What is AIOps? Guide to AI For IT Operations

Artificial intelligence for IT operations, or AIOps, is the application of artificial intelligence (AI) capabilities to automate, streamline, and optimize IT operations.

AIOps uses machine learning (ML), natural language processing (NLP), data analytics, and a range of other AI techniques to process and correlate vast amounts of data from across the IT environment. By continuously analyzing this information, it can detect anomalies, identify patterns, and surface actionable insights. 

This allows IT teams to cut through alert noise, accelerate incident response, automate routine tasks, and even prevent issues before they impact end-users.

Why is AI for IT operations important?

AIOps is important because it helps IT teams turn vast, fragmented, and fast-moving data into clear, actionable insights. Traditional tools can no longer handle this effectively.

For years, IT operations depended on manual monitoring, static thresholds, and isolated tools. As environments grew in scale and complexity with cloud services, hybrid infrastructure, and increasing user demands, this approach became too slow and reactive.  

AIOps applies artificial intelligence to automate analysis, prioritize what’s relevant, and help teams respond faster and more strategically. It's about staying ahead of issues and scaling operations without scaling chaos. 

And organizations are clearly seeing the value. According to Fortune Business Insights, the global AIOps market is projected to grow from $2.23 billion in 2025 to $8.64 billion by 2032, reflecting a CAGR of 21.4%.

5 key benefits of AIOps

• Faster incident detection and response: Identifies problems in real time and enables quicker action.
• Reduced alert fatigue: Filters out irrelevant signals and highlights critical events.
• Proactive issue prevention: Spots trends that indicate potential failures before they happen.
• Improved root cause analysis: Correlates data across systems to find the source of issues quickly.
• Higher operational efficiency: Automates repetitive tasks and streamlines workflows.

How does the AIOPs framework work?

An AIOps framework typically follows a layered structure designed to collect, analyze, and act on data from across the IT environment. 

While there’s no single standard, most implementations share the same core components. The framework is flexible and adapts to each organization's infrastructure, goals, and operational maturity. 

Below are the typical layers of an AIOps framework and what they do.

1. Data collection

This layer gathers information from logs, metrics, events, traces, and tickets across IT systems. It ensures visibility across infrastructure, applications, cloud services, and support tools.

2. Data aggregation and storage

Raw data is normalized, enriched, and stored in centralized systems like data lakes or message buses. This step prepares the data for effective correlation and analysis in later stages.

3. Analytics and correlation

AI and ML models process the data to detect anomalies, uncover patterns, and correlate related events. This helps reduce alert noise and surface real incidents faster and with better context.

4. Insight and decision-making

The platform provides actionable insights, highlights root causes, and supports faster decision-making. Insights are often displayed in dashboards or routed into ITSM tools as tickets or alerts.

5. Automation and orchestration

This layer triggers automated actions such as ticket assignment, service restarts, or escalations. It streamlines resolution workflows and enables teams to shift from reactive to proactive operations.

6. Visualization and collaboration

Dashboards, reports, and visualizations present data in a clear, contextualized way for faster understanding. These tools also help align teams by providing a shared view of system health and performance across domains. 

What are the types of AIOps?

AIOps platforms are generally categorized into two types: domain-centric and domain-agnostic. The difference lies in how they process data and the scope of their insights.

Understanding this distinction helps organizations choose the right type of AIOps solution based on their existing tools, desired integrations, and IT complexity.

Domain-centric AIOps

Domain-centric platforms are tied to a specific vendor, toolset, or IT function. They work best within that domain – like application performance monitoring (APM), infrastructure monitoring, or IT Service Management (ITSM) – and are deeply integrated into those tools.

These solutions are optimized for depth, offering advanced analytics and automation within a single area. However, they may lack visibility across the broader IT landscape. One example could be an AIOps built into a network monitoring tool or a cloud provider's native performance dashboard.

Domain-agnostic AIOps

Domain-agnostic platforms ingest and analyze data across multiple tools, vendors, and IT domains. They’re built to correlate events and metrics across silos, offering end-to-end visibility across the IT stack.

These platforms are ideal for organizations with hybrid or complex environments, since they can connect data from infrastructure, applications, service desks, and more. One example could be an AIOps layer that integrates data from multiple monitoring tools, logs, Configuration Management Database (CMDB), and ITSM systems. 

AIOps use cases and examples

AIOps is already helping IT teams address real-world challenges by analyzing data in real time and automating key tasks. Its applications span across monitoring, incident response, capacity planning, and more. 

Here are three common use cases that show how AIOps delivers value.

1. Incident detection and automated response

AIOps continuously monitors system performance to detect anomalies like unexpected latency, service failures, or traffic spikes. 

