What is AIOps? Examples & How To Implement

IT teams are facing a tidal wave of operational data from apps, devices, cloud services, and endpoint systems. Traditional monitoring and incident response methods weren't built for this scale or speed. That's where AIOps (artificial intelligence for IT operations) comes in.

By combining machine learning, big data analytics, and automation, AIOps helps teams reduce noise, spot anomalies in real time, and act before people even notice there’s a problem. It’s more than just another tool—it helps teams manage complex, hybrid IT environments with greater efficiency and fewer manual interventions.

In this guide, we’ll break down what AIOps is, how it works, and where it can make a real difference in your operations. You’ll see practical examples, a side-by-side look at AIOps and DevOps, and a simple, step-by-step path to implementation. No hype—just useful guidance to help your team reduce noise, spot issues earlier, and stay focused on what matters.

What is AIOps?

AIOps, also known as artificial intelligence for IT operations, uses machine learning and data analytics to monitor systems for detecting and responding to issues, often without human intervention.

In traditional IT operations, teams rely on predefined alerts, manual investigation, and delayed responses. The reactive model struggles with today’s growing volume of data and complexity.

AIOps shifts this approach. Instead of reacting to problems after they happen, AIOps tools can analyze thousands of data points in real time, identify patterns, and automatically highlight—or even resolve—potential issues before they impact employees, customers, or business operations.

It doesn’t replace your IT team. It gives them enhanced context, more immediate insights, and fewer repetitive tasks so they can focus on higher-value work and keep systems running smoothly.

Types of AIOps

AIOps platforms generally fall into one of two categories. Each has a different role in how operational data is analyzed and acted on:

Domain-centric 

These tools are designed for a specific area of IT, such as network monitoring, log management, or infrastructure performance. They offer deep visibility within a single system but often operate in silos, making it harder to share insights across teams or correlate events across different tools.

Domain-agnostic

These platforms integrate data from multiple sources—servers, applications, endpoints, and more—and apply machine learning across the entire environment. They create a centralized view of operations, helping teams identify patterns and surface issues that span across domains.

Domain-agnostic AIOps supports increased scalability and cross-functional collaboration by connecting disparate data sources and turning them into actionable, visual insights. This broader visibility helps teams make timely, coordinated decisions based on a shared understanding of what’s happening across systems.

AIOps components

Behind every successful AIOps platform is a set of foundational components that work together to transform fragmented operational data into clear, actionable information. 

Here’s a breakdown of what powers AI for IT operations:

Machine learning and big data

At the heart of every AIOps platform is machine learning fueled by big data. These models analyze vast amounts of operational data to recognize patterns, detect anomalies, and predict future issues far beyond what people could do manually.

Algorithms and automation

Algorithms drive how alerts are detected, correlated, and prioritized. Paired with automation, they help teams go from insight to action—whether that means suppressing redundant alerts, triggering a workflow, or initiating self-healing steps.

Analytics and visualizations

Dashboards and visual reporting make insights accessible to both technical and non-technical teams. Instead of sifting through logs, teams get clear, visual explanations that help them respond to incidents with greater clarity and make informed decisions based on current data.

Data ingestion and pipelines

AIOps tools rely on clean, timely data from diverse sources: logs, metrics, traces, events, and more. Strong data pipelines ensure the right information flows in at the right time to power everything else.

Each of these components helps shift IT teams from reactive troubleshooting to proactive, data-informed operations. But even the best AIOps tools depend on one thing: reliable data. If the inputs are incomplete, inconsistent, or disorganized, the insights will be too. The quality of your data preparation directly shapes the accuracy and usefulness of your AIOps outcomes.

