> For the complete documentation index, see [llms.txt](https://docs.aisera.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aisera.com/aisera-platform/llm-operations/understanding-llm-capabilities/change-optimization-with-cluster-analysis.md). # Change Optimization with Cluster Analysis The Aisera AI Ops application provides robust support for Change Management Optimization, a key process within ITSM. It goes beyond traditional rule-based approaches by using machine learning and pattern recognition to analyze historical change data, correlate related records, and provide actionable recommendations to streamline and automate the change lifecycle. The Aisera Gen AI platform uses AI to cluster similar changes by analyzing historical change requests across multiple dimensions—title, description, impacted configuration items (CIs), implementation steps, outcomes, and time windows. These clusters are then further analyzed against key ITSM indicators, such as the number and type of incidents, alerts, outages, and major incidents linked to those changes. This allows Aisera to evaluate the risk and impact profile of each cluster.\ \ By identifying change clusters that consistently demonstrate low impact and minimal risk—changes that result in a few or no associated incidents or outages, AI Ops flags these as candidates for classification as 'standard changes' under ITIL-defined change types. Additional assessment criteria include the number of potentially impacted business users, the presence of critical CIs or related CIs from the CMDB, and the change’s historical success rate. Once low-risk clusters are identified, AI Ops generates detailed reports highlighting the change patterns, impact history, and rationale for marking them as standard. These reports also assess the feasibility of automation by examining how similar changes were previously deployed—whether via scripts, orchestration tools, or manual processes. If past executions were automated or semi-automated, Aisera will recommend full automation of future occurrences within that change cluster. Furthermore, AI Ops continually monitors new change requests in real time. When a new change matches an optimized and pre-evaluated cluster, the application proactively suggests reclassifying the change to 'standard.' It presents a clear explanation based on historical context, risk data, and CI information—helping Change Managers make faster, more informed decisions. In summary, AI Ops enhances change optimization by intelligently identifying repeatable, low-risk changes that can be standardized and automated. This reduces manual effort, lowers the risk of service disruption, improves compliance, and accelerates the overall change management process. For more detailed information, see [**Knowledge Custers**](https://docs.aisera.com/aisera-platform/content-generation-from-tickets/analyze-kb-article-generation-results/ai-generated-documents-from-bulk-tickets#knowledge-clusters). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.aisera.com/aisera-platform/llm-operations/understanding-llm-capabilities/change-optimization-with-cluster-analysis.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.