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AIOps projects are most likely to stall when operational data is not ready, the use case has no clear value, or the technology cannot fit into existing workflows and governance. The six hurdles below are a practical synthesis of current evidence—not a canonical Gartner framework or a universal list of causes. Several cited statistics concern AI generally, not AIOps deployments specifically, so treat them as context rather than AIOps failure rates.
1. Unready, fragmented, or inaccessible data
AIOps depends on reliable signals from the systems it is meant to monitor. Logs, metrics, traces, events, configuration data, and service context may be split across tools, retained for different periods, labeled inconsistently, or inaccessible to the team building the system. Gaps or stale signals can undermine detection and diagnosis before a model or platform has a chance to help.
In Gartner’s 2025 survey of AI implementation barriers, conducted in Q4 2024, 34% of leaders in low-AI-maturity organizations and 29% in high-maturity organizations named data availability and quality among their top challenges. This was a survey about AI implementation broadly, not a measurement of AIOps deployments. In a separate 2026 Gartner survey of 782 infrastructure and operations (I&O) leaders, fielded in November and December 2025, 38% of respondents reporting AI setbacks cited poor data quality or limited data availability as a direct cause. These are respondents’ reported reasons, not results from a causal experiment. Gartner’s 2025 AI implementation findings; Gartner’s 2026 I&O findings.
Prepare the data before promising automated diagnosis
- Inventory the telemetry sources the use case needs, including who owns each source and how it relates to services and infrastructure.
- Check completeness, consistency, timeliness, labeling, retention, and missing data; note where events or service context cannot be joined reliably.
- Confirm that the intended platform and operators can access the data under approved permissions and retention rules.
- Start with a bounded service or problem area if a full-estate data inventory is not yet practical.
2. Security, privacy, and governance constraints
Operational telemetry can reveal system architecture, user activity, incident details, and other sensitive business information. Routing it to a new analytics service or using it to trigger actions introduces questions about access, retention, auditability, and who is accountable when an automated recommendation is wrong.
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Gartner’s Q4 2024 survey found that 48% of leaders in high-AI-maturity organizations named security threats among their top three AI implementation barriers. That figure describes respondents to a general AI survey; it is not an AIOps-specific rate. Gartner’s 2025 AI implementation findings.
Questions to settle before connecting data or enabling action
- Which telemetry is necessary for the use case, and can data minimization reduce exposure?
- Where is data processed and retained, and which roles or services can access it?
- Can operators audit what information informed an alert or recommendation?
- Which actions may run automatically, and which require human review or approval?
- How can an operator stop or reverse an action if it produces an unexpected result?
These are practical governance checks, not controls whose individual impact is quantified by the cited survey.
3. Choosing a use case with real operational value
A convincing demonstration does not establish that a use case solves a frequent, expensive, or actionable operational problem. A system may surface anomalies that teams cannot investigate, address an edge case with little service impact, or automate a response that operators would not trust.
Rank #2
In Gartner’s 2025 general AI implementation survey, 37% of leaders in low-AI-maturity organizations named finding the right use case as a top barrier. In its 2026 I&O findings, Gartner says aligning AI use cases with real operational needs is a success factor. Neither finding supplies a universal formula for selecting an AIOps use case. Gartner’s 2025 AI implementation findings; Gartner’s 2026 I&O findings.
Rank candidate use cases against the operating reality
As an editorial decision aid, score each candidate against the same questions before committing to a pilot:
- Frequency and impact: How often does the problem occur, and what service or business harm does it cause?
- Signal quality: Are the relevant events and telemetry available, timely, and sufficiently consistent?
- Actionability: Can a team or existing system take a useful next step when the tool identifies the problem?
- Automation risk: What is the cost of a false alert or an incorrect action, and where is human approval needed?
- Baseline: What current outcome can be measured so the pilot can be evaluated fairly?
This ranking approach is practical guidance, not a quoted Gartner model. A narrow use case with reliable signals and a clear response path is generally easier to evaluate than an ambition to automate operations across an entire estate at once.
Rank #3
4. Proving value and sustaining funding
AIOps teams need to show more than model activity or alert volume. They need a baseline and measures that connect a technical change to service performance or a business outcome the organization values. Without that link, a pilot can appear promising while decision-makers remain unable to judge whether it merits further investment.
