Walk into a network operations center this year and the useful AI is not running the place. It is doing three specific jobs, and doing them well: cutting an alert storm down to the one incident that matters, pointing at the likely cause, and deciding what deserves a human’s attention first. That is where AI in NetOps pays for itself right now. The part worth noticing is that all three jobs lean on the same thing.
The reason traces back to a problem Gartner® names at the top of its research on AI use cases for NetOps: “Enterprise networks are dynamic and complex, with cross-domain visibility gaps that legacy tools cannot address via rules and thresholds alone.” Closing those gaps is the job underneath the three jobs, and it is a data problem before it is an AI problem.
Selector’s place in the research
Selector is named in the representative vendor list for AI platforms for NetOps in Gartner’s “Top 3 Use Cases for AI in NetOps.”
This next part is Selector’s own view rather than Gartner’s. We think the reason these use cases stand or fall on a single dependency is straightforward: each one is an act of correlation. Reading signals in relation to one another, across domains, is the whole job, and it is what Selector was designed to do.
Three jobs, one dependency
Anomaly detection and triage. The job is to separate a real problem from background noise and decide what to escalate. It works by reading metrics, events, logs, and flows against each other and against the network’s topology, not by watching one threshold on one device. A signal that looks alarming in isolation is often routine once you can see what it connects to.
Event correlation and noise reduction. The job is to collapse a storm of alerts into the single incident behind them. That requires knowing which signals share a path, a dependency, or a change window. Without those relationships, you are suppressing alerts by guesswork, which is how a real incident gets buried under the noise meant to surface it.
Root cause analysis. The job is to validate a hypothesis against the live state of the network rather than an engineer’s hunch. That validation only holds if the state it checks against is complete and current across every domain the fault could touch.
None of the three is achievable from a per-domain, per-vendor slice of telemetry, which is why Gartner ties the whole category to one prerequisite: “Successful AI implementation in NetOps depends on improving troubleshooting workflows through cross-domain network data correlation.” It is also why the honest near-term scope is deliberately narrow. In Gartner’s words, “Near-term value will come from narrower AI use cases such as incident triage, event correlation and guided remediation.” The narrowness reflects where a correlated picture is already trustworthy enough to act on.
There is a structural reason to own this layer deliberately. Gartner observes that “No single AI platform vendor owns the full AI-driven network operation life cycle with depth across telemetry, correlation and validation.” If no one owns the whole stack, the layer that earns its keep is the one that can take in and normalize telemetry from every domain and vendor, so the three jobs reason from one consistent model instead of several partial ones. Correlation that stops at a single vendor’s coverage hands every incident an incomplete map, and an incomplete map is where triage, correlation, and RCA each come apart.
Where to begin
Adopting AI in NetOps well is mostly a sequencing question, and the sequence follows the data rather than the AI. The first project is the correlated, topology-aware view across domains and vendors, because all three use cases draw on it and it sharpens day-to-day operations before any agent is in the picture. From there, point AI at the jobs where that view is already good enough, and measure them against operational baselines such as alert noise and MTTR rather than against an autonomy roadmap. Keep validation in the loop throughout, so every recommendation can be checked against real network state before anything executes.
None of this asks you to bet on a winning agent or wait for a finished autonomy story. It asks for the layer the three use cases already run on. Build that layer, and each new use case costs less than the one before it.
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Required disclosures
Gartner, Top 3 Use Cases for AI in NetOps, Shriya Mehrotra, Sushovan Mukhopadhyay, 5 May 2026.
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