• Multi-vendor, cross-domain solution integrates Nokia Data Suite with Microsoft Fabric.
• Faster access to trusted, unified data allows telecom providers to scale autonomous, AI-driven operations.
• Initial use cases focus on RAN optimization through autonomous VoNR assurance and geo-experience, which correlates subscriber, network and RF data.
• Collaboration enables intelligent, agent-based solutions across the network stack.

Nokia is extending its longstanding partnership with Microsoft to accelerate network automation, announcing the development of an agentic, unified data foundation that supports faster, more reliable AI-driven operations for telecommunication providers.

The collaboration combines Nokia Data Suite’s ready-to-use telco data products with Microsoft Fabric’s unified analytics, governance and AI capabilities, enabling intelligent, agent-based solutions across the network stack. Operators will be able to access high-quality, trusted data in minutes instead of weeks, simplifying complex data integration and significantly reducing time to insight.

“Nokia and Microsoft are bringing the right data, at the right time, for the right reason to telecom operators worldwide,” said Vivek Jaiswal, Senior Vice President, Autonomous Networks at Nokia. “Together, we are helping networks evolve from static infrastructures into programmable, AI-native platforms.”

From fragmented data to scalable autonomy

Nokia Data Suite provides prebuilt, reusable telco data products with glass-box data quality controls and telco-specific semantic modeling, ensuring transparency, explainability and trust in AI-driven decisions. Microsoft Fabric delivers a unified data platform with OneLake storage, analytics tools and AI-native apps such as Microsoft 365 Copilot, Foundry and Power BI.

By integrating these capabilities, operators can move from fragmented data environments to a more autonomous, scalable model. Nokia’s data products can be provisioned on demand and seamlessly combined with Microsoft Fabric’s enterprise, IT and third-party data sources, enabling broader AI use cases such as automated root-cause analysis and closed-loop operations to improve service quality and efficiency.

The solution is designed for multi-vendor, cross-domain telecom environments and supports hybrid and cloud infrastructures, including on-premises deployments. This flexibility allows operators to meet regulatory requirements while advancing AI and automation strategies globally. It also addresses a key barrier to AI adoption in telecom networks by reducing the time required to prepare and operationalize data — a process that can take several weeks when performed by traditional methods.

“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, Corporate Vice President, Worldwide Telco & Media at Microsoft. “By bringing together Nokia Data Suite and Microsoft Fabric, we are creating a faster path to turn complex network data into actionable intelligence.”

Strengthening a partnership in cloud, data, AI and cybersecurity

This collaboration builds on Nokia and Microsoft’s existing partnership in cloud, data, AI and cybersecurity, supporting development and go-to-market activities for telecom customers. The solution is available now, with ongoing expansion of autonomous network use cases and customer deployments. Initial go-to-market examples include:

• Autonomous VoNR (voice over new radio) assurance to detect and remedy service issues, where agents identify anomalies, run root cause analyses, and recommend actions with 360-degree observability (network, service and subscriber).
• Geo-experience, which uses AI and machine learning to map RAN (radio access network) subscriber sessions to precise geographic locations, identify users experiencing degraded radio performance and pinpoint coverage or capacity hotspots. By correlating subscriber, network and RF data, this capability uses agents to analyze the root causes of RAN issues and provides actionable recommendations to improve the experience of both network-sliced and standard 4G/5G users.
• Predictive maintenance and fault management, where agents analyze historical and real-time data to anticipate network issues before they impact customers.

Throughout the process, AI-driven operational assistants act as copilots for network engineers, providing contextual insights, recommended actions and automated workflow execution across complex, cross-domain environments. As a result, operators can achieve higher levels of network autonomy while maintaining human oversight, enabling AI to take on increasingly complex operational responsibilities within clearly defined governance boundaries.