AI can help network operations teams sift through alert floods, connect evidence across network and application domains, and automate narrowly defined fixes. It is not a substitute for trustworthy telemetry or sound change controls: teams should begin with recommendations, verify the evidence, and grant systems only the autonomy they can monitor and reverse.
Why is network complexity outpacing NetOps teams?
Modern service problems rarely stay inside one network boundary. A slow application reported over Wi-Fi, for example, could involve wireless conditions, DNS, identity policy, endpoint posture, security inspection, cloud connectivity, application behavior, or an upstream provider. This is an illustrative troubleshooting scenario described by Cisco, not a measured case study. A tool that sees only one domain may surface the symptom without identifying the cause.
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A September 23, 2026 Cisco Newsroom announcement summarized an independent Omdia survey of 1,000 IT and network operations leaders at organizations with 500 or more employees in North America, Western Europe, and Asia-Pacific. In that sample, 92% said performance issues commonly span multiple domains and require correlation across ten or more tools. The figure describes respondents in this survey, not every NetOps organization. Cisco’s summary of the Omdia survey is the published evidence for these figures.
The same summary reported about 4,100 monitoring alerts and events per organization per day, with more than half network-related. Cisco/Omdia estimated that manually clearing the daily network-alert backlog would require roughly 100 IT specialists. That estimate illustrates the burden described in the survey; it is not a universal staffing formula.
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Change speed is another pressure point: 57% of survey respondents said their current change processes could not keep up with the speed required. These results make a case for improving how teams collect, correlate, and act on information—not for assuming that automation will automatically reduce costs or incident duration.
How can AI help network operations?
“AI” in NetOps covers several different capabilities. Analytics and machine learning can find patterns across telemetry, identify anomalies, prioritize events, and support traffic, capacity, or maintenance analysis. Generative interfaces can help operators ask questions of available network context, summarize evidence, and draft configuration or documentation. Automation can carry out a bounded action when the system has enough context and the organization has defined the permitted action, approval conditions, and recovery path.
These are use cases, not a guarantee that every product offers them equally well. An August 2024 Enterprise Strategy Group research infographic, commissioned by Juniper Networks and published in December 2024, listed the following percentages of respondents implementing or considering each application. The figures are older than the 2026 Cisco/Omdia survey and should not be read as current adoption rates.
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| AI use case | Share implementing or considering it |
|---|---|
| Network performance optimization | 40% |
| Security threat detection | 39% |
| Traffic analysis and optimization | 34% |
| Capacity planning | 33% |
| Load balancing | 32% |
| Anomaly detection and alerting | 31% |
| Predictive maintenance | 30% |
| Dynamic network scaling | 28% |
Those percentages come from the infographic reproducing the ESG research, not from a product performance test. The commissioned infographic provides the use-case list and attribution.
How does AIOps reduce network alert overload?
AIOps can make a large event stream more actionable by grouping related alerts, correlating them with topology and service context, highlighting unusual behavior, and ranking likely investigations. The aim is to help an engineer move from many separate notifications to a smaller set of evidence-backed questions: what changed, which services and users are affected, and where should troubleshooting begin?
Correlation matters because alerts that arrive from separate monitoring tools can describe different effects of one underlying incident. A system with only partial telemetry may group unrelated events or miss the link between them. Operators still need to check whether the proposed relationship fits the actual service, topology, and change history before closing alerts or taking action.
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- FLEXIBLE MOUNTING OPTIONS: Compact metal design supports desktop or wall-mount placement for versatile installation.
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The 2026 Cisco/Omdia summary said 95% of respondents found significant shortcomings in existing non-agentic AIOps tools. This is a survey finding about respondents’ assessments, not proof that a particular newer tool resolves those shortcomings.
Can AI troubleshoot network problems?
AI can assist investigation by connecting symptoms to available context and presenting a likely explanation with supporting signals. For a slow application, useful context might include wireless performance, DNS resolution, identity and security policy, endpoint state, application behavior, cloud paths, and provider connectivity. A cross-domain view can help an operator test possibilities that a single-domain dashboard would not show.
