GlobalLogic’s AI-RAN Trials: A Quiet Breakthrough in the Hype-Filled Race to Intelligent Networks

In a laboratory in Latin America, a team of engineers recently ran a series of tests that may not have made global headlines but could quietly reshape how operators think about AI in the radio access network. GlobalLogic, the digital engineering arm of the Hitachi Group, set up a 5G Open RAN environment and introduced AI and GPU acceleration to see what would happen. The results were striking: processing times improved by as much as 15 times under certain conditions, while power consumption fell by around 40%. In a second experiment, deep-learning models used to distinguish genuine network-access attempts from noise or interference achieved a 99.98% reduction in false detections.

These are not the kind of numbers that come from a marketing department. They come from a controlled proof-of-concept, and they matter because they point to something that the telecom industry has been chasing for years: a practical, incremental way to make networks smarter without ripping and replacing everything. While vendors like Nokia and Ericsson grab headlines with commercial AI-RAN deployments and multi-billion-dollar trials, GlobalLogic’s work in Latin America offers a grounded reminder that the path to AI-native networks may be paved with small, measurable wins rather than giant leaps.

The AI-RAN Gold Rush

The past week alone has seen a flurry of AI-RAN announcements. Nokia reported growing global momentum for AI-RAN, with operators across North America, Europe, Asia-Pacific, and the Middle East advancing from early evaluation to lab and live field trials alongside partners such as NVIDIA. The platform has already delivered spectral efficiency improvements of more than 20%. Ericsson and Vodafone Egypt announced the commercial deployment of Ericsson’s AI-powered RAN Processor 6672, which enables AI-native operations directly at the radio site with up to four times higher processing capacity and more than 50 percent lower energy use. Nokia CEO Justin Hotard claimed that 19 operators have signed up for “initial deployments” of its forthcoming AI-RAN offers. The company is also betting on dApps—a new strain of network application designed for tasks demanding even speedier execution and a real-time RIC, leveraging a newer E3 interface that Nokia now supports.

Meanwhile, the Open Networking & Edge (ONE) Summit 2026 program, hosted by LF Networking, is putting AI center stage, with sessions on agentic AI, autonomous networks, and open source RAN. And the India Mobile Congress 2026 is set to feature discussions on AI-native telecom networks, RAN, and edge AI infrastructure. The message is clear: AI-RAN has moved from vision to reality, and everyone wants a piece of it.

But amid this gold rush, it’s easy to lose sight of what actually works. The industry has a long history of hype cycles that promise transformation but deliver little more than PowerPoint slides. The GlobalLogic trials, while modest in scale, offer something different: concrete, measurable evidence that AI and GPU acceleration can deliver real gains in a 5G Open RAN environment.

Inside the GlobalLogic Trials

GlobalLogic’s experiments were conducted in a controlled 5G Open RAN environment, which is significant because Open RAN’s disaggregated architecture—separating hardware and software, encouraging interoperability—creates the ideal sandbox for testing alternative computing architectures. By bringing AI and GPU acceleration into the RAN, GlobalLogic was able to demonstrate that processing times could be improved by as much as 15 times under certain conditions. That’s not a marginal improvement; it’s an order-of-magnitude leap that could enable new use cases and dramatically improve efficiency.

The 40% reduction in power consumption is equally important. Energy costs are one of the biggest operational expenses for mobile operators, and reducing them by nearly half would be a game-changer, especially as networks densify and 5G becomes more widespread. The fact that this was achieved in a controlled environment means the results may not translate directly to commercial networks, but they provide a strong indication of what’s possible.

The second experiment, which used deep-learning models to distinguish genuine network-access attempts from noise or interference, achieved a 99.98% reduction in false detections. This is a critical capability for network security and reliability. False detections can lead to unnecessary handovers, dropped connections, and wasted resources. By using AI to filter out noise, operators can improve the user experience and reduce operational overhead.

What’s particularly notable is that these gains were achieved in an Open RAN environment. Open RAN’s inherent flexibility—the ability to mix and match components from different vendors—makes it easier to introduce new technologies like GPU acceleration and AI models. In a traditional, proprietary RAN, such experimentation would be far more difficult and costly.

Why Open RAN Is the Enabler

Open RAN and AI-RAN are closely linked. By separating network functions and encouraging greater interoperability between hardware and software components, Open RAN creates an environment in which software-defined capabilities and alternative computing architectures can play a larger role. This matters because AI-RAN is unlikely to be a single piece of equipment that operators simply install. It is better understood as an architectural shift bringing together radio systems, software, AI models, accelerated computing, and distributed processing.