How AIOps works

AIOps turns raw operational data into automated insights by following a continuous cycle of intake, analysis, and action. Here’s how the process typically unfolds:

1. Data ingestion
   Logs, metrics, traces, and events flow in from across your infrastructure—cloud platforms, on-prem systems, applications, and devices. This step depends on strong data pipelines to bring everything together in real time.
2. Correlation
   The platform identifies relationships between data points. For example, it might link a CPU spike on one server to a slowdown in a dependent application, reducing alert noise and isolating root causes.
3. Analysis
   Using machine learning algorithms, AIOps platforms detect anomalies, surface trends, and prioritize what matters most. The goal is not just to report issues but to anticipate them.
4. Action
   Depending on the setup, the system can notify the right teams, trigger workflows, or even resolve incidents automatically.

Over time, feedback loops improve the model’s accuracy. As the system learns from historical data and human input, it fine-tunes what gets flagged—and what doesn’t.

This end-to-end flow allows IT teams to respond more effectively with less guesswork and more confidence.

Top benefits of AIOps

AIOps isn’t just another layer in your tech stack—it’s a fundamental shift in how IT operations teams detect, understand, and act on system events. As environments grow more complex and the volume of machine data expands, traditional monitoring can’t keep up. AIOps fills the gap with automation, intelligence, and scale.

Whether you’re looking to improve uptime, reduce incident noise, or free up your engineers for more strategic work, here’s how AIOps translates into tangible impact.

Shorter incident response times

By automating alert triage and surfacing the most relevant issues, AIOps helps teams respond to incidents with greater efficiency. As a result, teams often see a notable reduction in mean time to resolution (MTTR).

Reduced operational costs

Through automation of repetitive tasks—like log parsing, correlation, and basic remediation—AIOps reduces manual workload. Increased automation leads to lower support costs and frees up engineering resources for strategic projects.

Fewer outages and service disruptions

By detecting anomalies before they escalate, AIOps helps prevent outages altogether. It can improve uptime and reduce the impact of downtime, especially in customer-facing applications.

Accelerated root cause analysis

What once took hours of log review can now take minutes. AIOps platforms can correlate events across systems, helping teams pinpoint the root of a problem with greater accuracy and context.

Broader data access across teams

AIOps platforms often include dashboards and visual tools that make operational data accessible to non-specialists. This data democratization empowers operations and engineering teams to make informed decisions without needing to dig through raw logs or custom scripts.

The takeaway: AIOps drives real results by improving visibility, speeding up resolution, and reducing the cost and complexity of modern IT operations.

AIOps vs DevOps

AIOps and DevOps are often mentioned in the same conversation, but they’re not interchangeable. Each plays a distinct role in modern IT operations, and when used together, they enhance one another.

DevOps is a cultural and process-driven shift that brings development and operations teams together. It emphasizes collaboration, continuous delivery, and more frequent, reliable software releases. DevOps isn’t just about tools—it’s about how teams work: agile, iterative, and aligned.

AIOps, on the other hand, adds a layer of intelligence and automation to the operational side. It uses machine learning, analytics, and automation to detect issues, reduce noise, and improve how teams manage incident response. AIOps doesn’t change how teams deploy code—it enhances how they monitor, maintain, and scale their systems once that code is live.

Here’s how the two approaches compare:

‍

Used together, AIOps can enhance DevOps practices by improving observability, automating routine tasks, and identifying patterns that human teams might miss. For example, AIOps can analyze performance data across deployments, flag recurring issues, and even trigger workflows that align with continuous integration pipelines.

The result? DevOps teams spend less time reacting and more time building. With AIOps in place, they gain the operational visibility and automation to maintain stable, reliable systems as they scale.

Examples and use cases of AIOps

AIOps adapts to the specific pressures of different sectors, helping teams automate decisions, reduce risk, and stay ahead of disruption. Here are standout examples across industries:

IT operations: Predictive incident management

• Early failure detection across hybrid systems: AIOps monitors infrastructure logs and usage trends to predict hardware failures, helping IT teams replace components before performance dips.
• Dynamic alert suppression: Instead of sending a flood of tickets during a single outage, AIOps recognizes root causes and suppresses duplicate alerts—saving time and reducing alert fatigue.
• Runbook automation for recurring issues: When routine problems are detected, like memory leaks or queue delays, AIOps can trigger a predefined fix, eliminating wait time for human intervention.

