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AIOps is moving network operations beyond scripted responses toward systems that can analyze events, recommend changes and, in some cases, act on them. But 2026 survey findings show a gap between interest in AI-enabled networking and confidence in letting AI make production decisions. For network teams, the practical question is not whether a system is called “agentic”; it is what it can see, what it may change, how its decisions are checked and how operators can recover control.
What AIOps changes in network automation
Traditional network automation applies defined rules to repeatable tasks: for example, enforcing a configuration standard or carrying out a known response when a condition is met. AIOps adds AI-assisted analysis of network data to help identify anomalies, connect related events, diagnose likely causes and recommend responses. Agentic NetOps extends this approach toward AI agents that can take actions within assigned goals and controls.
These capabilities form a chain, not a single switch from manual work to autonomy. Success at one stage does not establish that the whole chain can safely manage a production network.
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1. Collect telemetry and detect anomalies
The system needs useful data from relevant network segments and connected environments. It can then flag behavior that differs from a baseline or expected pattern. Missing or inconsistent visibility limits what any later analysis can reliably conclude.
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2. Correlate events and diagnose likely causes
AI-assisted analysis can help connect signals that might otherwise appear to be separate incidents and suggest a likely cause. A diagnosis is still a hypothesis to evaluate; it is not proof that the system has identified the root cause correctly.
3. Recommend or generate a change
A system may propose a configuration change or response based on its diagnosis. Teams need to know what evidence supports the proposal, what outcome is expected and what conditions would make the change unsafe.
4. Execute, verify and recover
Execution introduces operational risk. A production workflow should verify whether the intended result occurred, detect an adverse effect and provide a way to stop or reverse the change. A tool that can generate or apply a change is not, by that fact alone, a proven end-to-end autonomous operator.
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What 2024–2026 findings say about adoption and trust
The figures below describe forecasts, survey responses and expectations—not independently verified rates of successful autonomous operation. Broadcom’s 2026 report presents Dimensional Research surveys conducted in 2024 and 2025; Cisco’s September 2026 announcement summarizes independent Omdia research. Those distinctions matter when interpreting the numbers.
| Evidence | Reported finding | What it indicates |
|---|---|---|
| Gartner forecast, September 2024 | Gartner forecast that 30% of enterprises would automate more than half of their network activities by 2026. Gartner’s announcement | This is a forecast made in 2024, not evidence that the target was reached in 2026. |
| Dimensional Research surveys, 2024 and 2025; reported in Broadcom’s report dated November 2025, based on more than 1,350 global IT professionals with networking, operations or cloud responsibilities | 92% planned to use AI-enabled networking solutions; 71% did not fully trust AI to make network operations decisions; 37% reported mature automation practices; 95% lacked visibility into network segments. The report also identifies cloud and internet environments as visibility challenges. The State of Network Operations, 2026 | Interest in adoption coexists with trust, automation-maturity and visibility constraints. The survey is not a census and does not demonstrate that these conditions caused particular outcomes. |
| Independent Omdia survey of 1,000 IT and network operations leaders in organizations with 500 or more employees; summarized by Cisco on September 23, 2026 | 82% said they were comfortable with AI making at least some production network changes without prior approval; 84% expected an AI-led operating model within 12 months; 86% considered a single integrated platform the most effective path; 69% required detailed explainability for agent-driven actions. Cisco’s summary of the Omdia survey | These are respondents’ reported attitudes and expectations, not verified deployment results. Willingness to allow some actions without prior approval does not mean unrestricted autonomy. |
| EMA survey of 458 IT professionals in North America and Europe; summarized by Broadcom on January 30, 2026 | 93% had allocated budgets for AI-driven NetOps; 35% reported complete success with AI-driven NetOps; 47% of users frequently encountered false or mistaken AI insights requiring human oversight. Broadcom’s summary of the EMA survey | Budget allocation is not proof of successful deployment. The reported mistaken insights make human review and continuing accuracy checks relevant operational safeguards. |
In its September 2026 announcement, Cisco quoted Jim Frey, Omdia’s Chief Analyst for Network and IT Operations: “The findings point to a broader shift from the advisory model of AIOps to agent-powered operations, AgenticOps, that can take action.” The statement describes a direction of travel; it does not establish that AI-led operation is reliable across networks or use cases.
