Like many tools and trends in IT and development, AIOps has evolved from a new concept to a popular term that is on the lips of many IT leaders. At its core, AIOps is simple and practical — it's the practice of using artificial intelligence (AI) and machine learning (ML) to automate IT operations.
But as AIOps has permeated the technology vernacular over the last few years, to many in the tech world, its true purpose has been clouded by hype, myths and misinformation. It's a term that many enterprises have latched onto thinking it will be a panacea. Before reaching for AIOps as a solution to IT ops challenges, let's explore some of the top myths surrounding AIOps — what it is, what it isn't, and what IT ops challenges it can solve.
Myth — AIOps is expensive and difficult to implement
Not all AIOps tools are alike, and this myth likely emerged from some specific AIOps tools that require a significant time commitment for configuration, training and onboarding. Some of these tools can seem like a black box with regard to how correlations are performed, and they often leave users in the dark as the machine learning takes place.
Reality - Not every AIOps solution takes weeks or months to start delivering value. Pre-trained ML models can begin bearing fruit immediately without any steep learning curves, training periods or prohibitive pricing.
Myth — AIOps is only accessible for large companies. It is more trouble than it's worth for small teams
Here again it's important to think about the purpose of AIOps. Why wouldn't a small team benefit from discovering operations problems and correlations faster and with less human intervention?
Reality — While a large-scale AIOps deployment requiring months of training may be out of reach for a smaller company, the pre-trained tools mentioned above are accessible for even the smallest of engineering teams.
Myth — AIOps is just marketing hype around vaporware and repackaged solutions
I can't promise that every AIOps product is the real deal and that there aren't vendors exaggerating the capabilities of existing solutions with smoke and mirrors. However, many AIOps solutions are the real deal, and have already provided measurable impact.
Reality — Innovative DevOps and SRE teams are now using AIOps to detect and prevent problems before they reach the customer, cut down on alert noise, and identify the root cause of problems faster than ever before. The market is also validating AIOps as a legitimate tool. Gartner estimates that 10% of organizations use AIOps today, but that number is expected to grow to over 40% over the next two years.
Myth — AIOps is only valuable if it is used in a large-scale deployment
I am finding that some enterprises think they need to search for a large, hairy problem to which to apply AIOps. With this mindset, these companies often struggle to successfully get their implementation off the ground and check off every box on their wish list.
Reality — In many cases, an enterprise will see more value, faster, by automating smaller processes and taking a step-by-step approach. The far-reaching, cross-department AIOps deployment isn't easy to achieve, and waiting around for it could cause teams to miss out on effective solutions on a smaller scale. Over time, these smaller automations will add up to deliver a result that is greater than the sum of its parts.
Myth — AIOps is just a fancy term for aggregating alerts and reducing noise
There is an element of truth to this myth, in that aggregating alerts is one of the most common use cases for AIOps. However, AIOps is much more than just reducing noise.
Reality — The best uses of AIOps proactively detect unusual changes and anomalies, prevent potential problems before they impact end-users, and provide root cause analysis of issues. AIOps tools can become a vital aspect of observability by rapidly helping teams prioritize their responses to problems, with the right people getting alerts at the right time, but more importantly, insight into the causes of problems and how to fix them, enabling them to take faster and more effective action.
Myth — AIOps replaces human work
This one may be a result of that marketing hype-AIOps does not eliminate the need for human work, it simply shifts its focus so IT teams can be freed from certain manual tasks and focus on more strategic ones.
Reality — To put it simply, AIOps allows engineers to redirect their energy from firefighting to fireproofing. It frees up toil, manual troubleshooting time, costly war rooms and guesswork, enabling teams to instead work proactively—building better software, more resilient systems, and automated processes that help their teams move faster.
Myth — AIOps automatically remediates problems without any human intervention
This is a myth that may one day turn into a fact — in the future, AIOps could be able to solve problems without requiring any intervention on the part of the engineer at all. We aren't quite there yet.
Reality — Currently, the majority of practical AIOps use cases are about detecting problems, reducing the flood of alert noise, and providing insight into likely root causes to lead engineers to the right solution in the most efficient manner. However, the future may not be too far off. Some observability and AIOps solutions already provide seamless integration into incident management and automation tools, allowing engineers to trigger remediation workflows.
So, while AIOps may not be the cure-all solution for every challenge facing SRE and DevOps teams, it can provide immediate benefits to most organizations through proactive anomaly detection, root cause analysis, reduced alert noise, and enhanced observability capabilities. And as models improve and technology matures, we may someday see some of the optimistic myths around AIOps become reality. For more information on demystifying AIOps, see this infographic.