What Is AIOps?

AIOps, short for artificial intelligence for IT operations, refers to the use of machine learning and big data to automate IT processes. The discipline is still relatively new, yet many companies use it in their network and security operations. In fact, the global AIOps industry is on pace to exceed $32 billion by 2028.

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

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What Is AIOps? 

AIOps involves collecting data from multiple sources, then using AI and machine learning to process and analyze the information, ultimately identifying the root cause of problems and quickly assisting in resolving them. 

AIOps helps IT teams understand patterns, detect anomalies, automatically remediate troublesome issues and make predictions, Stephen Elliot, group vice president of I&O, cloud operations and DevOps at IDC, told Built In.

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 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.

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How Does AIOps Work?

AIOps platforms gather massive volumes of data, including historical, network and infrastructure data. This data is then processed and analyzed by algorithms and machine learning models, which can distinguish specific data events from common noise, identify patterns and learn over time through experience. 

The process for how AIOps adapts to a company’s IT infrastructure can be described in three steps: 

1. Observe: AIOps platforms compile large amounts of data from various sources, ideally collecting data in real time. This approach is meant to break down data silos and maintain a holistic view of a company’s IT operations. Platforms then analyze this data to look for patterns and assess performance, among other needs.
2. Engage: IT and operations personnel step in to take appropriate action, based on the AIOps platform’s findings. They may reorganize IT workloads, address bugs, follow up on alerts and take other steps to handle a situation.
3. Act: The ultimate goal of AIOps platforms is to automate IT processes. During this phase, platforms automatically take steps to enhance and monitor IT workflows. IT and operations teams can also teach a platform certain responses and enable it to learn from various scenarios.   

What Are the Three Stages of AIOps?

Another way to visualize what AIOps looks like in action is to break down how it resolves issues into three distinct stages: 

1. Detect

In the initial stage, AIOps platforms can identify issues by evaluating historical and performance data. They can then report problems like overloaded devices, workflow bottlenecks and cyber attacks before they grow into larger issues.

2. Predict

Through data collection and analysis, AIOps platforms can begin to predict potential issues before they occur. Platforms can analyze historical data and information they gather from scenarios to better detect anomalies and anticipate issues.

3. Mitigate

A fully mature AIOps platform is able to identify issues and take steps on its own to contain and resolve the situation. In this stage, platforms can fully automate IT processes and give IT personnel the information they need to handle more complex scenarios.

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

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.

KPI Analysis 

Understanding KPI movement is another use case, said Xanthos. By understanding where their KPIs are trending, companies can plan before something happens, or plot out how a KPI metric might evolve, said Xanthos.

Major Outage Reduction

Reducing major emergencies is one main goal of IT teams using AIOps, according to Gab Menachem, senior director of product management for ServiceNow’s IT operations management business. Because of machine learning’s predictive capabilities, AIOps platforms can anticipate issues before they occur and take the appropriate measures to solve problems before they get out of hand. 

Customer Experience Improvement

The desire to improve the customer experience is the largest driver in companies adopting AIOps. Teams can use AIOps platforms to identify bugs and other issues early in the development process, leading to higher-quality products and customer interactions.  

“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.”

Benefits of AIOps 

Integrating AI into IT operations provides numerous advantages to organizations, such as:

Improved Incident Responses 

Machine learning’s ability to distinguish between noise and particular data events enables AIOps platforms to quickly pinpoint anomalies, diagnose situations and deploy automated responses or alert appropriate personnel. As a result, IT teams can more quickly resolve issues. 

Efficient IT Operations 

AIOps platforms can learn over time how to perform certain tasks or respond under specific circumstances, automating vital IT processes. Human personnel are then freed up to work on higher-level challenges, allowing IT teams to better manage their time and resources.

Lower Budget Costs 

AIOps also aims to lower the burn rate in budgets, according to Bill Lobig, vice president of IBM automation. Budget burn rates account for unplanned time in dealing with IT firefighting, and other metrics that affect operations, Lobig told Built In. Avoiding major, prolonged issues saves organizations both time and money in the long run. 

Reliable Infrastructure

Because AIOps systems have the ability to predict and resolve issues before they occur, these platforms can help companies avoid outages and workflow disruptions. This leads to more reliable IT infrastructure that is consistently up and running. 

Unified IT Environment  

AIOps platforms gather data from a range of sources across an organization’s digital ecosystem. This approach removes any data silos and provides a more holistic view of a company’s entire IT environment, making it easier to monitor and protect all assets. 

Challenges in Implementing AIOps

Although AIOps has a number of benefits, it also has some downsides that businesses should consider:

IT Environment Requirements

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. 

“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.

High-Volume Data Demands

The algorithms used by AIOps platforms require massive volumes of data to develop accurate analyses and predictions and learn from past results. In addition, they perform best when fed a continuous stream of real-time data. If organizations can’t meet these data needs, it may not be worth investing in an AIOps platform. 

Data Quality Issues  

Data that is messy, incomplete or flawed in other ways can impact AIOps platforms’ ability to provide accurate predictions and insights. Making changes to an IT environment can also affect the data itself, requiring platforms to re-learn processes based on the new data. 

Tech Stack Adjustments 

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.

Initial Upgrade Costs  

Persuading finance to purchase AIOps tools can also be a challenge. 

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

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

Frequently Asked Questions