AIOps: A Step-by-Step Guide

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The Hype: AIOps Platforms and functionality is hot, promising to revolutionise IT operations with AI-powered tools. But the reality is more nuanced.

This post explains what AIOps is, how it works, and how to implement it successfully in your business.

What is AIOps?

Imagine an AI assistant for your IT team. AIOps uses AI and machine learning to automate tasks and insights that humans can’t easily handle. It can:

  • Automate mundane tasks: free up your IT team for more strategic work, like migrating servers to the cloud.
  • Handle complex tasks: detect cyberattacks by analysing vast amounts of security data.
  • Predict and prevent problems: identify potential issues before they occur, using historical data and real-time monitoring.

How Does it Work?

Think of AIOps as a two-pronged approach:

  1. Data analysis: AIOps ingests and analyses tons of IT data, like logs, metrics, and security alerts.
  2. Actionable insights: It uses AI to find patterns, predict problems, and automate repetitive tasks.

Choosing the Right AIOps Tool:

Before diving in, identify your specific needs. Don’t get caught up in the hype! AIOps can’t solve everything.

Ask yourself:

  • What are our biggest IT problems?
  • How can AIOps address these issues?

Getting Started with AIOps:

Here’s a step-by-step approach:

  1. Understand your data: Identify all relevant IT data, including structured and unstructured sources.
  2. Target your biggest problems: Analyse past failures and use data to identify potential causes.
  3. Train your AIOps system: Feed it historical data and known problems to help it learn and predict.
  4. Monitor and automate: Implement real-time monitoring and automated responses for known issues.
  5. Expand and optimise: Start small, then gradually expand to other areas and teams.

Potential Pitfalls:

Here are some things to watch out for:

  • Over-reliance on AI: It’s not a magic bullet. Human oversight is crucial to ensure accuracy and avoid false positives.
  • Limited data: Insufficient data can lead to inaccurate predictions. Collect and share data across teams to build comprehensive datasets.
  • Data silos: Isolated data creates blind spots. Break down silos and unify data access for better insights.

The takeaway:

AIOps has the potential to transform your IT operations, but it’s not a quick fix.

Take a thoughtful approach, understand your needs, and avoid common pitfalls.

By implementing AIOps strategically, you can empower your IT team and optimise your IT environment.

With the Ivanti Neurons platform for Hyper-Automation functionality and it’s built-in Machine Learning (for ticket classification suggestions as well the Incident Correlation feature), which empowers IT administrators, to swiftly identifying and linking related incidents.

AI-recommended incidents are intelligently correlated, allowing for the analysis of patterns and timelines. This ticketing feature enables quick root cause pinpointing, reducing downtime, and facilitating resource allocation efficiency. Analysts have the flexibility to take immediate actions, such as linking and unlinking incidents, directly from the AI Recommendations panel. This not only contributes to faster incident resolution but also enhances overall service reliability, user satisfaction, and operational efficiency.

Speak to us today to see how we can assist you on your AIOps journey.

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