AI-driven network automation is transforming enterprise IT

AI-driven network automation is reshaping the way enterprises scale their infrastructure to meet the demands of artificial intelligence.

As AI workloads surge, networks must evolve with smarter, autonomous solutions to handle rising data demands. Technologies such as liquid cooling, co-packaged optics and high-performance fabrics are now essential for AI-driven data centers. To stay competitive, organizations must enhance efficiency, scalability and resilience by adopting AI for networking to streamline operations and networks for AI to support massive GPU clusters, according to Rami Rahim (pictured), chief executive officer of Juniper Networks Inc.

Juniper Networks Rami Rahim talks with theCUBE about AI-driven network automation.

“I think there are three really critical ingredients of an AI native network,” Rahim said. “The first is you must have access to the right data. Second, it’s about having a proven cloud that can scale from the smallest to the largest of customers as we have done with incredible wins around the world.”

Rahim spoke with theCUBE’s Dave Vellante and Bob Laliberte at MWC25, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how AI-driven network automation is transforming enterprise infrastructure, optimizing network operations and enabling scalable, high-performance connectivity to support the rapid growth of AI workloads. (* Disclosure below.)

AI-driven network automation: Transforming operations

The integration of AI in networking is fundamentally reshaping how organizations manage their infrastructure. Traditional network operations have long been burdened by complex troubleshooting and maintenance requirements. However, AI-driven solutions are now making network management more autonomous, allowing IT teams to focus on strategic initiatives rather than daily firefighting.

“Starting with AI for networks, any CTO of a big company, CIO, is going to be typically struggling just keeping up with maintaining the network to provide a great experience for their end users. Keeping the lights on typically is an arduous task in and of itself,” Rahim said. “Providing artificial intelligence and moving much of that operation to robots, basically software that’s doing that work that would otherwise have to be done by humans is what this opportunity is all about.”

As AI continues to permeate network operations, enterprises are seeing tangible benefits such as reduced downtime, lower operational costs and improved end-user experiences. Companies such as Juniper are leading the way with AI-powered platforms that provide real-time insights, automate troubleshooting and enhance overall network performance, according to Rahim.

“Our solution, which is driven by Mist AI, is truly unique in the industry today in giving operators that freedom to focus on much more consequential, important things like advancing their strategies, keeping the disruptors out, generating new revenue streams,” he explained.

The rise of networks built for AI applications

AI applications require more than just traditional data center networking. The computational power necessary for large-scale AI models, such as generative AI and large language models, demands ultra-fast and high-capacity networks. Enterprises and cloud providers alike are investing heavily in building a strong networking infrastructure that can efficiently connect thousands — or even millions — of GPUs.

“The pace of investment, tens of billions, hundreds of billions of dollars going into learning. In time, all of that learning has to translate to inference and value generation, that’s necessarily going to happen closer to where the data is at the edge or even in the customer premises,” Rahim noted.

Networking for AI also requires innovations in power efficiency and congestion management. Companies are increasingly turning to purpose-built silicon and software-driven optimizations to ensure seamless AI model training and inference. The ability to detect and mitigate network congestion in real time is now a competitive differentiator in the industry, Rahim said.

“We have built into our automation capabilities for the data center, the ability to detect congestion and to proactively alleviate it before it starts to reduce the utilization of those precious GPU resources. That has resulted in some of the wins that we’re achieving now in that space,” he added.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of MWC25:

(* Disclosure: Juniper Networks Inc. sponsored this segment of theCUBE. Neither Juniper Networks other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE