What if telecom networks could predict failures before they happen, optimize power use without impacting performance and create entirely new revenue streams? That’s not some distant vision – AI is already making it possible. From cloud to edge, telecom operators are integrating AI into their networks to cut costs, improve reliability and unlock new business models.
At a recent panel discussion, industry leaders tackled how telcos can harness AI across cloud, core, and edge infrastructure. Moderated by Abe Nejad, Publisher of Network Media Group (NMG), the session featured experts from Intel, Ribbon Communications and Rakuten Symphony sharing insights on what’s working, what’s next and what challenges remain.
Speakers:
Watch the full video here.
The conversation made one thing clear: AI isn’t just another layer in the network; it’s becoming a fundamental part of how telcos operate. Instead of reacting to issues, operators are using AI to predict problems before they happen, reducing downtime and improving service quality.
Energy efficiency was another hot topic. With AI-driven deep learning models, telcos can optimize power consumption and cut energy use by as much as 25% – without affecting network performance. That’s not just good for the bottom line; it’s a step toward making telecom infrastructure more sustainable.
Beyond their own networks, telcos are also starting to enable AI for other industries. With edge locations and powerful infrastructure, telecom providers have an opportunity to offer AI-as-a-service, allowing enterprises to run AI applications without having to invest in their own compute-heavy environments.
A key takeaway from the discussion was that AI processing needs to happen at the right place. Not everything belongs in the cloud. While cloud AI provides scalability, some use cases, such as real-time security monitoring, need to happen at the edge, where data is generated.
The panelists shared real-world deployments where AI at the edge is enabling real-time decision-making, improving security, and even reducing network congestion. Instead of sending everything back to the cloud, these applications process data on-site, reducing latency and improving response times.
AI’s impact goes beyond reducing costs and improving efficiency. It’s also helping telcos explore new monetization strategies. Some of the ways AI is unlocking revenue include:
The shift is clear: telcos are moving from being just connectivity providers to AI-powered digital platforms. Despite the enthusiasm, the biggest challenge isn’t the technology – it’s making sure AI strategies align with business goals. Many operators struggle with scattered AI implementations, where different teams pursue AI projects without a clear business case.
The panelists agreed: AI adoption needs to be intentional. Instead of deploying AI just because it’s possible, telcos must focus on practical use cases, whether it’s cost reduction, new revenue streams, or better customer experiences.
“We will be rolling out deep learning algorithms on the Rakuten Cloud Native Platform, which have already been tested on real network data at some scale. And they are delivering energy savings across the network on the cross cluster level, which is significant.”
Stay tuned for more insights from industry leaders on the future of telecom and technology. Follow us for updates on upcoming discussions.
