"AI Is Just Another Phase… Right?" 5 Myths About AI for NetOps



I recently went through a detailed discussion around AI in network operations, specifically focused on the skepticism many engineers have seen before with previous technology waves.

Some practical observations:

• Most networks already generate large volumes of telemetry, logs, and tickets, but correlation remains manual
 • Troubleshooting often requires switching between multiple vendor tools and systems
 • AI becomes useful when it can read across telemetry, configurations, and tickets in one workflow
 • Vendor-specific AI tools are limited to their own ecosystems
 • A platform approach allows teams to build workflows tailored to their environment

One misconception addressed was that AI is just another layer on top of existing tools. In practice, its value comes from connecting data across silos and providing answers that engineers can act on.

Another key point was around build versus buy. Building everything internally provides control but creates long-term maintenance overhead. Using a platform and building workflows on top provides a more balanced approach.

The biggest takeaway is that AI becomes operationally meaningful when it reduces fragmentation across tools and provides a unified way to troubleshoot and analyze network behavior.

Sharing this because it aligns closely with how real-world environments are evolving and how teams are starting to think about AI beyond the hype.

Sharing the full write-up below for anyone who wants to explore all five myths and where platforms like Network Copilot™ fit in:


Reference

AI Is Just Another Phase? 5 Myths About AI for NetOps
https://aviznetworks.com/resources/blogs/ai-just-another-phase-5-myths-about-ai-for-netops

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