What Is AIOps? A Guide With Uses and Applications.

Artificial Intelligence for IT Operations (AIOps) combines machine learning and big data to automate crucial IT processes. It has only existed for roughly five years yet the majority of companies are using it in their network and security operations.

If you’re already in the know and looking for new ideas or seeking information on what AIOps is all about, these experts can walk you through this rapidly growing market that is expected to double from $1.5 billion in 2020 to about $3 billion by 2025.

Although AIOps is primarily viewed as an important tool for IT operations, a surprising development is it’s also capturing the attention of business executives who use it to stay on track with their key performance index (KPIs) metrics.

“A few times now, our customers have adopted our technology and applied it to their KPI business metrics like revenue, transactions, or e-commerce,” said Spiros Xanthos, senior vice president and general manager for observability at Splunk. “They take purely business KPIs and tie them back to the underlying software infrastructure.” 

More on AIIs Artificial Intelligence a Good Career? These AI Professionals Think So.

What Is AIOps? 

AIOps aims to collect a lot of information from multiple sources, then use AI and machine learning analytic models to process and analyze the information, Stephen Elliot, group vice president of I&O, cloud operations and DevOps at IDC, told Built In. 

It also seeks to identify the root cause of problems and quickly assist in resolving them. AIOps helps IT teams understand patterns, anomalies, and potentially automatically remediate troublesome issues, as well as make predictions on what may happen, Elliot added.

Retail ranks among the top industries using AIOps, according to a 2021 State of AIOps report by ZK Research. Based on a survey of 510 technology executives in the U.S., 76 percent of retail executives were using AIOps, 68 percent in the finance industry and 66 percent in manufacturing.

“AIOps says if your network is doing this, you should do that. If your servers are doing that, you should do this. It’s able to make recommendations like shopping on Amazon,” said Zeus Kerravala, founder and principal analyst at ZK Research.

Why Is AIOps Important 

AIOps helps companies protect their brand and retain customers by assisting IT teams in keeping their digital systems always on and making them more reliable, Elliot said.

AIOps tools identify problems faster than humans because they correlate data and reduce the complexity, which allows resolution to occur faster, he added. AIOps also plays an important role in addressing the shortage of IT workers, because AI automation can handle some of the tasks performed by humans, said analysts and tech executives. Moreover, AIOps platforms help workers with minimal AI knowledge perform complex AI tasks.

More on Data ModelingWhat Is Data Modeling? Common Tools, Techniques and Model Types.

Benefits of AIOps 

The benefits of AIOps are divided into three pillars, said Gab Menachem, senior director of product management for ServiceNow’s IT operations management business.

First, AIOps allow the prediction of incidents. Second, those insights create an opportunity for prevention. Third, there’s eventually automation of resolutions through AI to gain efficiencies, he said.

These pillars help companies with “cost avoidance,” which is currently a big discussion among enterprises, Elliot said. IT teams can resolve issues faster with AIOps tools which, in turn, leads to improved site reliability and performance and cuts back on costly system outages, he explained.

“By reducing the amount of noise with alerts, you are looking for a needle in a paper bag instead of in a haystack,” said Elliot. 

AIOps also aims to lower the burn rate in budgets, Bill Lobig, vice president of IBM automation told Built In. Budget burn rates account for unplanned time in dealing with IT firefighting, and other metrics that affect operations, Lobig said.

Challenges In Implementing AIOps

Although AIOps has a number of benefits, it also has a downside too. When companies first jump into AIOps, they are often looking to automate their IT tasks as their first step but soon find it requires a hefty investment, Menachem said. 

“AIOps comes a little later in the maturity curve. If you don’t have the basics in place, this is not a place where you want to start,” Xanthos said. “You need to have your basic data collection and monitoring in place before you consider AIOps.” 

Lobig echoed similar sentiments noting companies need to have proper change management policies in place, like continuous integration (CI) and continuous deployment (CD) before leaping into predicting what might happen when using AIOps.

Company culture can be one of the greatest challenges in adopting AIOps. That’s because you ideally need everyone to agree to move to a data-driven decision-making process, Elliot said. 

For example, each IT team may have their own set of tools they are comfortable using so it can be a challenge to shift over into a new set of AIOps tools, he added.

Persuading finance to purchase AIOps tools is also a major challenge, Lobig said. 

“Business buyers are increasingly controlling the spend and wallet share. When someone wants to spend money on AIOps to improve IT operations, someone may ask, ‘How does it help the business?’” said Lobig.

Companies that try to implement AIOps in a horizontal, layer-by-layer fashion across their enterprise may experience more frustration and cost, than zeroing in on specific use cases, Menachem said.

More on Data Science RolesData Analyst vs. Data Scientist: Similarities and Differences Explained

AIOps Use Cases 

In addition to the often cited use cases of reducing the volume of alerts, correlating troublesome events and detecting anomalies, experts cited a handful of other use cases for AIOps.

Topology mapping is another use case, Elliot said. This type of mapping shows the proverbial connective tissue for all the pieces that make up a company’s digital service, from the region where it operates to the product it sells, and zeros in on a potential cause of the problem.

Understanding KPI movement is another use case, said Xanthos. By understanding where your KPIs are trending towards, it allows for companies to plan before something happens, or plot out how a KPI metric might evolve, said Xanthos.

Reducing major emergencies (what Menachem refers to as a P1 outage) is one main goal of IT teams using AIOps, Menachem said. 

“P1 is a total system down, where it’s all hands on deck and you need to solve it immediately. I’d say maybe one to five big customers experience this a quarter, but if they’re using AIOps they have between 25 percent to 35 percent fewer P1s,” Menachem said of his own experience.

The desire to improve customer experience is the largest driver in companies adopting AIOps.

“At the end of the day, you’re going to make an investment here because you care about speed, you care about how your customers will digitally engage with you, and all those digital products and services you’re giving them,” Elliot said. “All of these things have really driven the need for a more effective and efficient operational model and AIOps is part of that story.”