Nokia and Microsoft have said they will combine Nokia Data Suite, which provides pre-built, reusable telecommunications data products, with Microsoft Fabric, the software giant’s unified data and analytics platform.
The joint offering is designed to provide AI agents with access to trusted, telco-specific data in minutes rather than the weeks that Nokia and Microsoft say can be required using traditional data integration approaches.
The management and understanding of diverse and often disparately structured telco data sets has been a key area for the industry as it seeks to benefit from Generative and Agentic AI advances. Indeed, some have seen the data layer as foundational to the success of telco endeavours, and critical to who “wins” in the ecosystem
The two companies are positioning the work as a foundation for more autonomous network operations, allowing AI agents to draw on network, service, subscriber and other enterprise data to identify problems, analyse their causes and recommend or execute remedial actions.
“Nokia and Microsoft are bringing the right data, at the right time, for the right reason to telecom operators worldwide,” said Vivek Jaiswal, Nokia’s senior vice president for Autonomous Networks.
“Together, we are helping networks evolve from static infrastructures into programmable, AI-native platforms.”
Nokia Data Suite provides what Nokia describes as “glass-box” data quality controls and telco-specific semantic models. The intention is to make the data used by AI systems more transparent and explainable, while allowing individual data products to be provisioned and reused across different applications.
The products can be combined with data held in Microsoft Fabric, including enterprise, IT and third-party sources. Microsoft Fabric provides OneLake storage alongside analytics and AI capabilities, including Power BI, Microsoft Foundry and Microsoft 365 Copilot.
The combination is intended to address one of the practical obstacles to deploying AI in network operations: getting data from multiple network domains and vendors into a form that AI systems can reliably use.
Nokia and Microsoft said the platform can support use cases including automated root-cause analysis and closed-loop operations, including in multi-vendor and cross-domain environments, as well as hybrid, cloud and on-premises deployments.
Initial use cases include autonomous VoNR assurance, in which AI agents can identify service anomalies, conduct root-cause analysis and recommend actions using network, service and subscriber observability.
Another application, dubbed geo-experience, correlates subscriber, network and radio-frequency data to identify locations experiencing degraded RAN performance and pinpoint coverage or capacity problems. AI agents can then analyse potential causes and recommend remedial action for both standard 4G/5G services and network-sliced users.
The companies are also targeting predictive maintenance and fault management, with agents analysing historical and real-time network data to identify potential problems before they affect customers.
The approach does not envisage removing humans from the operational loop. AI assistants are intended to provide network engineers with contextual information and recommended actions, while also being able to execute workflows within defined governance boundaries.
“Telecom providers are ready to move AI from experimentation into everyday network operations, but that requires trusted data, strong governance and platforms that can scale,” said Silvia Candiani, Microsoft’s corporate vice president for Worldwide Telco & Media.
The solution is available now, according to Nokia and Microsoft, with the companies planning to expand the range of autonomous network use cases and customer deployments.
The announcement extends an existing Nokia-Microsoft relationship spanning cloud, data, AI and cybersecurity. The new focus reflects a broader shift in the telecom AI market from experimenting with individual AI applications towards building the data, infrastructure and governance layers needed to operate AI agents across the network.
