What Is AIOps? Definition, Examples, and Use Cases

Written by Coursera Staff • Updated on

Learn more about AIOps, which combines AI and machine learning technologies with IT operations to provide new solutions to help IT professionals do their jobs.

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Key takeaways

  • AIOps is the use of artificial intelligence and advanced analytics to automate IT operations and optimize tasks such as performance monitoring and workload scheduling.

  • AIOps tools can help lower operational costs, enable more efficient issue detection and remediation, improve online customer experience, and deliver faster service management.

  • AIOps platforms to consider include IBM Instana Observability, Cisco AppDynamics, and Datadog.

Use cases of AIOps include root cause analysis, event correlation, performance monitoring, and cloud optimization. Read on to learn more about what AIOps is, how it utilizes advanced analytics to streamline IT tasks, and the resulting benefits for businesses and IT professionals alike. Also, discover how AIOps can help prioritize critical issues, and explore some of the leading AIOps platforms available today.

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What is AIOps?

Artificial intelligence for IT operations (AIOps) combines advanced analytics with IT operations. AIOps is the practice of automating critical IT functions, such as IT infrastructure maintenance and service management, using artificial intelligence technologies.

In recent years, businesses have become more reliant on digital technologies. As a result, organizations face more complex digital problems and a greater need for IT professionals prepared to address them using modern techniques such as AI and machine learning. By ingesting large volumes of data generated by IT systems, AI technologies can identify patterns and issues, automatically respond to or escalate incidents to IT or DevOps teams, and provide insights to optimize IT tasks such as performance monitoring and workload scheduling.

Why AIOps is important

AIOps is about leveraging advanced analytics tools like artificial intelligence (AI) and machine learning (ML) to automate IT tasks quickly and efficiently. Rather than replacing workers, IT professionals use AIOps to manage, track, and troubleshoot the complex issues associated with digital platforms and tools, like managing monitoring alerts across tools, securing cloud environments, and consolidating real-time changes in interdependent IT technologies, including microservices and containers.

AIOps also provides a way for IT professionals to parse through the vast amounts of data produced by businesses’ many digital platforms. By collecting and analyzing data from multiple such sources, AI models can separate critical insights from irrelevant data and identify the root cause of an issue, enabling them to resolve problems quickly and, in some cases, design solutions before they arise.

According to a 2020 survey conducted by McKinsey & Company, businesses “accelerated the digitization of their customer and supply-chain interactions and of their internal operations by three to four years” during the COVID-19 pandemic [1]. In 2026, 82 percent of businesses view digital transformation as a core part of their business strategy, with 36 percent aiming to reduce operational inefficiencies and 30 percent aiming to improve current business processes [2].

The takeaway: Modern businesses continue to rely on digital technologies to run virtually every aspect of their operations.

What are the use cases of AIOps?

Artificial intelligence helps IT professionals streamline many operational processes, including anomaly detection and cloud optimization. Common ways that you can use AIOps solutions in routine IT work include:

  • Anomaly detection: AIOps detects anomalies without human intervention and flags them to relevant personnel.

  • Root cause analysis: AI-powered root cause analysis traces the source of an issue by processing and correlating data from multiple sources to identify why a problem occurred.

  • Event correlation: Machine learning models efficiently scan large volumes of data to detect the most important events and derive predictive insights.

  • Automated remediation: Intelligent automation systems can solve certain problems and troubleshoot issues without human intervention based on predefined remediation workflows.

  • Performance monitoring: AI can monitor application and IT infrastructure performance and provide visibility into multiple layers of dependent services, such as networking resources and APIs.

  • Cohort analysis: AI analyzes user data to better understand when errors occur, why they occur, and how to fix them to improve performance.

  • Security operations: By analyzing security events, network traffic, and user behavior, AI systems can employ anomaly detection to predict security threats and streamline security event management.

  • Cloud optimization: AIOps tools offer visibility and automation in hybrid multicloud environments with frequently changing dependencies, reducing operational risks in cloud migration and cloud application deployment.

AIOps benefits

AIOps brings the power of artificial intelligence and machine learning to the IT domain, providing real-time performance monitoring, continuous insights, and a faster time to resolution. Artificial intelligence for IT operations enables IT professionals to improve operations through descriptive, diagnostic, prescriptive, behavioral, and predictive analytics.

Additional benefits that businesses and IT professionals can expect include:

  • Lower operational costs and increased ROI for IT solutions

  • More efficient remediation and detection of potential issues due to intelligent monitoring tools

  • Better customer experience online resulting from improved digital systems

  • Faster service management due to AI capable of managing high-volume data sources

  • Proactive problem identification using predictive modeling and assisting in the design of solutions to stop them from occurring

Read more: 10 Artificial Intelligence Examples: AI in Practice

AIOps example: Minimizing alert fatigue

As workplaces become more reliant on interdependent digital platforms that connect one department to another, the likelihood of a critical technical failure, such as a system shutdown, increases. As a result, IT operations management must maintain a real-time view of how digital technologies function within a business. This necessity can result in constant notifications. A high volume of alerts can conceal the most important problems within a wave of routine reports.

To highlight only the most important alerts, AIOps tools can monitor notifications and flag only the most critical issues to IT operations teams, ensuring swift resolution of the most pressing problems and allowing IT teams to focus on strategic, value-added work instead of being overwhelmed by alerts.

AIOps tools and platforms

Many AIOps platforms, tools, and business services are available to organizations today. Some of the most popular platforms include:

  • IBM Instana Observability: IBM Observability is an enterprise application performance monitoring tool with agentic AI-based automation capabilities and full-stack visibility. It can be deployed on-premises or as a SaaS solution.

  • Splunk AppDynamics: AppDynamics is a full-stack application performance management tool. It uses analytics to measure application performance with key business metrics for deeper insights.

  • Datadog: Datadog specializes in AI-powered observability across tech and application stacks at scale. It also allows you to build interactive dashboards and offers real-time AI-enabled security event detection and investigation.

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Article sources

1. 

McKinsey & Company. “How COVID-19 Has Pushed Companies Over the Technology Tipping Point—and Transformed Business Forever, https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever.” Accessed September 12, 2026.

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