With the rapid advancement of generative AI (GenAI) and AI-enhanced RAN (AI-RAN), networks are becoming more intelligent, autonomous and efficient. The combination of these technologies is enabling smarter automation, real-time analytics, and better monetization opportunities for operators and their ecosystems.
At a recent panel discussion, industry leaders explored how AI and GenAI are reshaping Open RAN, driving network efficiencies, and accelerating the adoption of AI-driven automation. Moderated by Abe Nejad, Publisher of Network Media Group (NMG), the session featured experts from A5G Networks, Verizon Enterprise Solutions, Rakuten Symphony, Boldyn Networks, and Oracle, sharing insights on the impact of AI in private and public network deployments.
Speakers
Watch the full interview here.
The discussion highlighted how AI is fundamentally changing how Open RAN networks operate. Open RAN started as a way to introduce vendor diversity and interoperability, but the addition of AI and GenAI is taking automation to the next level. With AI-driven optimizations, networks can self-adjust, allocate resources dynamically, and reduce operational costs.
One of the biggest shifts is the ability to automate network management tasks that were previously manual and time-consuming. AI-powered solutions are now handling traffic optimization, spectral efficiency, and predictive maintenance, allowing operators to focus on innovation instead of troubleshooting. The introduction of GenAI in Open RAN is also lowering entry barriers for developers by making it easier to create and deploy rApps and xApps.
But AI is only as good as the data it can access, and one of the major challenges discussed was data fragmentation across telecom networks. Right now, critical network data is often siloed between radio access, core, operations and business systems – limiting AI’s full potential. For AI-driven automation to work at scale, operators need a strategy to unify and standardize data across these domains.
Beyond that, another issue raised was that data isn’t just siloed within networks, but also between operators. If AI models could be trained across multiple operators, rather than each telco working in isolation, the industry could accelerate AI adoption, improve predictions, and create smarter, more adaptable AI-driven networks.
While AI and GenAI offer game-changing potential, the question of how to monetize these technologies remains open. The panelists agreed that real monetization happens where there is tangible value creation. For telcos, that value comes in multiple forms:
As AI becomes more embedded in telecom, new business models will emerge, shifting operators from traditional connectivity providers to AI-powered digital service platforms.
The conversation wrapped up with a look into the future. The next wave of AI in telecom will focus on energy-efficient networks, real-time network slicing and the evolution of fully autonomous operations. Sustainability is becoming a bigger focus, with AI helping optimize power consumption and reduce environmental impact.
GenAI is also expected to redefine how telecom engineers work, acting as an AI assistant that can help build, manage, and troubleshoot networks with minimal human intervention. This shift will lower technical barriers and enable a broader range of developers and enterprises to leverage AI-driven networks without requiring deep telecom expertise.
"We will see a lot of traction in terms of use cases, especially around sustainability and green slicing, which is energy efficient slicing."
Stay tuned for more insights from industry leaders on the future of telecom and technology. Follow us for updates on upcoming discussions.
