-based solutions across the network stack. Nokia shares rose more than 5% in US after-hours trading.

Nokia Data Suite provides pre-built, reusable telecom data products with unified data quality controls and telecom-specific semantic modeling. Microsoft Fabric provides a unified data platform that includes OneLake storage, analytics tools, and native AI applications such as Microsoft 365 Copilot, Foundry, and Power BI. The solution is designed for multi-vendor, cross-domain telecom environments and supports hybrid cloud, cloud infrastructure, and on-premises deployments, balancing regulatory requirements with AI and automation deployment.

The partnership builds on Nokia's existing relationship with Microsoft in cloud, data, AI, and cybersecurity to support development and go-to-market activities for telecom customers. The solution is now available and is being scaled for autonomous network use cases and customer deployments. Initial go-to-market cases include autonomous VoNR (Voice over New Radio) assurance for detecting and resolving service issues, with agents able to identify anomalies, perform root-cause analysis, and output remediation options, while providing observability at the network, service, and user levels.

Another set of capabilities uses AI and machine learning to map radio access network (RAN) user sessions to precise geographic locations, identify users experiencing degraded radio performance, and locate coverage or capacity hotspot areas. The capabilities analyze root causes of RAN issues by correlating user, network, and radio data, and provide remediation options to improve the experience of network slicing users and standard 4G/5G users. Use cases also include predictive maintenance and fault management, with agents analyzing historical and real-time data to predict network issues before they affect customers. AI-driven operations assistants can provide contextual insights, operational options, and automated workflow execution in complex cross-domain environments, enabling operators to achieve higher levels of network autonomy while maintaining human oversight.