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AI-RAN combines openness and cloud-native infrastructure to create a more intelligent, programmable radio access network
The radio access network (RAN) is entering another major period of change, but this time the transition is not being driven by a single new architecture or technology. Open RAN, cloud-native infrastructure and artificial intelligence (AI) have largely developed along separate tracks. Increasingly, those tracks are converging around a shared, although varied, vision of an AI-RAN that’s more programmable, adaptive and capable of continuously optimizing itself around network, business and customer outcomes.
That convergence is the focus of the new RCRTech report, “Rethinking the RAN: Building an intelligent, programmable radio access network.” The report examines how the industry is moving from the foundations established by self-organizing networks, virtualization and disaggregation toward AI-RAN and, eventually, an AI-native 6G architecture. But the transition is not simply about putting more AI into the network.
For operators, the more immediate question is whether these technologies can produce measurable economic and operational outcomes. AI-RAN, for instance, has to demonstrate that new algorithms and accelerated computing can generate enough additional capacity from existing spectrum assets to justify changes in compute architecture, power consumption and cost. The report explores why spectral efficiency is emerging as one of the first important commercial tests for AI-RAN — and how the underlying architecture could evolve if those gains prove durable.
The report also examines the continuing role of Open RAN. Rather than judging openness simply by the number of vendors deployed in a network, the analysis considers its longer-term value as architectural optionality: the ability to introduce new radios, applications, compute platforms and algorithms without rebuilding the network around a single technology stack.
Real-world operator strategies show how these ideas are beginning to move from architecture diagrams into operating models. The report looks closely at TELUS’ brownfield Open RAN transformation, including how the operator has aligned infrastructure refresh, virtualization, interoperability and automation. It also examines T-Mobile US’ approach to AI-driven network operations, where traditional network KPIs are increasingly being connected with application performance, customer experience and progressively more autonomous decision-making.
The longer-term implications extend well beyond 5G.
As AI moves deeper into the radio stack and RAN and AI workloads begin sharing distributed infrastructure, orchestration, data architecture and lifecycle management become increasingly important. The report argues that this points toward a future RAN that functions less like static communications infrastructure and more like an intelligence fabric, dynamically coordinating connectivity, compute, data and policy.
Exactly how operators get there, and where the near-term business cases justify investment, remains the critical question. Download “Rethinking the RAN” for the complete analysis, operator perspectives and commercial outlook for Open RAN, AI-RAN and the path toward AI-native 6G.
