AI adoption is accelerating — and the resulting traffic is more bandwidth-intensive, unpredictable, and latency-sensitive than traditional applications. According to Enterprise Management Associates (EMA), fewer than half of AI-adopting enterprises believe their networks or observability tools are prepared for more AI traffic.

EMA research also shows that enterprises delaying observability and optimization upgrades are already playing catch-up, and might risk degraded AI performance, user frustration, or wasted infrastructure investment.

Our latest white paper explains what AI traffic changes, why legacy tools fall short, and how IT leaders can build AI-ready networks without sacrificing performance for everything else.

 

Key Takeaways in This White Paper

  • Why AI workloads generate traffic patterns that overwhelm traditional monitoring tools
  • New observability requirements for WAN, cloud, and edge networks
  • How real-time, high-fidelity data collection closes blind spots created by polling and sampling
  • Why AI-aware analytics, predictive insights, and anomaly detection are now essential
  • How AI itself is becoming a critical tool for managing AI-driven networks
  • The role of automated traffic prioritization for AI workloads

 

AppLogic Networks Prepares
Networks for AI Readiness

  • Elevated network observability beyond packet-level metrics
  • AI-powered application intelligence and experience scoring
  • Context-aware traffic optimization aligned to business intent
  • A single experience plane that connects insight to action