AIOps is on pace to become one of the trendiest buzzwords of 2021. Like all buzzwords, AIOps is partially mere hype, and partially true innovation. Where does the distinction lie? Read on what AIOps actually means and whether or not your team needs AIOps tools.
What Is AIOps?
AIOps, which is short for Artificial Intelligence for IT Operations, is a term coined by Gartner in 2016 to refer to tools that use AI and machine learning to help perform IT operations tasks based on data sources about IT environments.
It has been only in the past couple of years, however, that AIOps has become massively popular, with vendors like Splunk and AIOps rolling out a variety of AIOps tools or features that promise to make IT teams’ lives easier.
Is AIOps New?
Most AIOps tools are not totally new. They’re monitoring or security tools that originated long before AIOps became a thing.
Indeed, the concept and technology that we today define as AIOps has arguably been around in one form or another for decades. For instance, monitoring tools like Nagios, which dates to the mid-1990s, have been collecting data from software environments and automatically analyzing it for signs of problems for many years.
To be sure, Nagios’ performance monitoring and predictive analytics features are pretty primitive. The tool primarily parses monitoring information to determine whether any statistics have surpassed a predefined threshold. It doesn’t apply advanced AI or machine learning algorithms.
Nonetheless, you could make a plausible argument that tools like Nagios are essentially AIOps tools. If you take this view, then AIOps is hardly new.
That said, most modern AIOps tools offer much more sophisticated levels of operational analytics functionality. To the extent that AIOps is actually innovative, its value stems from its ability to apply advanced AI to solve IT operations problems, rather than the simple data analytics that tools like Nagios have supplied for decades.
What Problem Does AIOps Solve?
The main benefit of AIOps is that it reduces the time and effort required to perform IT operations work. By leveraging machine learning and advanced data analytics to interpret complex data sets, AIOps tools help IT teams work faster and smarter.
This is particularly valuable in the context of today’s large-scale, endlessly complex IT environments. When you are managing dozens of applications and services that straddle multiple clouds and data centers, it can be challenging to make sense of all the data available to your team. AIOps tools can analyze it for you automatically and in real time, and even make recommendations about how to resolve problems or improve performance.
Some AIOps tools go a step further by automatically remediating problems, too. For example, if your AIOps-powered monitoring software detects that a server has crashed, it could automatically restart it (or start a replacement) for you.
Common use cases for AIOps so far include application performance monitoring and management, where AIOps tools can help to automate the detection and interpretation of anomalies caught by monitoring systems. Security operations and DevSecOps teams, too, can leverage AIOps to automate threat detection. In theory, though, AIOps could improve any type of IT workflow or task.
When Do You Need AIOps?
If your IT operations are running smoothly enough using the tools and processes you already have, you don’t need AIOps. Or, if the software environments you manage are relatively simple--you have only one cloud and a handful of services to manage, for example--AIOps tools will offer less benefit.
In contrast, AIOps is valuable for large-scale, multi-layered environments. If you have to support thousands of servers and users, and a diverse array of services and applications, AIOps tools will make your job much simpler. It may also reduce the number of staff members you need.
How Does AIOps Impact Other Technologies?
AIOps is not a standalone technology or tool. It’s integrated into other tools. In theory, you can take any standard IT tool--like a monitoring or performance management solution--and add AIOps to it by implementing features that help process data or automate workflows via AI and machine learning.
So far, however, not every type of IT tool on the market offers AIOps features. If you decide you want AIOps, you’ll need to restrict your tool acquisition strategy to solutions from vendors who have embraced the AIOps trend.
How Long Does It Take to Adopt AIOps?
The time and effort required to adopt AIOps depends on which tools you deploy that incorporate AIOps. In many cases, using AIOps features is no harder than using the other features in these tools.
However, you may need to write policy files that help guide the behavior of your AIOps tools. You will also have to think about how to integrate AIOps efficiently into your existing IT workflows: If an AIOps-powered automated remediation feature eliminates the need for an on-call engineer to fix a problem, for instance, that change will have to be reflected in your incident management strategy and playbooks.
For now, most enterprises are in the early stages of AIOps adoption, if they have started at all. Data from Omdia shows that intelligent IT automation--a category that includes AIOps tools--is not yet a leading priority for most businesses, but nonetheless ranks on the list of IT initiatives for 2021, as highlighted in this chart (which ranks the top IT spending priorities for about 5,000 enterprises worldwide):
Learn more about AIOps
Gartner (which, again, invented the term AIOps) remains one of the go-to authorities on AIOps. Its market guide to AIOps platforms is useful for understanding which tools and solutions are actually available. In addition, Omdia offers a deep dive into AIOps’s impact on IT, as well as on the relationship between AIOps, DevOps and cloud.
Conclusion
AIOps is relatively new as a buzzword, but the technology behind it is less innovative. Nonetheless, AIOps offers some opportunities for improving operations such as software monitoring and security incident resolution. AIOps will likely evolve to become more useful as AIOps tools mature and become more prevalent in the enterprise.
