Global data creation will hit 181 zettabytes this year. Enterprise IT systems are responsible for a huge share of this data in the form of logs, metrics, events, performance data, and alerts. The irony? The same teams generating the flood of operational data also struggle to make sense of it.
AIOps is a solution that gives IT the speed and context to process and use data meaningfully, so they can tackle issues before they hit users or service level agreements (SLAs.) This guide breaks down what AIOps really means, why it matters, and where it delivers the most impact. We’ll also explore how monday service helps teams operationalize AIOps in a flexible, user-friendly platform.
Try monday serviceWhat is AIOps?
AIOps, or artificial intelligence for IT operations, is the use of AI, machine learning, and big data to improve and automate how IT teams manage their systems and respond to any issues.
The term AIOps was coined by Gartner in 2017 to describe a new class of tools that analyze large volumes of IT data in real-time. These tools identify problems early so IT teams can take prompt action to resolve them. By doing so, AIOps shifts the focus from manual, reactive work to smarter, proactive operations.
Key components of AIOps
AIOps platforms unite several core technologies to simplify complex IT environments and drive intelligent automation. These key components include:
- Machine learning: A type of artificial intelligence that allows systems to learn from data instead of relying on fixed rules. In AIOps, it identifies patterns, flags unusual behavior, and improves how the system responds over time.
- Data correlation algorithms: A set of calculations that connects the dots across different data types, such as logs and events, to reveal the root cause of any issues.
- Automation engines: The use of software to act on those insights by triggering workflows, resolutions, or escalations without human intervention.
- Visualization and observability tools: The presentation of relevant data in dashboards and reports, to give IT teams a unified view of their systems’ health and service performance.
How do AIOps platforms work?
AIOps platforms take an intelligent approach to managing IT operations. Here’s how they work:
They ingest diverse data at scale
AIOps solutions continuously pull operational data from across the digital environment then consolidate actionable insights into a central system.
Example: Your platform could collect server metrics, application logs, open tickets, and user feedback to create a single operational view.
They filter noise and connect signals
Through advanced pattern recognition and statistical correlation, AIOps platforms know which alerts matter, which are related, and which you can safely ignore.
Example: Instead of sending dozens of alerts for a single database slowdown, the platform correlates them and notifies the team of one critical incident, which avoids alert fatigue.
They detect and diagnose potential issues in real time
Machine learning models highlight anomalies and track performance trends often before they become an obvious incident.
Example: If a normally low-latency API suddenly starts slowing down, AIOps can flag the deviation immediately, even if it hasn’t yet caused a full outage.
They trigger automated actions
Based on these insights, the platform can initiate predefined workflows to auto-address or escalate any incidents.
Example: An application error spikes, so AIOps might create a ticket, assign it to the right team, notify the incident manager, and kick off a resolution checklist.
Overall, the combination of real-time analysis and intelligent automation enables IT teams to act faster and at a scale that manual operations processes simply can’t match. Take a trial of monday service to elevate your IT workflows.
Try monday service5 benefits of AIOps in service management
Traditional monitoring tools are based on static thresholds, meaning everything is black and white, and they don’t always surface useful signals. Service operations teams must sift through endless alerts and manually route tickets to deal with issues, potentially overlooking critical incidents.
Unsurprisingly, 86% of IT professionals have already adopted artificial intelligence to alleviate their workloads — according to the monday.com World of Work report. AIOps takes this further by applying AI directly to IT operations management to produce the following benefits:
1. Reduced operational costs
AIOps lowers the cost of operations by reducing the volume of manual work, which frees up technical talent to focus on higher-impact projects. AIOps solutions also cut tool sprawl by centralizing monitoring, alerting, and automated workflows in a single system, which reduces licensing and maintenance costs.
Over time, these small efficiencies add up to create measurable savings across staffing and infrastructure.
2. Faster problem solving
AIOps shorten resolution times by quickly highlighting what’s wrong so teams can identify the root cause without wasting hours reviewing event histories. With clearer signals and fewer false positives, teams can respond with confidence and precision.
3. More efficient service management
Service operations often depend on coordination between systems, teams, and workflows. AIOps connects insights across these tools and channels to produce a clear picture of what to focus on. As a result, it’s easier for IT service management teams to prioritize issues and keep processes running smoothly across the organization.
4. Proactive issue prevention
Some of the most disruptive outages start with small signals that are overlooked. AIOps continuously analyzes system behavior to identify patterns early.
5. Better customer service
Reliable systems lead to better outcomes for employees trying to stay productive and customers expecting fast, seamless support. AIOps improve the quality and consistency of service delivery so IT teams can meet customer expectations without being overwhelmed by volume or complexity.
AIOps platform use cases
AIOps platforms solve a wide variety of problems common in modern IT environments. Below are 5 high-impact use cases that show where AIOps make a measurable difference in service delivery, performance, and operational resilience.
AIOps for incident management
When ticket volumes spike or multiple alerts fire at once, it’s typical for response times to suffer. AIOps platforms manage this load by automatically classifying, prioritizing, and routing incidents based on context and historical patterns. This reduces bottlenecks at first-line support and enables the right issues to reach the right teams faster.
AIOps for root cause analysis
IT incidents present as symptoms across different systems, making it difficult to pinpoint where the real problem lies. AIOps platforms connect data points across environments to trace issues to their source. By understanding cause and effect more clearly, teams can resolve incidents faster and avoid any recurring problems.
AIOps for anomaly detection
Unusual system behavior could signal anything from an emerging capacity issue to a potential security event. AIOps continuously monitor for deviations from normal patterns, flagging them before they trigger broader failures. Early detection is especially valuable in complex, distributed environments where problems don’t always follow a predictable path.