Retail: Demand forecasting for infrastructure scaling

• Promo-aware scaling: AIOps learns from historical sales, marketing calendars, and traffic patterns to automatically scale ecommerce systems ahead of known surges like Black Friday or flash sales.

• Inventory-driven resource optimization: By analyzing point-of-sale (POS) and warehouse feeds, AIOps adjusts cloud computing capacity to match inventory activity and reduce over-provisioning costs.

Finance: Fraud detection via anomaly spotting

• Behavior-based transaction flagging: AIOps identifies deviations in customer transaction habits (e.g., new login locations or out-of-pattern transfers) and escalates only those that meet risk thresholds.
• Compliance automation: When suspicious activity is detected, AIOps can initiate reporting workflows automatically—accelerating audit readiness.
• Payment processing diagnostics: AIOps flags delays in processing times across payment gateways, enabling operations teams to intervene before SLA violations occur.

Healthcare: System uptime for patient-critical apps

• Clinical app monitoring: AIOps tracks latency and usage across EHR systems and notifies IT before physicians experience lag, thereby preserving continuity of care.
• Device fleet health checks: From nurse stations to diagnostic machines, AIOps evaluates endpoint performance to reduce downtime across medical hardware.

These use cases highlight how AIOps adapts to real-world challenges, helping teams anticipate problems, act sooner, and maintain the systems their organizations rely on every day.

How to implement AIOps: A step-by-step guide

Bringing AIOps into your existing operations doesn’t require a full system overhaul. It starts with a few strategic steps and a realistic scope. Here’s a practical path to getting started:

1. Audit your current toolchain and data flow

Start by mapping out what tools you're using across infrastructure, application performance, incident response, and ticketing. Identify where your monitoring overlaps, where visibility is limited, and which data sources are disconnected or duplicated.

2. Define clear goals

Set specific, measurable objectives. Common goals include reducing mean time to resolution (MTTR), eliminating alert fatigue, consolidating observability tools, or improving uptime for key systems. These goals will inform how you measure ROI.

3. Choose the right platform

Your AIOps platform should integrate cleanly with your existing environment and unify data from multiple sources. Prioritize solutions that support real-time ingestion, customizable workflows, and visual analytics. Platforms like Domo—built for data integration and cross-team visibility—can provide the foundation AIOps needs to perform.

4. Start small with a pilot

Begin with one team, system, or workflow. A focused rollout helps you identify what works, what to refine, and how to scale responsibly. Look for early wins that show tangible impact.

5. Build feedback loops

AIOps isn’t set-and-forget. Gather feedback from the teams using it to refine thresholds, filters, and automated responses. Loop in historical data to train models and improve decision accuracy over time.

6. Scale gradually

Once the pilot proves valuable, expand to other environments and use cases. Formalize documentation, training, and governance so AIOps scales alongside your team’s needs.

Tips for success

• Clean up first: Automating noisy, disorganized inputs won’t yield meaningful results.
• Engage early: Involve stakeholders across IT, security, and engineering.
• Plan for iteration: AIOps is an evolving practice and continual tuning should be part of the process.
• Measure and adapt: Revisit your goals regularly to track progress and refine priorities.

Bring AIOps to life with Domo

AIOps isn’t just about reducing alerts or automating tasks; it’s about helping teams make timely, informed decisions using the data they already have. By connecting signals across tools, surfacing issues early, and minimizing manual triage, AIOps brings clarity and control to complex IT environments.

With Domo’s AI solutions, you can take a practical approach to AIOps, one rooted in unified data, transparent insights, and automation that’s both flexible and focused. See how Domo helps IT teams manage operations with more context, less noise, and greater confidence—powered by the data you already have.