AI has become one of the hottest topics in network operations. Every week seems to bring another announcement about AI-powered monitoring, AI-driven troubleshooting, or autonomous operations.
Its promise is compelling; instead of digging through dashboards, correlating alerts, and manually tracing problems across the network, engineers can simply ask a question and receive an answer.
At least in theory.
In practice, many AIOps implementations still struggle with one of the most important challenges in network operations: identifying root cause. The reason isn't that the AI is inadequate. It's that root cause analysis requires something many platforms still lack—context.
Most network issues don't announce themselves clearly. Often, the symptoms are obvious, but the cause is not.
A single performance issue may involve multiple devices, interfaces, paths, applications, and network conditions. Understanding what happened requires more than simply identifying an anomaly. It requires understanding how events relate to one another and which event triggered the problem in the first place.
Researchers studying automated root cause analysis have increasingly focused on causal relationships and contextual reasoning because identifying the true source of an issue is significantly more complex than detecting symptoms alone.
This distinction is important because many AIOps tools excel at identifying unusual behavior. Finding root cause, however, is a different challenge.
Many AIOps platforms begin with anomaly detection. The system observes network behavior, identifies something unusual, and generates an alert. Machine learning may help prioritize events, group related alerts, or identify patterns that deserve attention.
All of that is valuable, but identifying that something has changed does not explain why it changed.
A spike in latency is not a root cause, nor is an increase in packet loss, or an application slowdown. These are symptoms.
Determining root cause requires understanding the relationships between events, devices, configurations, and traffic flows across the network. The effectiveness of AI-driven operations depends heavily on the quality, completeness, and context of the underlying data. Without that context, AI can only make educated guesses.
This is where many AIOps initiatives encounter challenges; AI can only reason over the data it receives.
If the underlying monitoring platform collects limited telemetry, sampled data, or isolated metrics, then the AI's understanding of the environment will be equally limited.
In a recent article on the future of observability, industry observers noted that AI systems require complete, high-fidelity telemetry and contextual information to reason effectively about system behavior. Incomplete or heavily sampled data creates blind spots that limit the quality of AI-generated conclusions.
The same principle applies to network operations. An AI engine that sees only alerts and dashboards can summarize those alerts and dashboards, but it cannot magically infer information that was never collected.
That is why network visibility remains foundational.
As discussed in our article, What Total Network Visibility™ Really Means (and Why Most Tools Fall Short), visibility is not simply about collecting more data. It is about collecting the right data, preserving context, and understanding how conditions across the network relate to one another.
When that context is missing, root cause analysis becomes dramatically more difficult for humans and AI alike.
The most promising AIOps implementations are not simply adding AI on top of existing dashboards. They instead combine AI with richer operational context. Configuration data, interface statistics, path intelligence – the more complete the picture, the more effective the analysis becomes.
This is one reason recent AIOps research increasingly focuses on correlation, causality, and full-stack observability rather than anomaly detection alone. The goal is not simply to identify unusual behavior. The goal is to understand what caused it.
And that requires context.
AI will undoubtedly play an increasingly important role in network operations. Natural language interfaces, automated investigations, and AI-assisted troubleshooting have the potential to make engineers more productive and reduce time to resolution.
However, the success of those systems will depend less on the sophistication of the language model and more on the quality of the underlying network data. At the end of the day, AI cannot determine root cause from information it cannot see.
The organizations that get the most value from AIOps will not necessarily be the ones with the most advanced AI. They will be the ones that provide AI with the visibility, context, and operational intelligence needed to understand what is actually happening across the network.
That's when AI stops generating summaries and starts delivering answers.
Want to see what AI looks like when it's built on complete network visibility?
Join our upcoming webinar, "How AI Can Change NetOps," for a live demonstration of TotalView AI. We'll show how AI-powered analysis, deep network visibility, and built-in correlation work together to help teams troubleshoot faster, uncover root causes, and get accurate answers from their network using natural language.