AIOps stands for Artificial Intelligence (AI) for IT Operations and was coined by Gartner in 2016. It refers back to the use of AI capabilities resembling machine learning models and natural language processing to automate operational workflows. AIOps augments and supports IT processes and monitors real-time data to quickly detect and resolve issues.

There has been remarkable growth in the quantity of knowledge that’s being generated by IT systems, and the shortage of monitoring and evaluation of the identical may lead to missed opportunities and expensive downtime. This is where AIOps is useful. It uses smart monitoring systems and AI capabilities to stop outages, maintain uptime, and achieve continuous service assurance, ensuring that organizations proceed to operate at the specified speed.

Why is AIOPs crucial?

The significant growth of knowledge generated by IT systems necessitates monitoring of the identical to detect any anomalies. However, manually coping with hundreds of alerts is laborious and time-consuming. Moreover, managing, interpreting, and correlating multiple applications for tracking performance metrics is daunting. AIOps helps tackle these issues by providing a single evaluation pane, detecting issues, and alerting the team to scale back the time spent on these alerts.

Most businesses nowadays use predictive analytics to make sure a seamless user experience, one of the sought-after capabilities of AIOps. Furthermore, many IT professionals have realized the importance of AIOps by way of automation capabilities, higher efficiency, and predictive insights, which has greatly increased the demand for AIOps prior to now few years.

How does AIOPs work?

As mentioned earlier, AIOPs leverages artificial intelligence to automate and optimize IT processes. It is mostly powered by five algorithms which might be as follows:

  • Data Collection: Collecting large amounts of noisy data from structured and unstructured sources, resembling application logs and event data, and highlighting only those parts that indicate a problem.
  • Data Analysis: Analyzing the chosen data using algorithms like anomaly detection and predictive analytics to detect anomalies and separate the actual issues from false alarms.
  • Inference: AIOps helps in identifying the basis reason behind recurring issues and helps IT teams prevent the identical.
  • Collaboration: AIOps notifies the suitable teams once the basis cause evaluation is complete and facilitates collaboration between them by providing relevant information.
  • Automation: AIOps significantly reduces manual intervention by automating responses and remediation.

Key use cases of AIOps

Some of the common use cases of AIOps are as follows:

  • Root Cause Analysis: AIOps will help discover the leading reason behind a problem and suggest appropriate measures to tackle the identical. For example, AIOps platforms can find the reason behind network outages and fix them immediately. Moreover, in addition they take protective measures to stop similar issues.
  • Performance monitoring: AIOps acts as a cloud infrastructure and storage system monitoring tool and reports on metrics like usage, availability, and response times. It combines and aggregates information to enhance the tip user’s experience.
  • Threat detection: AIOps also assists in identifying security risks by detecting patterns of malicious activity, thus reducing threats and intrusions.
  • Anomaly detection: Using AIOps tools, users can discover data anomalies and predict problematic events, resembling data breaches.
  • Intelligent alerting: AIOps filters only the meaningful data and separates the actual issue from false alarms. Moreover, it also prevents alert storms, where one false alarm triggers one other, resulting in a domino effect.
  • Automation: Automation is one in all the important thing use cases of AIOps. It automates remediation for known issues, thereby saving effort and time.


Although AIOps tools help streamline IT processes, establishing and maintaining the identical requires significant effort and time. Moreover, for best results, organizations must make sure that their data is up-to-date and accurate since AIOps algorithms are depending on the information they’re trained on. Lastly, there’s at all times a risk of bias and ethical difficulties due to prevailing biases within the datasets.

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