Several converging trends make 2025 pivotal for AI-RAN. Across the Department of Defense (DOD), the intelligence community, and civil agencies, growing needs for enhanced mobile broadband capabilities and more robust security have driven deployment of an initial set of private 5G networks. As adoption expands, agencies will identify new use cases spurring demands for more agile edge networks.

Executive Order 14320, Promoting the Export of the American AI Technology Stack; mandates within the National Defense Authorization Act around 5G; and national spectrum policy initiatives from the National Telecommunications and Information Administration (NTIA) point to a federal push to secure U.S. telecom technology stacks. These policy moves create a regulatory foundation and strategic imperative to guide AI-native 6G network deployments.

AI-RAN represents a broader shift toward greater ecosystem-driven innovation. Multiple component providers can participate through open application programming interfaces (APIs), enabling network apps and edge AI applications, similar to how mobile app ecosystems transformed consumer technology. In addition, the rapid expansion of the AI-RAN Alliance to more than 100 members signals industry alignment around a common vision for this innovation and change. The coalition includes technology companies and academic institutions all working toward the integration of AI with RAN, with the goal of advancing RAN performance and enabling new capabilities and use cases.

Every second, millions of radio transmissions use a spectrum that extends from 600 megahertz to beyond 100 gigahertz. These transmissions can include voice calls, data packets, video streams, sensor readings, GPS coordinates, encrypted military communications, and other media. To manage this profusion of traffic, AI-RAN integrates artificial intelligence directly into the radio access network, which is the infrastructure layer connecting end-user devices from the edge to the broader telecom network.

This integration operates through the deployment of AI models that continuously optimize power control, beamforming, interference mitigation, and anomaly detection, with the ability to continuously train these models based on observed traffic patterns and network conditions. Traditional cell towers function as sophisticated relay stations; AI-RAN changes them into thinking machines.

As mentioned, AI-RAN will transform networks in two fundamental ways. First, the application of AI to wireless networks will drive new levels of spectral and power efficiency and make the network more programmable to adjust to multiple types of scenarios and use cases. This programmability will reduce the need for manual configurations while making the network more adaptable to changes in demand, interference, and outages.

In addition, the open programmability of AI-RAN will enable new network features. For example, industry and government have been developing new integrated sensing and communications (ISAC) capabilities, a key feature that will be incorporated in future 6G standards. With ISAC, the network now becomes a sensing system with the ability to detect objects in its vicinity, unlocking the potential for a plethora of novel applications.

Second, AI-RAN will allow for the same infrastructure that is providing the RAN to also deliver edge AI applications and workloads. Commercially, this critical feature will help telecom providers better monetize their infrastructure investment while offering mission operators at a forward operating base a reduction in the compute footprint needed to provide advanced wireless and AI services.

AI-RAN differs fundamentally from earlier network virtualization approaches. While virtualized RAN (vRAN) architectures made networks more vendor-agnostic, AI-RAN makes them intelligent. It takes the same AI techniques revolutionizing language processing and computer vision and uses them to transform radio wave management. Beyond routing traffic, AI-RAN systems use neural networks that learn from patterns and anticipate congestion, allowing for precise, instant adjustments.

The AI-RAN systems of the future will run on models that understand the deep physics of radio wave propagation and the tactics of electronic warfare, all rolled into a single AI system that can adapt to shifting scenarios. Technical implementation includes network apps that execute AI algorithms in sub-1-millisecond loops. These models range from optimization algorithms to sophisticated neural networks capable of processing computer vision or natural language tasks.

For federal agencies, the time is now to start pilots so that decision makers can evaluate the new technology and influence 6G standards and technology development if needed. Here are potential areas of focus: