AIOps is strongest at turning large streams of IT alerts and telemetry into connected incident context: it can group related events, flag unusual behavior, and guide or automate parts of a response. It is not a substitute for complete, reliable data or careful implementation. Its results depend on what systems it can see, how well those systems are connected, and how much tuning and oversight the organization provides.
What AIOps does—and what it does not promise
Gartner defines AIOps as combining big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination. That definition is quoted in Cisco DevNet’s AIOps overview. In practice, AIOps tools analyze operational data such as alerts and telemetry to help teams identify incidents and decide what to do next.
The category describes a set of capabilities, not a guaranteed outcome. A platform may surface a likely relationship between events or recommend a response, but that does not establish that every alert will be correctly grouped, that downtime will fall, or that a team can safely remove human review.
Three areas where AIOps can excel
1. Correlating related events to reduce alert noise
When monitoring tools generate many alerts across different parts of an environment, some may be symptoms of the same underlying issue. Gartner’s public 2024 AIOps platform criteria abstract describes capabilities including cross-domain ingestion, topology generation, event correlation, incident identification, and remediation augmentation. By relating telemetry and events through timing and system topology, an AIOps platform is intended to help teams see a connected incident rather than treat every downstream alert as separate.
#1 Best Overall
The value is clearer incident context and less manual sorting—not a universal percentage reduction in alerts. Whether correlation is useful depends on the platform’s data coverage and whether it understands the relationships among affected systems.
2. Detecting deviations and adding operational context
AIOps can establish dynamic baselines to help distinguish unusual behavior from normal variation. Cisco’s overview describes combining signals into predictive alerts, correlations, and root-cause analysis. This can help an operations team notice a developing issue that a fixed threshold or an isolated alert might not explain on its own.
Rank #2
A detected deviation is a signal to investigate, not proof of a cause. The quality of the analysis depends on the telemetry available and the context attached to it; a platform cannot reliably interpret systems it does not observe.
3. Guiding or automating parts of incident response
AIOps tools can connect detection to response through suggested workflows, tickets, notifications, or automation. Cisco describes machine-reasoning suggestions that may help a less-experienced responder follow remediation steps. That is an example of how guidance can be used, not independent comparative testing that proves a particular speed or performance gain.
Rank #3
Organizations can choose how much authority to give automation. A suggested action or ticket leaves a person in control; an automated change can act more quickly but needs suitable safeguards, approval rules, and a way to handle exceptions.
Two areas where AIOps still falls short
1. It cannot analyze data it cannot access or trust
AIOps does not automatically solve fragmented monitoring or poor-quality telemetry. Cisco notes that it can improve analysis for a given dataset but cannot itself overcome data silos; incomplete observability leaves parts of the infrastructure effectively unobserved. Missing, inconsistent, or poorly contextualized data can therefore limit both detection and correlation.
Data readiness is a real implementation concern, though survey findings should be read in context. In a Riverbed-published survey released in 2025, 46% of respondents said they were fully confident in their data’s accuracy and completeness. That measures respondents’ confidence, not an independent audit of their data. Riverbed reported that Coleman Parkes Research conducted the survey in July 2025 among 1,200 business decision-makers, IT leaders, and technical specialists in seven countries.
2. Deployment and upkeep can be demanding, and correlation can miss change
Connecting sources, building useful dependency context, calibrating detections, and maintaining integrations require ongoing work. Dynatrace’s vendor-authored discussion says traditional correlation-based approaches may require extensive data and manual tuning, and can struggle as systems change. Treat that as a vendor perspective on a class of methods, not a limitation that applies identically to every AIOps product.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Riverbed’s 2025 survey offers a separate snapshot of implementation maturity: 12% of AI projects had reached full enterprise-wide deployment. This figure concerns AI projects broadly, not the share of AIOps installations. The same Riverbed release reported that 87% of respondents said ROI on AIOps initiatives met or exceeded expectations; it is a vendor-published survey result, not proof that AIOps caused those outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess an AIOps platform
Evaluate whether the system fits your environment and operating practices, not just whether it advertises machine learning or automation. Gartner’s listed platform capabilities, data-readiness concerns raised in Riverbed’s survey, and Dynatrace’s account of tuning challenges point to several practical questions:
- Telemetry breadth and quality: Which monitoring domains and systems can it ingest? Are the data consistent, timely, and sufficiently complete for the incidents you need to detect?
- Topology and dependencies: Can it represent relationships among services and infrastructure, and does that context stay useful as the environment changes?
- Correlation and incident identification: Can teams inspect why events were grouped and distinguish a suspected relationship from a confirmed cause?
- Integrations: Does it work with the monitoring, ticketing, notification, and response tools already in use?
- Remediation controls: Can you set human approval, limits, and exception handling for actions that change systems?
- Ongoing effort: What data preparation, tuning, integration maintenance, and operational ownership will be needed after deployment?
For newer approaches using large language models, the evidence base is still developing. A 2025 survey by Lingzhe Zhang and coauthors reviewed 183 research papers published from January 2020 through December 2024 and described LLM applications in AIOps as an emerging area whose impact and limitations are not yet comprehensively understood. The work is available as an arXiv preprint; it should not be read as a proven performance benchmark for commercial platforms.
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