The Plan That Learns
summary
Network capacity planning uses AI to triage hundreds of weekly novel traffic patterns into a handful that need action, shrinking investigation windows from weeks to minutes, and to reconcile conflicting metrics via a measure‑enrich‑regrade loop that raises model accuracy from roughly 30% to 80%+ in pilots. It generates a ranked, costed menu of moves (reroute supply, pull inventory, shape demand) and records decision traces that remember each choice and its outcome so future recommendations reflect accumulated human judgment.
AI-written summary · about
related talks
21mFrom Reactive to Proactive: How AI is Redefining Resilience in Modern Networks
24mAI-Assisted Media Planning
08mReal AI Agents Need Planning, Not Just Prompting
27mAI-Powered Reliability: Stop Fighting Fires, Prevent Them
22mMicrodosing the Economy
22mThe Agent Control Plane: Managing Thousands of AI Agents
08mSelf-Healing Infrastructure and AI-Driven Code Evolution
22mThe AI Promise - From Myths to Real Benefits for Humans