That is decision support, not infallible root-cause diagnosis. The quality of an answer depends on telemetry coverage, data freshness, integration, and whether the relevant environment is represented accurately. Treat a proposed cause as a hypothesis to verify against logs, topology, policy, recent changes, and user impact—not as a finding merely because an AI system stated it confidently.
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What is agentic AI in NetOps?
In NetOps, agentic AI refers to systems that can pursue an operational goal through a sequence of actions, rather than only presenting an answer or recommendation. Depending on the system and its permissions, that could include gathering evidence, selecting a response, or carrying out a network change. The term does not specify how much autonomy a system has: a workflow may require human approval for every action, allow predefined low-risk actions, or permit broader activity under policy.
In the Cisco/Omdia survey summary, 75% of respondents reported deploying AI for NetOps, 51% reported agentic AI acting in production, and 84% expected an AI-led operating model within twelve months. These are self-reported responses from the survey sample, not audited counts of deployment across the market. The same summary reported that 80% were comfortable with high or full autonomy, 24% with no human oversight, and 82% with some production changes without prior approval. Stated comfort is not evidence that autonomous changes are inherently safe or successful.
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How do you safely automate network changes?
Expand autonomy in stages, tying each permission to a specific action and a verifiable outcome. A broad setting such as “enable autonomous networking” is less useful than defining which change is allowed, under what conditions, for which part of the environment, and how an operator can stop or reverse it.
- Observe first. Connect the relevant telemetry and let the system identify patterns or recommend actions without changing production. Check whether its view covers the network, applications, security, cloud paths, and user experience involved in the service.
- Validate recommendations. Compare the explanation with source events, topology, policy, and recent changes. Record when recommendations are useful and when they are wrong or incomplete.
- Set action-specific boundaries. Define allowed changes, affected systems, thresholds, and conditions that require human approval. Use role-based access so the system has no broader permissions than its assigned tasks need.
- Require evidence and accountability. Keep an audit trail of the rationale, inputs, approvals, action, and result. Make the action visible to operators and preserve an emergency override.
- Verify the outcome before expanding. Check whether service health improved and whether the change caused side effects. Increase autonomy only where the team can demonstrate reliable results and recover from failures.
The survey summary says 69% of respondents require detailed explainability for agent-driven actions, while 36% require full observability, including detailed tracing, summarized rationale, and post-action audits. These are stated requirements among respondents, not proof that systems consistently meet them. Cisco’s summary reports the survey figures; the practitioner article outlines operational controls.
What should you look for in an AIOps platform?
Evaluate a platform against the environment and operational decisions it must support. The criteria below help distinguish a useful operational capability from a convincing demo; they are not a ranking of vendors.
- Cross-domain coverage: Can it use relevant network, application, security, cloud, and user-experience context, or does it operate mainly within one silo?
- Telemetry integration: Can it work with the tools and data your team already depends on? Identify integration effort, gaps, and maintenance needs.
- Evidence behind explanations: Can operators trace a recommendation to the signals and relationships that informed it, and see what remains uncertain?
- Granular policy controls: Can you restrict actions, set approval thresholds, apply role-based access, and interrupt activity quickly?
- Audit and verification: Does it preserve the rationale and action history, and help confirm whether an intervention improved service or introduced a new problem?
- Fit with operating practice: Can your team define ownership, escalation, rollback, and emergency procedures for the workflows it will automate?
- Measurable outcomes: Establish a baseline for investigation workload, alert handling, service impact, and change results. Measure those outcomes in your environment instead of assuming savings from deployment.
Can AI make network operations create more traffic?
AI is both an operations tool and a source of network demand. Cisco said its aggregated direct-to-AI telemetry analysis showed traffic on a trajectory to double every six months. Cisco also reported testing in which tasks performed by agents generated up to 450% more total network traffic when agentic AI traffic was included. These are Cisco’s analysis and test results as described in its announcement, not independently verified universal benchmarks or forecasts for every organization. The announcement explains Cisco’s figures and their attribution.
The operational implication is to include AI-related flows in capacity and performance planning where they apply. A tool that helps manage the network does not remove the need to understand the traffic its own workloads generate.
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