- Architect for AI-era baselines, not legacy traffic assumptions. Design for sustained east-west flows, low jitter, and deterministic paths that support AI training and inference, not just classic north-south traffic.
- Translate network health into AI business outcomes. When you speak to leadership, link latency, loss, and path diversity to model training time, inference SLA, or customer experience, not just “link utilization.”
- Get in early on AI projects. Push to join the initial design discussions so connectivity, security, and data movement patterns are engineered in rather than patched later.
2. AI for networks: Self‑driving operations are becoming table stakes
The core thesis of Rahim’s presentation is that the legacy network can’t withstand the rigors of AI. “The old model of networking, static, manual, and reactive, simply cannot keep up with the speed and complexity AI introduces.” His alternative is an AI-native, self-driving operations model spanning Aruba Central and Mist, powered by Marvis, Marvis Minis, and an “agentic AI framework.”
On stage, he was joined by Sunalini Sankhavaram, VP of product management at HPE, who elaborated on this shift. “Experience-first AI in action” is built on “real-life experience data, every user, every minute, validated against real customer support cases, and enriched with digital twins.” In one demo, Marvis detected that “over 6% of user minutes were bad,” isolated the issue to a few overutilized APs, and “autonomously fixed the problem by enabling dual-band 5 GHz,” cutting peak utilization from 90% to 54%. Rahim summarized the outcome: “The network identified the issue, understood the root cause, determined the right action, and resolved the problem automatically before any user even had a chance to complain.”
For engineers, that’s a fundamental shift: from being the resolver to configuring, supervising, and governing these AI systems.