Gartner’s January 2026 abstract, The Future of NetOps Is Agentic, likewise urges infrastructure and operations leaders to act to exploit agent benefits safely. The accessible abstract supports that broad position, not detailed implementation claims beyond it.
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Where automation helps—and where judgment still matters
Deterministic automation and AI-assisted operation solve different problems, and they can be combined. Rule-based automation is suited to repeatable work with clear inputs and outcomes. AI can assist when teams need to sift through complex telemetry, correlate events or investigate possible causes. If an AI-generated diagnosis or change is allowed to affect production, the organization also needs controls for uncertainty, permissions and recovery.
- Good candidates for repeatability: configuration consistency, routine policy application, compliance enforcement and established response workflows.
- Work needing evaluation: probabilistic diagnosis, recommendations based on incomplete telemetry and generated changes whose effects may vary by environment.
- Work requiring explicit authority: production actions that can affect service availability, security boundaries or a wider set of devices than the original incident.
Assign ownership for the policy governing actions, exception review and audit records. Operators should be able to inspect the evidence behind a recommendation, see what action was taken and regain control if results depart from expectations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess an AIOps or agentic NetOps approach
Use questions that test the operating model rather than relying on a product label or a broad promise of autonomy. The cited surveys do not provide a head-to-head vendor assessment or independently benchmark specific products, so these are evaluation criteria, not a ranking.
Visibility and data coverage
- Which network segments and telemetry sources are covered, including cloud and internet paths relevant to your services?
- Can the system correlate data across the domains your operators must troubleshoot, or does it leave important gaps?
- How does it indicate when missing, stale or inconsistent data limits its diagnosis?
Integration and operating scope
- Does it fit the network, cloud and operational tools your team actually uses?
- Can teams define which environments, devices and workflows are in scope?
- What happens when the system cannot confidently associate an event with a permitted workflow?
Explainability and audit
- Can an operator inspect the supporting evidence and rationale for a recommendation or action?
- Are the action, decision context, approval and post-action result recorded for later review?
- Can the team trace a change to the policy and identity that authorized it?
Permission boundaries and human approval
- Can permissions be limited by device, change class, likely impact, time window and approval policy?
- Which actions require approval, and who can change that rule?
- Can operators pause execution or override a decision while an incident is in progress?
Verification and recovery
- How does the system check that a change produced the intended outcome?
- Can the team stop a rollout or reverse a change if service health worsens?
- Can higher-impact changes be tested on a limited scope before wider deployment?
Accuracy monitoring
- How are mistaken insights, rejected recommendations and unexpected outcomes recorded and reviewed?
- How does the team detect changes in model behavior or input quality over time?
- Can production outcomes feed into ongoing evaluation before permissions are expanded?
A controlled path from recommendations to production actions
A cautious rollout lets a team learn what a system can do before giving it broader authority. This sequence is a practical approach, not a universal standard or a guarantee of success.
- Establish visibility and a baseline. Confirm the telemetry available for the workflows under consideration. Record incident volume, time to diagnose, change failure rate and service impact so later claims can be compared against a defined starting point.
- Start with observation and recommendations. Let operators compare system detections and proposed causes with the evidence they already use. Track incorrect insights and recommendations that need correction.
- Test bounded workflows outside production. Use a lab or replay environment to check proposed changes against known scenarios, including failure cases and incomplete data. Define the expected result and the conditions that should stop execution.
- Add approvals for production changes. Specify which actions require human review, who can approve them, what evidence must be visible and which changes are outside the system’s authority.
- Use limited rollouts for changes that can affect availability. Where appropriate, apply canaries and verify service health before extending a change. Make the stop and rollback path clear to the operators responsible for the service.
- Expand permissions only against evidence. Review outcomes, audit records, exceptions and recovery performance. Widen the scope only when the team can inspect decisions and verify results for the workflow in question.
What 2026 adoption claims do not establish
The available figures show forecasts, planned use, budgets, attitudes and reported survey experiences. They do not establish that organizations broadly achieved the 2024 automation forecast, that an AI-led operating model is now the norm, or that autonomous network changes consistently improve reliability, reduce costs or avoid outages. The findings also do not prove that one architecture or platform is best for every organization.
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For a specific network, the decision should therefore rest on demonstrated coverage, workflow-level accuracy, transparent permissions, auditable actions and tested recovery—not on adoption expectations alone.
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