What is AIOps?

The link between DevOps and artificial intelligence for operations (AIOps) has only started to become clear within the last few years. Monitoring and alerting has evolved from a “black box approach,” where you don’t actually know what’s happening, into observability, where you have access to data that provides everything you possibly need to know about your IT systems. 

How does AIOps come into play? AIOps is the practice of applying artificial intelligence, machine learning, and advanced analytics to automate and improve IT operations. Since it entered as a formal discipline with Gartner in 2016, IT teams have been trying to figure out how to employ it to make their lives easier.

What are the Benefits of AIOps?

For today’s businesses, there’s a premium on the delivery of an optimal digital customer experience — all the time and every time. The most apparent benefit of AIOps is how it enables teams to do more with less — operating faster, more efficiently, and with more knowledge at your fingertips — so they can scale. And, because AIOps aggregates disparate data sources to provide insights that we can’t always see ourselves, there’s a decreased risk of manual errors or oversights.

With advanced machine learning, another advantage of AIOps is for discovering historical insights. These analytics provide actionable insights based on aggregated data over a certain time period, allowing teams to identify what kind of issues tend to recur and where efficiency gains can be made.

AIOps also shortens the triage and incident life cycle. An incident is not usually caused by just one thing, but rather a unique set of events. AIOps gets IT ops engineers closer to cause analysis by providing all of the data that they need upfront for an initial triage. It’s common for knowledge of a particular issue to be limited to just one person within a DevOps team. AIOps provides a way to democratize your organization’s tribal knowledge by providing full visibility to everyone, pooling team knowledge for everyone to be as effective as possible. In turn, this shortens the duration of impact for an incident, so customers are back up and running as quickly as possible.

Bringing efficiency to the very bottom of the tech stack, AIOps has a cascading effect downstream to improve MTTR and reporting, and subsequently create a better user experience that translates into happier customers and higher profitability. 

How Does AIOps Work?

In today’s extremely complex systems, developers and engineers are faced with floods of alerts, and yet, there is only a handful that really matters. Alert fatigue is common, which means critical alerts are often buried and ignored. With an AIOps solution, you can correlate, suppress, and prioritize alerts. This means that your team can focus on issues that are the most critical to reliability.

In short, AIOps works by providing IT teams with enriched insights and automation so they can find and resolve problems faster.

Advice for Easier AIOps Adoption

From the get-go, it can be tricky to accurately size up what you need from AIOps and estimate how much time and effort it will actually require to integrate it into your systems. While enterprise at scale organizations may require highly specialized experts or data scientists, small to midsize companies typically do not. It’s worthwhile to take the time you need to scope out what you actually want to accomplish, understand your business needs and find the right partners that are going to help you deploy intelligent technology in a way that makes the most sense for your business.

When it comes to understanding the necessity for AIOps, there’s often a gap in understanding between your IT ops leadership and your executive stakeholders. To tackle that challenge, find the best-value use case (the one with the lowest effort but highest impact) that an IT ops leader can use to explain to executives why you need to deploy your AIOps project. Your business case should demonstrate why it’s worth the investment not only in terms of resolving issues faster but also the time-savings for your team — releasing them from toil to do more valuable work.

A typical response to the proposal of an AIOps project is the fear of change. Employees enjoy the familiarity of the tools they already have and can be resistant to change. It’s important to clarify that AI technology isn’t automating someone’s job away, but rather automating toil so employees are freed up to work on higher-value tasks. Similarly, leadership has the perception that an AI tool will require a lot of training to properly use it. The truth is, most organizations can leverage solutions with built-in data science so they can enjoy the data science benefits of AIOps without data scientists. In general, to the extent that you can plug into existing tools, processes, teams, and skills to make them all work better will enable AIOps projects to be faster and less of a lift. 

Embracing AIOps

The danger of a black box approach is that you simply can’t understand why or how the system got the results. If you’re going to trust an algorithm to make your job easier, you want to be able to verify that it’s doing the right thing. Developing trust in your AIOps solution starts with having visibility from the inside-out. For example, event correlation and automation platform BigPanda allows users to have full control over which correlation patterns are active at any given time. They can review them, test it against their own data, tweak it if they need to, and test it again, so they can have full confidence before deploying to production. 

With the pandemic accelerating the push for AIOps, it has shown no signs of slowing down. Gartner predicts that large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. Gartner also anticipates that nearly a third of large enterprises will be using AIOps tools to monitor applications and infrastructure by 2023. Moving forward, organizations are looking to scale intelligently by building efficiency into every layer of incident management.

There’s a general tendency to let AIOps adoption drag on with the belief that it will take a year or two to get the right value out of it. In reality, it shouldn’t take that long. Platforms like xMatters help simplify and expedite that process. To find out more about xMatters AIOps capabilities, sign up for a free instance today.