Operations | Monitoring | ITSM | DevOps | Cloud

Throughout this blog series, we’ve followed how AI reshapes network operations – from foundational data harmonization to real-time correlation, from contextual insights to agent-driven automation, and most recently, to conversational access through natural language interfaces. But we haven’t reached the final destination.

When we discuss networks, we typically envision data packets racing along physical wires like vehicles on a highway. But beneath this visible traffic flows another critical pathway that few recognize: the control plane highway. This unseen infrastructure, where routing information flows between devices, makes the data highway possible. Before user data can flow, millions of paths must be established, creating a parallel network of equally vital importance.

For years, network operations have relied on complex query languages that demand specialized knowledge. Extracting insights from network data often meant writing intricate commands in formats like SQL, a skill reserved for seasoned IT professionals. But what if anyone, regardless of expertise, could ask a simple question and get immediate, accurate answers from their network?

Artificial intelligence is reshaping how enterprises manage and secure their networks, but not all AI is created equal, and not all Large Language Models (LLMs) are ready for the job. While tools like ChatGPT and Google Gemini are transforming communication and productivity, applying general-purpose LLMs to something as specialized and high-stakes as network operations is an entirely different challenge. Networks are dynamic, complex, and context-heavy.