Making AI (net)work: tips for a successful AI-integrated network by Jason Gintert | AIFNL

AI in network operations has moved past the demo. In some shops it's already cutting detection time and clearing the alert noise that used to bury the engineering team. In others, a promising rollout has quietly stalled the moment it met production. That difference between the two rarely comes down to the model, it comes down to how the AI gets integrated into the network and most importantly, the team around it.

This session gets into that integration and what has to sit underneath the AI for it to actually help: unified telemetry it can reason across instead of six tools that don't talk to each other. Answers a human in the loop can check, so nobody's asked to trust a black box. A home inside the workflow that people already use, not just another dashboard to babysit. A feedback loop that lets the system get corrected and improve, so trust builds instead of erodes.

We'll look at where AI is delivering real operational value today, and the specific conditions that separate a rollout that sticks from one that stalls.