Mark Berly, CTO, data center networking at Aruba, a Hewlett Packard Enterprise company, noted that things like zero touch provisioning have expanded the use of automation in recent years. He noted that for known, existing processes, automation is already well-established. Where AI goes beyond is that it can augment existing processes. The distinction is that earlier automation was more about codifying specific use cases and workflows.
In contrast, the panelists see AI-powered network automation as a shift towards more adaptive, autonomous capabilities that can handle unexpected situations, rather than just predefined tasks.
The path toward fully autonomous networks, where everything is automated, might be coming in the future, but it’s going to take time. Berly noted that he has a self-driving car and joked that it has tried to kill him at least once, which is why he now only uses the feature for parking the car.
“I do think we’re getting closer and closer to that autonomous state and honestly, it scares me. Like I said, my car almost killed me, I’m sure my network will try to crash itself,” Berly said.
Impact of adopting genAI on existing network capacity
As the adoption of generative AI (GenAI) continues to accelerate, the impact on existing network capacities and topologies is becoming a pressing concern.
In a panel session, Gerald de Grace, cloud architect and technical product manager at Microsoft, highlighted the immense scale of the challenge, noting that the company is looking at clusters with over 300,000 GPUs. The sheer number of components involved means that failures are inevitable, and de Grace emphasized the need for automated systems to quickly detect, isolate, and mitigate these issues.
