Artificial intelligence (AI) is rapidly converging with telecommunications infrastructure, promising to transform how networks are built, optimized, and monetized. Nowhere is this more evident than in the radio access network (RAN) – the crucial “last mile” that connects our devices to the digital world. At Mobile World Congress, Microsoft is sharing a strategic vision for AI in the RAN (AI-RAN) and intelligent edge computing. This vision centers on harnessing cloud and AI technologies to make telecom networks smarter, more efficient, and ready for new services. With decades of wireless research and a broad AI ecosystem spanning Azure to Copilot and Microsoft Foundry, Microsoft is partnering with the telecom industry to enable a new generation of AI-powered networks.
A New Era of AI-RAN: AI Meets the Radio Network
The concept of AI-RAN captures a threefold innovation in telecom networks, where AI and RAN technology intersect:
- AI for RAN: using advanced machine learning and AI algorithms to improve how RANs operate. By leveraging AI-based analytics and control, operators can dynamically optimize spectrum usage, network performance, and energy efficiency, leading to lower operational and capital expenditures. In practice, this means mobile networks that self-optimize – automatically adjusting parameters to reduce interference, enhance coverage, and cut power consumption without human intervention.
- AI on RAN: turning the RAN itself into a distributed AI computing engine. In this paradigm, the thousands of cell sites and edge data centers in a network can host AI inference workloads closer to end users. This intelligent edge approach allows telecom providers to offer new AI-driven services – from real-time translation to AR/VR and interactive gaming – with the ultra-low latency and data sovereignty that cloud alone cannot achieve.
- AI and RAN: creating a shared infrastructure where AI platforms and the RAN co-exist and collaborate. By co-locating AI resources with telecom network functions, operators unlock synergies like integrated sensing and communications (for example, using 5G cells as distributed sensors), and they can support “physical AI” use cases such as autonomous robots and smart factories at the edge. This convergence of AI and telecom infrastructure not only improves the network itself but also opens new revenue streams through innovative services delivered over 5G and future 6G networks.
Microsoft envisions AI-infused RANs that are more than just communication channels – they become intelligent platforms for innovation. For instance, in recent trials, Microsoft researchers demonstrated AI systems that detect radio interference in real time by turning a 5G base station into a wideband spectrum analyzer. Similarly, an AI-driven anomaly detection system can continuously learn a network’s normal behavior and spot irregularities before they cause outages, helping prevent failures and improve reliability. These examples illustrate how applying AI to RAN data can translate into more resilient networks and better user experiences.
Edge AI: Bringing Cloud Intelligence Closer
The push for edge AI in telecom is about extending the power of the cloud out to the network’s edge, closer to where data is generated and consumed. This is crucial for applications that demand instantaneous processing and response, or that must keep data local for privacy and security. In a traditional setup, complex AI models live in the cloud, and lightweight AI runs on devices. But many new scenarios – such as unmanned aerial vehicle, autonomous mobile robot, or industrial IoT – require a middle ground. The telecom network’s edge (for example, in 5G base stations or nearby edge data centers) can serve as that ideal “in-between” AI execution layer.
Edge AI offers several strategic advantages for operators and enterprises:
- Ultra-low latency: By processing data on edge servers just one “hop” away from end-users, critical applications (like autonomous driving or remote robotic control) can respond in milliseconds, far faster than sending data to distant cloud servers.
- Data sovereignty and privacy: Keeping sensitive data (such as video feeds, industrial sensor data, or health information) within local networks or on-premises helps meet regulatory and privacy requirements. AI at the edge can analyze data without that data ever leaving the telecom’s domain.
- Bandwidth optimization: By processing and filtering data locally, only the most important insights (or lightly compressed data) are sent to the cloud. This reduces backhaul traffic and lowers costs.
- Resilience and continuity: Edge AI systems can continue to operate even when connectivity to cloud is limited, ensuring critical services remain available.
In short, intelligent edge computing transforms telecom networks into platforms for innovation. A prime example is the concept of “physical AI” – where AI-driven services control physical devices in real time via the network. Imagine factory robots or autonomous drones connected to a 5G network: with edge AI, heavy computation (like computer vision or coordination algorithms) can run on nearby servers, leveraging GPUs at the base station or aggregation site. Microsoft’s research has shown that offloading robotics AI workloads from onboard devices to edge GPUs can improve response times dramatically – in one scenario, cutting inference latency from over a second on a device to under 100 milliseconds at the edge. This kind of performance boost can make previously impossible applications feasible, from real-time hazard detection in smart cities to advanced augmented reality experiences.
Unifying Cloud and Telecom through Microsoft’s AI Ecosystem
Achieving the AI-RAN and edge vision requires more than just ideas – it demands a cohesive platform that brings cloud technology into the heart of telecom networks. This is where Microsoft’s broad AI and cloud ecosystem plays a pivotal role.
Azure’s cloud platform provides the robust, scalable foundation. Telecom operators can run key network functions in Azure (such as 4G/5G core networks) and leverage Azure’s global infrastructure for high performance and elasticity. At the same time, Azure’s capabilities extend on-premises and to the edge via Azure Arc, enabling a single pane of glass for managing resources across public cloud, private data centers, and network edge sites. This means operators can deploy and manage AI models or applications on distributed RAN edge servers as easily as in the cloud – achieving “zero-touch” automation and unified operations across their entire network.
Microsoft’s AI platforms and tools further empower telecom innovation. With Azure Machine Learning and the new Microsoft Foundry platform, operators and partners can train, fine-tune, and deploy state-of-the-art AI models for their unique needs. In fact, Microsoft’s AI ecosystem includes thousands of advanced models – from the latest OpenAI GPT-5.2 and domain-specific models, to a vast catalog of open-source models from partners like Anthropic, Meta, and Mistral – all available through Foundry for use in custom solutions.
Likewise, Microsoft’s growing family of Copilot experiences and AI agent services can be harnessed to improve telecom operations and customer experiences. For example, the Network Operations Agent (NOA) Framework demonstrates how a service desk AI agent might assist network engineers by intelligently parsing through network alerts and suggesting fixes, while different agents could help automate customer support with industry-specific expertise. Under the hood, developers have access to powerful frameworks like the Semantic Kernel and Azure’s AI libraries to build their own telecom-focused AI applications and xApps (RAN applications) that run on cloud or edge infrastructure. Microsoft’s vision is to make developing AI-driven network solutions as seamless as any cloud application development – develop in Azure, deploy to the RAN.
Crucially, all these capabilities are grounded in an open, standards-based approach. Microsoft is working closely with the industry to support Open RAN standards and has collaborated with leading operators and vendors on initiatives like Project Janus – an open RAN programmability platform that exposes rich RAN telemetry and control to AI algorithms. By embracing open interfaces and partnering across the telecom ecosystem, Microsoft ensures that AI solutions can plug into existing networks and equipment regardless of vendor, protecting operators’ investments while extending their capabilities. Microsoft is also a founding member of the global AI-RAN Alliance, a cross-industry effort to accelerate AI-native RAN technologies and establish best practices for integrating AI into next-generation networks.
From Research to Reality: Innovation with Partners
Microsoft’s leadership in AI and cloud is backed by deep research and real-world experimentation. Microsoft Research has been pushing the boundaries of wireless networking for over 20 years. Today, that research is yielding dividends in the form of new telecom technologies:
- Microsoft’s researchers have constructed a live AI-RAN testbed network across two global innovation hubs. This 24/7 private 5G network – spanning more than 30 cloud-controlled cell sites on Microsoft’s Redmond (USA) and Cambridge (UK) campuses – serves as a blueprint for the future RAN. It is fully software-defined, cloud-managed, and open, allowing internal teams to develop and test advanced 5G/6G capabilities like AI-driven optimization, edge robotics, and healthcare applications in a real-world environment.
- Insights from these efforts are shared with the industry and academy, helping define 6G-era concepts such as real-time RAN intelligent control and AI-native RAN architectures. Microsoft’s research prototypes (including reference designs and proofs-of-concept) offer operators a head start in understanding how to implement AI in their networks – from intelligent resource allocation to network slicing and beyond.
- Collaboration is key: Microsoft works hand-in-hand with major communication service providers (CSPs), network equipment manufacturers, and startups to bring these innovations to production. Joint trials and proof-of-concepts have demonstrated use cases like interference detection, energy-efficient RAN automation, and near-real-time network anomaly detection in live networks. By co-innovating with the telecom community, Microsoft ensures that its AI solutions align with real operational needs and can be deployed in multivendor environments.
A Strategic Path Forward for the Telecom Industry
As the telecom sector looks to the future, the message is clear: AI and the network are no longer separate – they are becoming one and the same. Operators that embrace AI-powered RAN and edge computing stand to benefit from significant gains in efficiency and customer experience. They will be able to optimize network performance in ways not possible before, from squeezing more capacity out of spectrum to slashing energy usage during off-peak hours. At the same time, these intelligent networks can unlock new revenue opportunities by offering differentiated services – think of carriers providing AI-powered insights or automation services to enterprise customers, or delivering rich digital experiences (from cloud gaming to mixed reality) with quality guaranteed by AI-driven network slices.
Microsoft’s role is to serve as a platform and partner for this industry-wide transformation. By bringing its unparalleled cloud and AI ecosystem to the telecom domain, Microsoft is helping operators transform into hyperscale tech-driven enterprises. That means Azure infrastructure for carrier-grade reliability and scale, Azure ML and data platforms to train models on telecom data, Copilot and agent technologies to augment both network operations and customer-facing services, and the Foundry catalog of AI models and tools to jumpstart innovation. All of these building blocks are designed to work in a hybrid, open environment – spanning public and private clouds, the network core, and the far edge – so that AI can run wherever it creates the most value, even directly in the RAN.
The convergence of AI and telecom infrastructure is poised to define the next decade of networks. Microsoft’s strategic investments in AI-RAN and edge computing, combined with deep partnerships across the telecom ecosystem, position it as a key enabler of this transformation. As the industry gathers at MWC to discuss what’s next, Microsoft reaffirms its commitment to helping telecom operators and partners harness the power of AI, from the cloud to the intelligent edge, and to jointly create a future where networks aren’t just faster or more open – but truly smarter.
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