Everyone in IT has heard of AIOps by now. You may even be using products or features called AIOps. But what is AIOps, really?
With input from industry experts — both analysts and vendors — this 10-part blog series, to be posted over the next few weeks, will try to answer this question. And explore the advantages, challenges, and future of AIOps.
What Is AIOps, Really?
Put simply, AIOps is artificial intelligence for IT operations.
Scott Likens, Global AI and Innovation Technology Leader at PwC, elaborates, "AIOps is an approach that enhances and automates IT operations processes by harnessing the power and AI/Machine learning, in other words, it uses AI for general troubleshooting and other information technology operations."
Carlos Casanova, Principal Analyst at Forrester Research provides the official Forrester definition: "AIOps is a practice that combines human and technological applications of AI/ML, advanced analytics, and operational practices to business and operations data. AIOps enhances human judgment, proactively alerts on known scenarios, predicts likely events, recommends corrective actions, and enables automation. It is fueled by coalescing and transforming sensory data into AI-enriched actionable information. A retrospective causal analysis and governance structure fuels foundational improvements and trust."
Gartner's definition: "AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination."
These definitions give us a good idea what AIOps does. But the question remains: Is AIOps a feature, a set of capabilities, a product category, or is it simply a buzzword that can refer to a variety of aspects of AI for ITOps?
"Short answer: AIOps can be complicated. It's a product category, but it can also be a technology type, a feature, a buzzword — and everything in between," says Charles Burnham, Director, AIOps Engineering at LogicMonitor.
"People want intelligence in their systems, so they reach for AIOps solutions that promise to solve all of their problems, regardless of what they believe AIOps is," Burnham adds. "There is no arguing that AIOps is a 'thing,' but if you were to ask a group of people to define it, you would get many different answers."
Carlos Casanova from Forrester adds, "The notion is spoken about by most enterprises I engage with. Unfortunately, however, there is still a lot of confusion about what it fully entails and how it differs from Observability, a term that is used somewhat indiscriminately by vendors and enterprises alike right now to mean everything and anything related to data visibility."
Feeling the Buzz
Many experts are concerned that AIOps has inadvertently grown into a buzzword.
"In our view, AIOps is a product category, but the term has become so thoroughly vendor-washed that it has ceased to have a consistent meaning," says Thomas LaRock, Principal Developer Evangelist at Selector.
Gagan Singh, VP of Product Marketing, Observability, at Elastic, agrees, "AIOps is an important topic of conversation in the IT world but has become a muddled buzzword."
The reality is, AIOps is currently a category, a technology type, a feature, and a buzzword, says Asaf Yigal, CTO of Logz.io. "The first three are certainties, and being a buzzword is inevitable as everyone jumps on the bandwagon because there is momentum, and money, in it."
Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at Enterprise Management Associates (EMA) explains, "I think AIOps is overused as a buzzword today, but there is something real there. AIOps refers to technology that leverages AI algorithms (including machine learning), big data, and other analytics technologies to create intelligent systems that streamline and automate IT management. There are some vendors that offer products in this category, but many other vendors are enriching their existing IT management and IT infrastructure solutions with AIOps features to differentiate themselves."
Product vs. Feature
The expert opinions on what AIOps actually refers to vary widely from a set of features to a product category to a technological approach:
■ Yigal from Logz.io: "Within the context of observability, where AIOps is in fact a big deal, this technology is considered an enabler encompassing a set of powerful 'next generation' (feel free to cringe) features that analyze, combine, and collect data."
■ Singh from Elastic: "AIOps is a set of capabilities within a product that applies advanced machine learning and analytics capabilities on operational data along with an understanding of the dependencies between infrastructure and application services to proactively identify the root cause of the problem."
■ Bharani Kumar Kulasekaran, Product Manager at ManageEngine: AIOps is technology that integrates with existing practices rather than as a standalone product. This transformative approach leverages big data and ML algorithms to analyze an organization's data, identify patterns and anomalies, predict potential issues, and provide actionable insights to resolve and prevent them. Holistic AIOps platforms, as well as specific AIOps tools and features, help IT teams proactively monitor, analyze, and manage complex and dynamic IT environments.
■ Dennis Drogseth, VP at Enterprise Management Associates (EMA): "AIOps is far beyond being a buzzword, and in leading-edge solutions, it is also much more than just a feature. EMA views AIOps as multi-dimensional across three overarching use cases: incident, performance, and availability management; business impact and business-to-IT alignment; change impact, capacity optimization, and cloud assimilation."
■ Payal Kindiger, Senior Director of Product Marketing at Riverbed: "AIOps is a market of software products and solutions that apply AI/ML models to operational data such as logs, alerts, performance, and ticketing data across a variety of use cases, including automated insights, root cause analysis, incident prevention, and advanced correlation."
■ Michael Gerstenhaber, VP of Product Management at Datadog: "AIOps is not just a single product, technology, or feature, but rather, a combination of technology and processes, some of which leverage AI and Machine Learning, that are geared toward helping IT teams improve and automate aspects of their IT operations."
■ Spiros Xanthos, SVP and General Manager of Observability at Splunk: "AIOps uses data analytics, machine learning and artificial intelligence to deliver increased accuracy and speed to IT operations. This makes it possible to predict and prevent problems before they turn into customer-impacting incidents. By that definition, AIOps is more of technological enhancement that can be applied to multiple product categories rather than just a product or feature all on its own."
■ Camden Swita, Senior Product Manager at New Relic: "Personally, I think of AIOps as a collection of AI/machine learning technologies that helps us glean meaning or generate action from big data sets — including unconnected/un-normalized data sets — usually for the purposes of automating IT functions."
■ Bill Lobig, VP Product Management of Automation at IBM: "I think the term AIOps explains itself in its most basic sense: AIOps is AI applied to operational data to improve IT outcomes. While I expect the concept will continue to evolve, today it's probably most common to think of AIOps as a product category or type of technology but I tend to think it's not a category as much as it is a technique and approach for improving IT outcomes by leveraging AI to detect patterns that were previously undetectable."
One fact is clear, the industry has a wide range of views on what AIOps really means, in terms of how it is marketed anyway. And I have a feeling it may be too late to solve that problem. But it is important to know this as we embark on our mission to discover AIOps. The bottom line is that vendors are selling — and companies are buying — AIOps solutions, as products or features, however defined.
Go to: Discovering AIOps - Part 2, outlining the must-have capabilities for AIOps.
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