Gartner reported that 30% of surveyed chief data and analytics officers said inability to measure the business impact of data, analytics, and AI was their top challenge. That survey was conducted from September to November 2024 among 504 global data and analytics leaders. In a separate 2024 AI survey, difficulty estimating and demonstrating project value was the primary obstacle to AI adoption for 49% of participants. These figures come from different survey populations and should not be combined or presented as AIOps-specific findings. Gartner’s 2025 D&A leadership findings; Gartner’s 2024 AI value findings.
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- Record an agreed baseline before deployment, using the same definitions and measurement window that will be used later.
- Track relevant operational measures, such as time to detect, time to restore, alert quality, or repeat incidents.
- Choose a business measure that fits the use case, such as service availability or the cost of disruption, rather than assuming every technical improvement creates business value.
- Account for implementation and ongoing operating costs when assessing whether benefits justify continued funding.
These are candidate measures, not promised outcomes: an AIOps deployment cannot be assumed to improve them without evidence from the organization’s own baseline and results.
5. Integrating with existing tools and workflows
An insight is useful only if it reaches a person or system that can act on it with the right context. If alerts are detached from service ownership, incident processes, change history, permissions, or escalation paths, teams may have to rebuild context manually—or ignore the output.
Gartner’s 2026 I&O findings identify embedding AI into systems and processes people already use as a factor associated with successful I&O use cases. Gartner’s observability Hype Cycle abstract also recommends assessing integration opportunities and aligning initiatives with business value. Gartner’s 2026 I&O findings; Gartner’s observability Hype Cycle abstract.
Check the full path from signal to response
- Can the platform connect to the telemetry and infrastructure systems relevant to the use case?
- Does it preserve service context and work with the team’s incident and change workflows?
- Do permissions allow the right people to see, investigate, and respond to recommendations?
- Are escalation, human approval, and rollback paths clear before automated actions are enabled?
- Can operators understand and trace why an alert or recommendation was produced?
When comparing platforms or implementation approaches, assess integration coverage, support for on-premises, cloud, and hybrid environments, explanation and traceability, security and data handling, workflow controls, measurable outcomes, total operating cost, and the skills required to own the system. These are decision axes inferred from operationalization concerns; the cited Gartner abstracts do not rank vendors on them.
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6. Skills, operating model, and organizational adoption
AIOps is not a one-time software installation. Teams need the ability to interpret outputs, maintain data and integrations, manage models where applicable, govern automated actions, and improve the process as services change. If infrastructure, data, security, and service teams do not agree on ownership, the pilot can be difficult to support after its initial team moves on.
In Gartner’s 2026 I&O survey, 38% of leaders reporting AI setbacks cited persistent skill gaps as a direct cause. Gartner also identifies leadership support and cross-functional collaboration as success factors. Separately, Gartner’s 2025 operationalization report says DataOps, MLOps, and ModelOps can improve collaboration and deployment, while a crowded range of offerings can create confusion. Gartner’s 2026 I&O findings; Gartner’s 2025 operationalization report.
Make ownership explicit
- Name who owns the use case, data sources, platform integrations, security review, and day-to-day operational response.
- Decide how operators will validate recommendations and report false positives, missed incidents, or unsafe actions.
- Plan for ongoing training and support across the teams that maintain and use the system.
- Clarify whether the organization’s operating model needs DataOps, MLOps, ModelOps, or another defined set of responsibilities; do not assume a product label resolves ownership.
Gartner’s exact wording is: “Adopting DataOps, MLOps and ModelOps can enhance collaboration, streamline deployment and improve scaling of AI initiatives.” This is a broad AI operationalization statement, not a guarantee that adopting those practices will solve a particular AIOps deployment. Gartner, 26 February 2025.
Why AIOps pilots can fail to become sustained operations
The hurdles reinforce one another: weak telemetry makes outputs harder to trust; unclear value makes it harder to fund the integration and staffing work; and unclear ownership leaves useful insights without a reliable response path. Gartner reported that 28% of AI use cases in I&O fully succeeded and met ROI expectations, while 20% failed outright, in its survey of 782 I&O leaders fielded in November and December 2025. Those figures describe the survey’s reported AI use cases in I&O, not a universal AIOps success or failure rate. Gartner’s 2026 I&O findings.
As Gartner put it in that release: “ROI from AI is not driven by the sophistication of the model, but by how well the technology is integrated, governed, and aligned with real operational needs.” The practical implication is to evaluate readiness, workflow fit, governance, skills, and evidence of value alongside model or product capability—not after the pilot has already been chosen. Gartner, 7 April 2026.
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