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What is AIOps (artificial intelligence for IT operations)?

By Kinza Yasar

Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other AI technologies to automate and enhance IT operations.

The systems, services and applications in a large enterprise -- especially with the advances in distributed architectures such as containers, microservices and multi-cloud environments -- produce immense volumes of log and performance data that can impede an IT team's ability to identify and resolve incidents. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.

An AIOps platform should provide enterprises with the ability to do the following:

How does AIOps work?

AIOps uses advanced analytics to automate and optimize IT operations processes. AIOps typically works by following these steps:

  1. Data collection. AIOps platforms collect information from a variety of sources, including application logs, event data, configuration data, incidents, performance metrics and network traffic. This data can be both structured, such as databases, or unstructured, such as social media posts and documents.
  2. Data analysis. The gathered data is analyzed using different types of ML algorithms such as anomaly detection, pattern detection and predictive analytics to find abnormalities that might require the attention of IT staff. This step ensures real issues are separated from noise or false alarms.
  3. Inference and root cause analysis. AIOps carries out root cause analysis to assist in locating the origin of problems. IT operations teams can attempt to prevent disruptions from recurring by looking into the root causes of current issues.
  4. Collaboration. Once the root cause analysis is complete, AIOps notifies the appropriate teams and individuals, providing them with relevant information and promoting efficient collaboration despite any potential geographical distance among them. In addition, this partnership helps to preserve event data that could be essential for identifying future issues of a similar nature.
  5. Automated remediation. AIOps can remediate issues automatically, significantly reducing manual intervention and speeding up incident response. These can be automated responses, such as resource scaling, restarting a service or executing predefined scripts to address problems.

Getting started with AIOps

Setting up AIOps in an organization involves the following strategic steps to ensure the successful integration of AI technologies into IT operations:

  1. Assessing current infrastructure. Organizations should begin by evaluating their existing IT infrastructure and operations. They must identify the tools, processes and data sources currently in use to understand the gaps and areas that can benefit from AIOps.
  2. Defining objectives. Businesses must clearly outline the goals they want to achieve with AIOps. This could include improving incident response times, enhancing system performance or reducing operational costs. Having specific objectives helps guide the execution strategy.
  3. Integrating data. This step should identify all relevant data sources across the organization's IT environment, including logs, metrics and events. The organization must craft a plan to integrate this data into a centralized platform. This integration plan might involve using big data technologies to create meaningful insights and business intelligence dashboards.
  4. Selecting AIOps tools. Organizations should choose the right AIOps tools that align with their objectives and infrastructure. For example, the tools should offer built-in capabilities including ML, anomaly detection and automated incident management. The tools should also integrate seamlessly with existing systems.
  5. Setting up a pilot program. A pilot program to test the execution of AIOps should now be set up on a smaller scale. This approach enables businesses to evaluate the effectiveness of the tools and processes before a full-scale rollout of AIOps. User feedback is also typically gathered at this stage to make any necessary adjustments.
  6. Training and change management. The IT staff must be educated on AIOps and its benefits. Companies should address any concerns employees might have regarding their job roles and emphasize that AIOps are designed to enhance human intervention and capabilities rather than replace them. Effective change management will also help in gaining buy-in from the team and stakeholders.
  7. Continuous monitoring. Once the execution of AIOps is complete, IT teams should continuously monitor the performance of AIOps tools and processes to help refine and optimize their AIOps strategy. They can achieve this by using the insights gained during the previous steps.

Key AIOps use cases

AIOps is generally used in organizations that also use DevOps or cloud computing as well as in large, complex enterprises. AIOps aids teams that use a DevOps model by giving them additional insight into their IT environment and high volumes of data. This gives the operations teams more visibility into changes in production.

Some common use cases for AIOps include the following:

AIOps technologies

AIOps uses a conglomeration of various AI strategies, including the following:

For more on artificial intelligence in the enterprise, read the following articles.

Artificial intelligence vs. human intelligence: How are they different?

AI vs. machine learning vs. deep learning: Key differences

Main types of artificial intelligence: Explained

Top AI and machine learning trends

What is trustworthy AI and why is it important?

The future of AI: What to expect in the next 5 years

AI regulation: What businesses need to know

Steps to achieve AI implementation in your business

How businesses can measure AI success with KPIs

The role of AI parameters in the enterprise

AIOps benefits and drawbacks

AIOps comes with the following advantages and disadvantages:

Benefits of AIOps

Drawbacks of AIOps

What capabilities should an AIOps platform provide?

An effective AIOps platform should offer a range of capabilities to enhance IT operations and support DevOps practices.

The following are essential features to look for in an AIOps platform:

AIOps vendors

To demonstrate value and mitigate risk from AIOps deployment, organizations should introduce the technology in small, carefully orchestrated phases. They should decide on the appropriate hosting model for the tool, such as onsite or as a service. IT staff must understand and then train the system to suit the organization's needs and, to do so, must have ample data from the systems under its watch.

AIOps is an emerging area, but according to "The Forrester Wave Process-centric AI for IT operations (AIOps)" report from 2023 and Gartner Peer Insights, there's a growing stable of product offerings for businesses to review and evaluate, including the following:

Future of AIOps

The future of AIOps looks promising. According to a report from The Insight Partners, the global AIOps platform market is predicted to increase from $4.9 billion in 2023 to $46.2 billion by 2031.

AIOps is expected to assist enterprises in enhancing their IT operations by minimizing noise, facilitating collaboration, offering full visibility and boosting IT service management. The AIOps technology has the potential to facilitate digital transformation by providing enterprises with a more agile, flexible and secure IT infrastructure. In addition, it's expected to mature and gain market acceptance, with enterprises incorporating it into their DevOps initiatives to automate infrastructure operations.

The following are some key trends and predictions for the future of AIOps:

Interest in AIOps and observability is growing exponentially in IT, but it doesn't come without its adoption challenges. Learn how to overcome AIOps adoption barriers and get visibility into problem areas for enhanced operations.

28 Oct 2024

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