What is AIOps? Benefits and adoption considerations

AIOps technology helps IT operations and other teams improve key processes, tasks, and decisions via improved analysis of high-volume data. Here's how to decide whether it's a fit
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Today’s IT environments are continually changing, with new technologies constantly being introduced while new ways of working are being adopted. Through it all, the top job of IT operations is to keep systems running reliably. IT operations play a critical role in achieving and retaining positive customer experiences during the ongoing digital transformation of your enterprise. But it’s not easy. 

The volumes of diverse data generated in IT is only growing, and it can be difficult for IT operations to keep up. That’s why the automation and prediction capabilities associated with AIOps are so promising for IT operations. 

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What is AIOps? What can it do?

AIOps tools automate the fast ingestion of volumes of data. Machine learning can analyze the data for predictions or alerts, leading to automation or better decisions.

But before investing in AIOps, it’s important to understand the realities of it. The term “AIOps” is a combination of the words artificial intelligence (AI) and operations (Ops). This implies that it is the usage of artificial intelligence to manage or apply within IT operations, but there’s more to it than that. Let’s look at what AIOps is capable of today, and whether it’s a good fit for your IT organization.

AIOps solutions equip IT operations and other teams to improve key processes, tasks, and decision-making through improved analysis of the volumes and categories of data coming its way. The adoption of these tools automates the fast ingestion of volumes of data. Machine learning is used to analyze the data and present findings that either predict or alert you to issues. This newfound knowledge can then be used for automation or decision-making. 

Should you try AIOps? 

How do you know if AIOps would be a good match for your IT organization? Consider whether your IT operations organization is facing any of the following issues: 

  • The environment being monitored generates a large amount of data, but decision-making is difficult as a result.    
  • Your IT operations team has difficulties prioritizing different issues for resolution. They face a large volume of alerts, and many of those alerts are redundant.
  • Your tech team is struggling to manage the software and infrastructure performance aspects in a real-time or proactive way, and this is impacting your customers.
  • Your team can manage key operational problems with domain-specific tools, but there is no end-to-end visibility for a holistic IT operation approach.

3 benefits of AIOps

Assuming one of the above is resonating with you, you might ask, “So what are the benefits of AIOps?” 

First, it’s about processing data fast: AIOps gives you the ability to enable real-time data correlation, and even better, almost all data is a candidate for this processing. Raw data can be ingested into smart algorithms powered by machine learning and big data. This can help you derive new insights from your raw data sets. This data ingestion and analysis can then help you create and set new targets for key metrics such as mean time to detect and mean time to repair.

This newfound automation allows your staff to focus on resolution instead of detection.  

Second, it’s about data-driven decision making: Machine learning is based on algorithms that can learn from data without relying on rules-based programming. AIOps brings key ML techniques to your IT operations, including pattern matching, predictive analysis, historical data analysis, and causal analysis. This helps with decision making by enabling purely data-driven, automated responses. Such automated responses to incidents eliminate human error and data noise. This newfound automation allows your staff to focus on resolution instead of detection.  

Third, IT work will become more proactive: No matter what purpose and mission your organization has, your success depends on how satisfied your customers or clients are with your products or services. In a competitive environment, it is no longer enough to respond to actual events. It’s now essential to predict possible issues and bottlenecks. This means IT operations must be able to predict and remediate performance issues across your applications, services, and infrastructure before they materialize and cause issues for customers or partners. AIOps helps enable this shift. 

Other considerations before adopting AIOps

While AIOps has a lot of potential to do good for your IT organization, there are some essential factors you must consider before going all in. 

First, the outcomes will only be as good as the quality of the data being leveraged. In most cases, the reality is that shifting to AIOps requires the improvement of the quality of data coming from the managed environment. 

Second, an implementation must have specific goals – there’s no one-size-fits-all approach for using AIOps. It will depend on the nature of your organization and its challenges. Many implementations have failed because the right success measurements weren’t set at the onset.

At the end of the day, it’s all about your company’s priorities. Do you want to achieve competitive benefits from AIOps, or are you planning on eliminating the stress within your IT operations team? Be sure you take the time to identify what you’re really hoping to get out of AIOps before you jump in. 

It’s easy to be a skeptic of another promising “Ops” tool. But consider how much pressure your IT operations team is under to add value to your digital transformation efforts. If you can make their jobs easier, you’re likely to increase morale and retention while also freeing up time to focus on the more strategic aspects of your transformation.

[ How can automation free up more staff time for innovation? Get the free eBook: Managing IT with Automation. ] 

Eveline Oehrlich is an industry analyst, author, speaker and business advisor focused on digital transformation. Eveline is the Chief Research Director at DevOps Institute where she leads the research and analysis for the Upskilling: Enterprise DevOps Skills Report and other research projects.