Presented by Booz Allen
AI-RAN, or artificial intelligence radio area network, is a reimagining of what wireless infrastructure can do. Instead of treating the network as a passive medium for data, AI-RAN turns it into an active computational layer. It is a sensor, a compute fabric, and a control plane for physical operations, all rolled into one. That shift will have a wide-ranging impact on industries ranging from manufacturing and logistics to healthcare and smart infrastructure.
VentureBeat spoke to two leaders at the center of this transformation: Chris Christo, senior vice president at Booz Allen, and Sherwin Gerami, managing director of the Cerberus Operations Supply Chain Fund.
“AI-RAN can bring the promise of expanding 5G and eventually 6G networks into the enterprise,” Christo said. “Proving that a platform can host inference at the edge to enable new types of AI – in particular, physical AI and autonomy-type use cases for things like smart manufacturing and smart warehousing – can make operations more efficient and effective.”
“AI-RAN allows enterprises to move from digitizing processes to allowing them to operate autonomously,” Gerami said. "Enterprise investments should not view AI-RAN as a networking upgrade. It is an operating system for physical industries."
AI for RAN, AI on RAN and difference between AI and RAN
The distinction between AI for RAN, AI on RAN, and AI and RAN is important. AI on RAN runs enterprise AI workloads on edge compute infrastructure integrated with the RAN, enabling real-time applications such as computer vision, robotics, and localized LLM inference.
AI and RAN represent deep convergence – where the network is designed to be AI-native, with AI workloads and radio infrastructure designed together as a coordinated, distributed system. At this stage, RAN evolves from a transportation layer into the foundational layer of the AI economy.
"This is the transformative part," Gerami said. "It is jointly designing applications with the network. Now the application knows the network state, and the network understands the intent of the application. AI for RAN saves money. Adds AI capabilities on RAN. Then AI and RAN combine to create entirely new business models.
It’s this layered framework that makes AI-RAN much more than an incremental evolution of existing wireless technology, and instead a platform shift that opens up the network to the kind of developer ecosystem and application innovation that has historically been the domain of cloud computing.
How ISAC turns networks into sensors
Integrated Sensing and Communications (ISAC) is central to the infrastructure. The network becomes the sensor, an integrated infrastructure simultaneously communicating and sensing its environment as well as hosting algorithms and applications at the edge. This will enable drone detection, pedestrian safety and automotive sensing, and ultimately even more innovative use cases.
Jerami says the enterprise value proposition of ISAC and the network as sensors is clear. Today, organizations rely on many different systems to achieve situational awareness: cameras, radar, asset trackers, motion sensors, and more. Each comes with its own maintenance burden, integration overhead, and vendor relationships. ISAC has the ability to seamlessly handle many of those capabilities within the network.
“With ISAC you can do asset tracking at sub-meter precision inside factories and hospitals," he explained. "You can detect motion patterns, perimeter violations and anomalies. Smart buildings can have occupancy-aware HVAC and energy optimization."
How AI-RAN shaves milliseconds off edge AI and inference
With AI-RAN, edge AI and low-latency inference become supercharged in use cases like real-time robotics management, accelerated quality inspection, and predictive maintenance. There are applications where the latency difference between the cloud and the edge is the difference between a system that works and one that doesn’t.
“Where edge AI works is it performs operations not in seconds, but in milliseconds, which is what the cloud does,” Gerami explained.
Divided estimation can also change the game, says Christou.
“You have many different use cases where the processing is done on the device, making that device more expensive and more power-consuming,” he said. “There is now the possibility of unloading this into the local AI-RAN stack, even getting into concepts like partition inference, so you do some inference on the device, some on the edge AI-RAN stack, and some in the cloud, all appropriate to the use cases and time scale of processing required.”
Why is the timing of AI-RAN investments important now?
Both Germany and Christou said there is a narrow and strategically important window of AI-RAN investment.
“5G infrastructure is already being deployed, almost nearing completion. 6G standards have not yet been finalized,” Gerami said. “This is an architectural moment for the advent of AI-RAN. It allows the ability to make RANs not just telecommunications-centric designs. It allows the enterprise to become the co-creator of the application, the revenue and value generator of that network infrastructure.”
Historically, enterprise IT has consumed wireless standards rather than shaped them. The open architecture of AI-RAN, built on software-defined, cloud-native, containerized components, changes the dynamics of standardization.
“It used to be a very long cycle in the wireless industry. Now we’re seeing pressure to implement it, get it out there, get early pilots, and then we’ll see how the technology works," Christo said. Also, in parallel, you can start defining standards. You have the real-life implementation experience to help influence the way those standards take shape.
And the entry point is accessible, Gerami said.
“The barrier to entry is very low," He said. "Right now, it’s all code-based, all software. It’s no different than downloading software. You get yourself an Nvidia box and you can deploy it with the radio.”
Why AI-RAN is the future of innovative AI use cases?
“We see AI-RAN as an open architecture that is really driving innovation," Gerami said. "This is a cycle for innovation. We want to build everything from microservices, open native, cloud native, to allowing partners to build vertical AI apps. There is a lot of focus in the industry right now on how we can effectively adopt AI, how it will enable autonomy and robotics. It’s one of the foundational pieces that can help realize some of those use cases. The future is about owning that physical projection.
“There is a huge focus right now in the industry on how we can effectively adopt AI – how it will enable autonomy and robotics," Christo said. "This is one of the foundational pieces that can help realize some of those use cases.”
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