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AI is changing IT operations in two directions: operations teams are using AI to monitor, investigate, and automate work, while also having to operate AI systems as production workloads. The practical shift is not simply fewer manual tasks. It is a greater need to observe changing system behavior, protect the data and permissions that AI-driven tools rely on, and decide which actions still require human approval.
What does AI change in IT operations?
Traditional operations teams already monitor services, investigate incidents, and manage infrastructure. AI can support those activities by helping teams interpret operational data, surface patterns, or assist with routine work. At the same time, AI features and models are becoming systems that operations teams must monitor for availability, performance, accuracy, resilience, and data security.
That two-sided change appears in a 2026 Dynatrace survey of 919 global SRE and platform engineering leaders. Its respondents described both using AI in operational work and monitoring AI systems in production. This is a vendor survey, so its results describe those respondents rather than proving that every organization has adopted AI in the same way.
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In the survey, different percentages refer to different questions or sections; they should not be treated as interchangeable measures of AI use.
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| Reported measure | What the figure describes |
|---|---|
| 58% of SRE respondents | Named monitoring AI systems for performance, accuracy, resilience, and data security as their top AI use. Dynatrace, 2026. |
| 67% of SRE respondents | Identified monitoring AI models as their leading use case in the report’s SRE-practices section. This is a separate reported measure from the 58% figure. Dynatrace, 2026. |
| 55% of platform engineering respondents | Said their predominant AI use was providing developers access to AI-powered tools, alongside monitoring AI tools. Dynatrace, 2026. |
| 64% of respondents | Said most or all IT operations processes were fully automated. This reports respondents’ stated automation maturity; it does not show that AI caused that automation. Dynatrace, 2026. |
AI in operations, often discussed under the term AIOps, is not one specific product or level of autonomy. It can mean AI-assisted analysis, AI features embedded in operational tools, or systems that propose or execute actions. A 2025 review in ACM Computing Surveys analyzed 183 research articles published from January 2020 through December 2024 on AIOps in the era of large language models. That volume of research indicates an active field, not that one settled operating model has emerged. ACM Computing Surveys, 2025.
Why does operating AI itself require new monitoring?
AI systems can behave probabilistically: the same system may not produce identical outputs for every similar input. Their behavior can also change as models, data, prompts, connected tools, or operating conditions change. Some problems may appear as degraded quality or unexpected outputs rather than a conventional outage, so checking a system before release is not enough to establish that it will remain reliable in production.
NIST’s March 6, 2026 report explains that post-deployment monitoring helps validate real-world reliability, track unforeseen outputs caused by factors such as model non-determinism or changing inputs, and reveal unexpected consequences in deployment contexts. In operational terms, teams need visibility into both the service around a model and the model’s behavior in its actual use. NIST, “Challenges to the monitoring of deployed AI systems”.
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This monitoring practice is still developing. NIST says terminology, validated methods, and best practices remain nascent and scattered, and identifies obstacles that include performance degradation and fragmented logging. Teams should therefore treat their monitoring approach as an evolving operational capability, not assume that a single metric or standard checklist will cover every AI system.
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Can AI automate incident response?
AI can assist parts of incident response, but a capability to suggest an action is not the same as a safe basis for allowing an agent to execute it. The level of autonomy should reflect the potential impact of an action, the reliability and provenance of the signals behind it, and the ability to reverse the change.
A USENIX Security ’26 paper describes a specific risk in the setting it studied: manipulating operational telemetry can mislead LLM-driven IT operations agents into actions that compromise infrastructure. The paper establishes a security concern, not how often this occurs across deployed products. It makes telemetry integrity an operational design issue, alongside access control and approval boundaries. USENIX Security ’26, “When AIOps Become ‘AI Oops’: Subverting LLM-driven IT Operations via Telemetry Manipulation”.
Set autonomy according to the consequences
- Analysis: Let AI summarize or group evidence, while an operator evaluates what it means.
- Recommendations: Have AI propose a response, but require an authorized person to approve consequential changes.
- Execution: Restrict automatic actions to defined, bounded cases with least-necessary permissions, trustworthy inputs, and a tested rollback path.
These are operational guardrails, not a guarantee of safety. A workflow should make clear which data an agent can read, which systems it can change, which actions need approval, and how operators can interrupt or reverse execution.
Are the benefits consistent?
Not according to the Dynatrace survey. Its 2026 report says AI largely meets respondents’ expectations for reliability and developer productivity, but falls short of expectations for cost reduction and faster incident detection and resolution. This is a finding about the survey’s respondents, not evidence that AI will produce the same outcomes in every IT organization. Dynatrace, 2026.
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Adoption and reported automation do not by themselves establish savings or shorter incidents. To assess a local rollout, compare outcomes against a baseline that matters to the team, such as service reliability, time spent investigating, the quality of alerts, or the rate of unintended changes. Keep the chosen measures distinct from activity measures such as how many tools or workflows use AI.
Adoption is also visible in public-sector inventories, though those figures have a narrower scope. In 11 selected U.S. federal agency inventories, the Government Accountability Office reported that 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 are reported use cases in selected agencies, not a measure of outcomes or adoption across all government agencies. GAO, July 29, 2025.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should an operations team introduce AI?
- Choose a bounded workflow. Identify a specific operational task and define the result it should improve. Begin with assistance or recommendations when the impact of an incorrect action would be high.
- Establish observability. Decide what signals reveal service degradation, model behavior changes, unexpected outputs, and the consequences of actions. Record enough context to investigate without assuming that fragmented logs will tell the whole story.
- Protect the inputs and permissions. Check the origin and integrity of telemetry, limit access to the systems and data needed for the task, and separate recommendation rights from execution rights where appropriate.
- Define human checkpoints and recovery. Specify which actions need approval, how an operator can stop an agent, and what rollback or remediation process applies if an action has an unintended result.
- Evaluate in operation. Compare actual outcomes with the baseline, review failures and unexpected behavior, and adjust the workflow, monitoring, and permissions as the system changes.
For a framework to organize risk-management work, NIST’s AI Risk Management Framework is voluntary. NIST says the framework is being revised, so readers should check its current status rather than treat version 1.0 as final or mandatory guidance. A framework can structure decisions, but it does not resolve the monitoring gaps NIST identifies. NIST AI Risk Management Framework.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhat should teams compare when choosing an approach?
There is no neutral vendor-to-vendor comparison established by the sources cited here. Teams assessing an internal build or an operational tool can instead compare how well each approach addresses the requirements that determine operational risk:
Quick Recap
- Visibility: Can the team observe service health and AI behavior after deployment, including changes that may not appear as a conventional outage?
- Detection: How does it identify model behavior changes, unexpected outputs, and service degradation, and can operators investigate the signals behind an alert?
- Automation boundaries: Which actions can run automatically, which require human approval, and how are those rules enforced?
- Telemetry integrity: Can the team establish where operational data came from and detect or limit manipulation of the signals an AI system uses?
- Permissions and recovery: Are access rights appropriately limited, and can the team interrupt or roll back consequential changes?
- Workflow fit: Does the approach work with existing operations processes without obscuring responsibility for an incident or change?
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