For instance, an e-commerce company can rely on AIOps to spot a slowdown in its checkout process and automatically scale server capacity or restart affected services, all before customers even notice.

2. Root cause analysis across complex systems

When multiple tools generate alerts at once, manually tracing the origin of an incident can be time-consuming. AIOps correlates data from across applications, networks, and infrastructure to quickly identify the most likely cause. 

A financial services company, for example, might use AIOps to pinpoint that a widespread outage actually stems from a misconfigured load balancer, not the database or application as initially suspected.

3. Capacity planning and resource optimization

By analyzing historical usage patterns alongside real-time metrics, AIOps helps teams anticipate future demand and adjust resources proactively. 

During a high-stakes product launch, an IT team might use AIOps insights to forecast a surge in traffic and allocate extra cloud resources in advance to ensure consistent performance. 

How to get started with AIOps?

Getting started with AIOps looks different for every organization. It depends on your tools, data, goals, and level of IT maturity. However, there are some common steps that can help you lay the foundation for a successful AIOps strategy.

1. Identify your top operational pain points

Start by pinpointing the biggest challenges your IT team faces. This could mean frequent outages, alert fatigue, slow resolution times, lack of visibility across tools, etc. This will help you prioritize the right use cases and avoid adopting AIOps for the sake of trend-following.

2. Assess your current data sources and toolchain

AIOps relies on large volumes of high-quality data. Evaluate whether your monitoring, observability, and ITSM tools can provide clean, timely, and structured data, and how well they integrate with each other.

3. Choose the right AIOps type (domain-centric or agnostic)

Decide whether a focused, domain-specific tool will address your immediate needs or if your environment requires a domain-agnostic platform that can correlate data across the entire stack.

4. Start small with a focused use case

Don’t try to automate everything at once. Start with a single, well-scoped use case – like noise reduction or automated incident routing – and measure its impact. This allows you to build internal trust and refine your strategy before scaling.

5. Prepare your team and processes

AIOps requires a cultural shift. Train your team to interpret AI-driven insights, define escalation workflows for automated alerts, and ensure accountability for refining the models over time.

6. Measure, iterate, and expand

Set clear KPIs (like reduced MTTR or alert volume) and continuously evaluate results. Use the insights gained from your first use case to refine your models and expand into more advanced areas like root cause prediction or capacity forecasting. 

InvGate’s AI capabilities to support IT operations

AIOps principles like automation, prediction, and intelligent analysis aren’t limited to infrastructure monitoring. They also apply to ITSM tools like InvGate Service Management.

Through the InvGate AI Hub, the platform offers several AI-powered features that support proactive, efficient IT operations. The most relevant is Predictive Risk and Impact Analysis, which uses historical data and machine learning to assess the risk and impact of change requests. This helps teams avoid disruptions and maintain service continuity.

“What we did at InvGate was create a layer of abstraction – what is called the AI Service – that allows us to connect each feature to the best language model created for that particular purpose. That allows us to create more value at an optimal cost to our customers.”

Ariel Gesto, CEO and co-founder of InvGate - Episode 91 of Ticket Volume - IT Podcast

Other features aligned with AIOps goals include:

• Major Incident Detection, which identifies patterns in reported incidents and flags those that could escalate into major disruptions.
• Common Problem Detection, which surfaces recurring issues and suggests creating a problem ticket to address the root cause.
• Smart Request Escalation, which monitors tickets in real time and alerts teams about requests at risk of missing SLAs.

Together, these capabilities help organizations move toward proactive, data-driven IT operations (the core promise of AIOps) using tools that are already integrated into their Service Management workflows. 

AIOps certifications

As AIOps gains traction, certifications are emerging to validate the skills needed to implement and manage these advanced solutions.

These credentials benefit individuals by boosting career prospects and validating expertise, while also helping organizations ensure their teams can effectively leverage AIOps for improved operational efficiency and proactive problem-solving.

Whether vendor-neutral or vendor-specific, these certifications prove a professional's capability to transform IT operations with AI-driven insights and automation.

Key certifications include:

• AIOps Foundation (AIOF) Certification by DevOps Institute (PeopleCert Group): A vendor-neutral option covering core AIOps principles and implementation.
• AIOps Certified Professional (AIOCP) by DevOps School: Another vendor-neutral certification focused on practical application of AI/ML in IT operations.
• Splunk IT Service Intelligence Certified Admin: Validates expertise in deploying and using Splunk ITSI, which incorporates AIOps capabilities for service monitoring.
• Broadcom AIOps Technical Specialist Certifications: A suite of role-based certifications focusing on skills with Broadcom's AIOps-enabled products.