When the time comes to start handing over my projects' deliverable to ITOps, I always point out the use of AIOps for efficient monitoring and support activities.

Because AIOps (AI for IT Ops) may combine big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination, in the following manner:
• Using data generated by IT environments;
• Real-time understanding of issues;
• Preventing outages and maintaining uptime;
• Quick identification of root cause via the use of AI/machine learning for error detection with the help of data on previous outages, for fast issue resolution;
• Continuing to assure service delivery.

AIOps automates tasks and allows error detection with alert analysis and event reporting, helping prioritizing likely solutions, because:
• AIOps deduces frequency of checking for alarms as it automates detection and pinpoints problems;
• Moreover, AIOps achieves faster Mean Time to Resolution (MTTR) and fixes issues before they become outages with error detection.

AIOps solutions are Data-agnostic, Department-agnostic:
• It analyzes data from multiple sources;
• Produces outputs as they are not not tied to use case or teams;
• The organization's units speak the same language and collaborate with the help of AIOps;
• As it performs in autonomy, AIOps has visibility into systems’ status using a single source of truth.
• And its analysis result is reliable and context-based, therefore it helps to prioritize tasks and automate functions; which means ITOps teams spending less time on repetitive tasks, while taking decisive actions.

19 May 2025
(Source: Aligning IT and Business Strategies, Book3 - IT Contributing to the Organization's AI Strategy; Study Notes)

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