For Latin American operators, this could eventually provide a route to introducing new capabilities incrementally rather than treating the transition to future networks as one massive upgrade. The region’s next connectivity challenge will not simply be about adding more towers, spectrum, or capacity. It will be about extracting more intelligence and efficiency from the infrastructure being built today. If the mobile network of the future can dynamically decide where to compute, where to save energy, and what to ignore, the next revolution in connectivity may be less visible than the last one. It may happen inside the network itself.

GlobalLogic’s experiments do not prove that AI-RAN is ready to transform commercial mobile networks overnight. They demonstrate, in a controlled 5G Open RAN environment, that the underlying technologies can produce measurable gains. That’s an important distinction. The telecom industry is littered with promising lab results that never made it to prime time. But the fact that these results were achieved in an Open RAN setting, with commercially available GPUs and AI frameworks, suggests that the path to deployment may be shorter than some skeptics believe.

The Competitive Landscape

While GlobalLogic’s trials are notable, they are not happening in a vacuum. Nokia is making a big bet on dApps and Nvidia GPUs, with CEO Justin Hotard claiming 19 operators have signed up for initial deployments. The company’s long-term vision for the RAN involves a transition towards Nvidia’s graphics processing units as a general-purpose substitute for custom silicon. While Nokia has co-developed its latest custom silicon product with Marvell Technology, it anticipates a future where Nvidia chips become the primary alternative.

Startups like Pulsone (Cohere Technologies), DeepSig, and ZaiNar are already designing RAN software for Nokia-built networks. Nokia’s adoption of the E3 interface for dApps is a significant move, as it enables support for sensing, geo-precise location monitoring, and improved spectral efficiency. This is part of a broader trend towards open, interoperable RAN architectures that can support a vibrant ecosystem of applications.

Ericsson, meanwhile, is focusing on AI-powered RAN processors like the 6672, which enables AI-native operations directly at the radio site. The Vodafone Egypt deployment is a commercial validation of this approach. Samsung and other vendors are also investing heavily in AI-RAN, though with different strategies. The competition is heating up, and operators have more choices than ever.

But the GlobalLogic trials highlight a different aspect of the AI-RAN story: the role of system integrators and engineering firms in bridging the gap between vendor promises and operator realities. GlobalLogic, as a Hitachi Group company, brings a unique perspective—combining deep engineering expertise with a focus on practical, deployable solutions. Its work in Latin America is particularly relevant because the region is often overlooked in global telecom narratives, yet it faces many of the same challenges as more developed markets: rising data traffic, pressure to reduce costs, and the need to stay competitive.

The Road Ahead

The AI-RAN market is expected to grow significantly in the coming years. Dell’Oro projects $35 billion in cumulative AI RAN revenue by 2030, and Analysys Mason estimates the market for AI-driven network intelligence at$2.2 billion. These forecasts are encouraging, but they also raise expectations. Operators need to see real, tangible benefits before they commit to large-scale deployments.

GlobalLogic’s trials provide a glimpse of what those benefits could look like: faster processing, lower power consumption, and fewer false detections. These are not flashy features, but they are the kind of operational improvements that can make a real difference to an operator’s bottom line. They also align with the industry’s broader push towards sustainability and energy efficiency, which is becoming a key consideration in network planning.

Of course, there are still challenges to overcome. Scaling these results from a controlled environment to a commercial network will require significant engineering effort. Operators will need to ensure that AI models are robust and reliable, that GPU acceleration can be integrated seamlessly into existing infrastructure, and that the economics work. But the direction of travel is clear. AI and GPU acceleration are coming to the RAN, and Open RAN is the vehicle that will get them there.

As the industry gathers at events like the ONE Summit and India Mobile Congress, the conversation will inevitably focus on the big announcements and the bold visions. But it’s worth remembering that the real progress often happens in quieter places—in labs and testbeds where engineers are figuring out what actually works. GlobalLogic’s trials in Latin America may not have the same headline appeal as a multi-billion-dollar deal, but they represent a crucial step forward. They show that AI-RAN is not just hype; it’s a tangible technology with measurable benefits. And for operators looking to make their networks smarter, that’s the most important news of all.

Sources

• LATAM mobile networks could become smarter with AI-RAN and GPU acceleration – Intelligent CIO LATAM
• Enterprise IT & Communications Technology Industry News | Pipeline Magazine
• Nokia bets on dApps, Nvidia GPUs for 5G open RAN competitiveness: Report, ETTelecom
• AI Takes Center Stage at Open Networking & Edge (ONE) Summit 2026 Program, Hosted by LF Networking
• 10 Terms to Know Ahead of India Mobile Congress 2026, as AI-led evolution of telecom networks continues - The Economic Times