Presented by Booz Allen
AI-RAN, or artificial intelligence radio area networks, is a reimagining of what wireless infrastructure can do. Rather than treating the network as a passive conduit for data, AI-RAN turns it into an active computational layer. It’s a sensor, a compute fabric, and a control plane for physical operations, all rolled into one. That shift has huge implications for industries from manufacturing and logistics to healthcare and smart infrastructure.
VentureBeat spoke with two leaders at the center of this transformation: Chris Christou, senior vice president at Booz Allen, and Shervin Gerami, managing director at Cerberus Operations Supply Chain Fund.
“AI-RAN can bring the promise of extending 5G and eventually 6G networks into the enterprise,” Christou 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 lets enterprises move from digitizing processes to autonomously operating them,” Gerami added. “The enterprise investment should not look at AI-RAN as a networking upgrade. It’s an operating system for physical industries.”
The difference between AI for RAN, AI on RAN, and AI and RAN
The difference between AI for RAN, AI on RAN, and AI and RAN is critical. AI on RAN runs enterprise AI workloads on edge compute infrastructure integrated with the RAN, enabling real-time applications like computer vision, robotics, and localized LLM inference.
AI and RAN represents the deeper convergence — where networks are designed to be AI-native, with AI workloads and radio infrastructure architected together as a coordinated, distributed system. At this stage, RAN evolves from a transport layer into a foundational layer of the AI economy.
“This is the transformational part,” Gerami said. “It’s jointly designing applications with networks. Now the application knows the network state, and the network understands the application’s intent. AI for RAN saves money. AI on RAN adds capability. Then AI and RAN together create entirely new business models.”
It’s this layered framework that makes AI-RAN more than an incremental evolution of existing wireless technology, and instead a platform shift that opens the network to the kind of developer ecosystem and application innovation that has historically been the domain of cloud computing.
How ISAC turns the network into a sensor
Integrated sensing and communications (ISAC) is the center of the infrastructure. The network becomes the sensor, a converged infrastructure simultaneously communicating and sensing its environment at the same time it hosts algorithms and applications at the edge. It will enable drone detection, pedestrian safety, and automotive sensing, and eventually even more innovative use cases.
The enterprise value proposition of ISAC and a network as the sensor is clear, Gerami says. Today, organizations rely on multiple discrete systems to achieve situational awareness: cameras, radar, asset trackers, motion sensors and more. Each comes with its own maintenance burden, integration overhead and vendor relationship. ISAC has the potential to handle many of those capabilities natively within the network.
“With ISAC you can do asset tracking at sub-meter precision inside factories and hospitals,” he explained. “You can detect movement patterns, perimeter breaches, 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, instant quality inspection, and predictive maintenance. There are the applications where the latency gap between cloud and edge is the difference between a system that works and one that doesn’t.
“Where edge AI kicks in is driving operations in milliseconds, not seconds, which is what cloud does,” Gerami explained.
Split inference can also change the game, Christou says.
“You have a lot of different use cases where the processing is done on the device, making that device more expensive and more power-hungry,” he said. “Now there’s the possibility of offloading that to a local AI-RAN stack, even getting into concepts like split inference, so you do some of the inference on the device, some on the edge AI-RAN stack, and some in the cloud, all appropriate to the use cases and the time scale of the processing required.”
Why the timing of AI-RAN investment is critical now
AI-RAN investment has a narrow and strategically critical window, both Germani and Christou said.
“5G infrastructure is already being deployed, almost getting to a point of completion. 6G standards are not yet locked in,” Gerami explained. “This is an architectural moment for AI-RAN to come in. It allows the ability to not make RAN become a telco-centric design only. 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. AI-RAN’s open architecture, built on software-defined, cloud-native, containerized components, changes that standardization dynamic.
“Previously in the wireless industry it was a very long cycle. Now we’re seeing a push to get it implemented, get it out there, get early pilots, and then we’ll see how the technology works,” Christou said. Simultaneously, in parallel, you can start defining the standards. You have real-life implementation experience to help influence how those standards take shape.”
And the entry point is accessible, Gerami added.
“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 a radio.”
Why AI-RAN is the future of innovative AI use cases
“We see AI-RAN as being an open architecture that’s truly driving innovation,” Gerami said. “It’s a flywheel for innovation. We want to create everything to be microservices, open native, cloud native, to allow partners to build vertical AI apps. There’s so much focus right now in the industry around how we can adopt AI effectively, how it will enable autonomy and robotics. This is one of those foundational pieces that can help realize some of those use cases. The future is about owning that physical inference.”
“There’s so much focus right now in the industry around how we can adopt AI effectively — how it will enable autonomy and robotics,” Christou said. “This is one of those foundational pieces that can help realize some of those use cases.”
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