AIOps for proactive issue prevention
When your teams identify early warning signs, such as subtle performance drifts, recurring error patterns, or changes in baseline activity, AIOps allows teams to intervene with scheduled fixed and planned maintenance long before their end users are affected.
AIOps for SLA and performance monitoring
Service level agreements are only as strong as the systems supporting them. AIOps platforms track key metrics in real time, alerting teams to potential SLA breaches or performance degradation before they occur. As a result, IT leaders gain greater confidence in their ability to meet commitments and have clear visibility into where adjustments are needed.
How does AIOps compare to other IT frameworks?
AIOps often overlaps with other operational frameworks, although each has a distinct focus. Here’s how AIOps fits alongside other key concepts.
AIOps vs. DevOps
DevOps is about speed and collaboration, bringing development and operations together to ship software faster and more reliably. AIOps complements the operational side of DevOps by automating detection, triage, and response. Where DevOps focuses on deployment velocity, AIOps keeps the systems behind those deployments healthy and responsive.
AIOps vs. MLOps
Both AIOps and MLOps involve machine learning, but their objectives differ. MLOps supports data science teams by helping them train, deploy, and maintain machine learning models in production environments. AIOps brings that same intelligence into IT operations, analyzing system data to detect issues, prevent downtime, and automate incident response.
AIOps vs. Observability
Observability allows you to understand what’s happening inside complex systems using signals like traces and logs. AIOps builds on that foundation of visibility by analyzing those signals in real time, correlating them across sources, and initiating intelligent actions. If observability lets you see the problem, AIOps solves it faster.
AIOps vs. DataOps
While DataOps focuses on moving high-quality data through pipelines for advanced analytics and machine learning, AIOps applies intelligence to the operational data those systems produce. One supports data teams in building models and dashboards; the other supports IT teams in keeping systems running smoothly.
AIOps vs. ITOps
ITOps is the traditional backbone of IT which monitors infrastructure, manages incidents, and keeps services running. AIOps enhances that function using intelligence and automation to help ITOps teams respond faster, make smarter decisions, and focus less on repetitive tasks and more on strategic improvement.
monday service: The future of AIOps in action
IT teams under pressure need intelligent support that reduces complexity and empowers them to act quickly and confidently. As frontend developer Rushika Rai puts it:
AIOps is about ‘optimizing IT performance without the constant pressure of putting out fires.
That’s exactly the mindset behind monday service. Here’s what you can expect from our enterprise-grade service management platform.
Handle high ticket volumes with precision
Automated ticket classification and AI-powered fields detect type, priority, and sentiment to instantly route tickets to the right person. SLA timers, smart escalations, and satisfaction surveys are also baked in, so every request is resolved efficiently and transparently.
Deliver personalized, context-rich experiences
By integrating with your CRM, employee directory, and asset management tools, monday service gives agents the full picture so every interaction feels informed and personal. AI-assisted fields pre-fill relevant info to speed up responses while keeping the experience consistent.
Boost agent productivity at scale
Agents can resolve tickets faster using our AI Copilot, which delivers in-the-moment suggestions based on historical resolutions, request context, and past interactions. Combined with automated workflows and a self-serve knowledge base, your team spends less time on repetitive tasks and more on high-value work.
Mitigate risk with real-time insights
Dashboards and service analytics provide live views of performance, capacity, and risk areas. Whether tracking SLA breaches, analyzing ticket trends, or forecasting workloads, monday service aligns with your business goals and stays ahead of potential disruptions.
Move from reactive to proactive service delivery
With trend detection, workflow correlation, and predictive reporting, monday service helps teams spot issues before they escalate. You can track how service requests map to broader initiatives, monitor recurring patterns, and act decisively, all before users experience any hiccups.
Designed for IT leaders, service desk managers, and cross-functional teams, monday service is easy to adopt, highly customizable, and built to scale with your business. From rapid onboarding to flexible integrations with tools like Outlook, Slack, Azure DevOps, and DocuSign, it connects every moving part of your service operations without added complexity. Get a free trial to see how the platform supports faster resolution and better service outcomes.
Try monday serviceWhat are AIOps tools?
AIOps tools are platforms that combine data ingestion, machine learning, predictive analytics, and automation to improve how IT teams monitor and manage their systems. Common features of these tools include anomaly detection, root cause analysis, event correlation, and automated remediation workflows.
What is the difference between AI and AIOps?
Artificial intelligence is a broad field focused on developing systems that execute tasks typically requiring human intelligence, such as learning, reasoning, or decision-making. AIOps is a specific application of AI that enhances IT operations by analyzing data, detecting patterns, and triggering actions across infrastructure and services.
What are the four stages of AIOps?
The four main stages of AIOps are:
- Data collection and curation: Gathering structured and unstructured data from across IT systems, then organizing and preparing it for analysis.
- Model training: Using historical data to train machine learning models that can recognize patterns, predict issues, and distinguish normal from abnormal behavior.
- Automated response: Building and configuring workflows that respond to model outputs, such as alerting, ticket creation, or automated remediation.
- Deployment and anomaly detection: Running trained models in real-time environments to identify anomalies, detect incidents early, and improve service performance.
Some frameworks also include a fifth stage: continuous learning, where models evolve based on new data and feedback to improve accuracy over time.
What is the scope of AIOps?
AIOps is used across the entire IT operations lifecycle, from monitoring and incident response to performance optimization, capacity planning, and service automation. Its scope includes infrastructure, applications, networks, cloud environments, and service management systems.
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