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AI is changing IT operations in two directions: teams are using it to monitor and manage services, and they are taking on the work of operating AI systems themselves. That shift makes observability, reliable telemetry, controlled automation and human oversight more important—not less. Current evidence points to useful applications, but it does not establish universal cost savings or faster incident resolution.
What does AI change in IT operations?
“AIOps” is commonly used for applying AI to IT operations: for example, helping teams interpret operational data, investigate issues or automate work. But the operational picture is broader than using AI as a tool. Teams also need to monitor AI models and services once they are in production, where behavior can change with inputs and conditions.
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| Direction of change | What operations teams do | Key operational question |
|---|---|---|
| AI used in operations | Apply AI to operational workflows and, in some cases, automate tasks. | Can the team verify the system’s inputs, outputs and actions, and intervene when needed? |
| AI operated as a production workload | Monitor AI systems alongside the services and infrastructure they depend on. | How will the team detect unreliable behavior, degradation or unexpected outputs after deployment? |
These directions can overlap: an operations team might use AI to support its work while monitoring an AI-powered service. Dynatrace’s 2026 survey of 919 global SRE and platform engineering leaders describes both monitoring AI systems in production and using AI to manage dynamic workloads.
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How are operations teams using AI?
Monitoring AI systems
In Dynatrace’s 2026 survey, 58% of SRE respondents named monitoring AI systems for performance, accuracy, resilience and data security as their top AI use. In a separate section on SRE practices, the report says 67% identified monitoring AI models as their leading SRE use case. Those figures come from different contexts in the same report; they should not be treated as one measure or combined.
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Providing AI tools to developers
Among platform engineering respondents to the same survey, 55% said their most prevalent use of AI was providing developers access to AI-powered tools, alongside monitoring those tools. This describes reported adoption, not a measured effect on delivery speed or service quality.
Automating operational work
Dynatrace reports that 64% of respondents said most or all IT operations processes were fully automated. That is a report of respondents’ stated automation maturity; it does not show that AI caused the automation. The figure also does not say which processes were automated or how much human review remained.
Why monitoring has to continue after deployment
Pre-release checks cannot establish how an AI system will behave in every real operating context. AI workloads may be probabilistic, drift over time or fail in ways that are not immediately visible. A model can produce an unexpected result without the kind of obvious outage that traditional service monitoring is designed to catch.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteA March 2026 report from NIST’s Center for AI Standards and Innovation describes post-deployment monitoring as important for validating reliability in real-world scenarios, tracking unforeseen outputs and seeing unexpected consequences in deployment contexts. In practical terms, monitoring should connect model behavior to the service’s operational health rather than treating the model as a black box outside the normal production environment.
What monitoring should help teams see
- Service degradation: whether the AI-dependent service is still performing as expected.
- Behavior changes: whether outputs or accuracy have shifted under real-world inputs.
- Resilience and security: whether the system remains dependable and its data remains protected.
- Unexpected consequences: whether a system’s effects in deployment differ from what teams anticipated.
The monitoring discipline is still developing. NIST says shared terminology, validated methods and best practices remain nascent and scattered. Its report also identifies practical barriers such as performance degradation and fragmented logging. Teams therefore need to make monitoring choices in the context of their own systems rather than assume there is one settled standard that covers every AI workload.
Can AI automate incident response?
AI can be used in operational workflows, but the available evidence does not establish that AI-driven automation reliably resolves incidents faster across organizations. Automation should be treated as a spectrum: a system may help surface or interpret information, suggest a response, or be given authority to take an action. Each step toward consequential action raises the importance of guardrails.
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Before delegating an operational action, teams should decide:
- Which actions may run automatically and which require a person’s approval.
- What permissions an AI-driven agent needs, and how to keep them limited to its task.
- Whether the telemetry used to decide on an action is trustworthy and traceable.
- How to stop, review or roll back an action if it is wrong.
- How AI-assisted work fits existing incident workflows and escalation paths.
These are not hypothetical design questions. A paper presented at USENIX Security ’26 describes how telemetry manipulation could mislead LLM-driven IT operations agents into actions that compromise infrastructure. The paper establishes a security concern in the setting it studied; it does not establish how prevalent such attacks are across deployed products. Read the USENIX Security ’26 paper.
Are the benefits consistent?
No. Dynatrace’s 2026 report says respondents found that AI largely met expectations for reliability and developer productivity, but fell short on cost reduction and faster incident detection and resolution. These are the survey’s reported findings, not a universal result or proof that AI caused a particular operational outcome.
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Leadership support was widespread among those surveyed: 92% of SRE respondents reported some executive leadership support, including 55% who reported very strong support. That indicates reported organizational backing, not that every team has the skills, monitoring or controls needed to deploy AI safely.
Other adoption figures have different scopes. The U.S. Government Accountability Office found that, across 11 selected federal agency inventories, reported generative AI use cases rose from 32 in 2023 to 282 in 2024, while total reported AI use cases rose from 571 to 1,110. These figures describe reported use cases in selected U.S. federal agencies, not IT operations adoption across government or the wider economy. The GAO report was published July 29, 2025.
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Compare operational safeguards and workflow fit, not just the number of tasks a system claims it can automate. Useful questions include:
Best Value
- Visibility: Can the team observe relevant service health and AI behavior after deployment?
- Detection: Can it identify changes in model behavior as well as degradation in the service?
- Action boundaries: Is the degree of automation appropriate to the consequences, with human approval where needed?
- Telemetry integrity: Can the team establish where operational data came from and whether it may have been manipulated?
- Recovery: Are permissions limited, and can an action be stopped or rolled back?
- Workflow fit: Does the approach work with existing monitoring, incident response and escalation processes?
A 2025 review in ACM Computing Surveys analyzed 183 research articles on AIOps published from January 2020 through December 2024, reflecting the breadth of active work in the area. It does not, by itself, establish a universal best practice or a vendor ranking. The survey is available through ACM.
What governance can—and cannot—settle
Frameworks can give teams a way to organize risk and trustworthiness work, but they do not remove uncertainty about operational performance. NIST describes its AI Risk Management Framework as voluntary and says the framework is being revised. Its monitoring report also identifies unresolved gaps in terminology, methods and practice. NIST’s AI RMF page provides its current framework information.
For IT operations, governance is most useful when it translates into concrete decisions: what is monitored, who can authorize actions, what evidence is retained, and how people respond when outputs or service behavior depart from expectations.
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