If I had to pick one technological innovation that is poised to exert the greatest impact on the current decade, I’d place my bet on AIOps. Just as virtualization was the defining technology of the 2000s, and cloud computing fundamentally reshaped the technology landscape during the 2010s, AIOps is laying the groundwork for another phase of transformational change during the 2020s.
To prove the point, let me explain what exactly AIOps means, and why now is the time that it is taking off.
AIOps refers to the use of AI and machine learning to support IT operations teams and processes.
IT operations is the discipline that sets up infrastructure, deploys applications, monitors application environments to maintain performance and availability, and so on. It has been around for decades; indeed, as long as there has been an IT industry, there has been IT operations.
Traditionally, however, IT operations were powered by manual, ad hoc processes. Operations teams rarely leveraged data to drive their workflows – and if they did, they typically analyzed and reacted to that data manually.
AIOps changes this by allowing IT operations teams to take full advantage of modern AI to improve visibility into IT systems, as well as to automate many operations processes. Instead of having to rely on IT engineers to identify a problem with a server and fix it manually, for example, AIOps can use algorithms to identify and resolve the problem automatically. Likewise, rather than requiring IT staff to determine how best to configure an application or how many resources to allocate to it, AIOps can provision environments automatically by parsing data to determine the optimal settings.
Why Now is the Time for AIOps
AIOps is not an entirely new concept. The term was introduced in 2016 by Gartner to refer to an emerging set of IT operations tools and strategies that leveraged AI.
Yet it was not until more recently – the last year or so – that AIOps’s moment fully arrived. That was due to the convergence of several technological trends that did not play out fully until the end of the 2010s, including the following.
The modern container revolution began with the launch of Docker in 2013. But it was only in the past few years that widespread adoption of Kubernetes made it practical for organizations to deploy containerized applications on a large scale.
What does containerization have to do with AIOps, you ask? The answer is that the switch from conventional application hosting technologies (like virtual machines) to containers has added a magnitude of complexity to application environments. Containers represent another layer of infrastructure that didn’t exist before. They also make applications more dynamic since individual container instances spin up and down constantly.
Maintaining visibility into fast-changing, multi-layered environments built with containers requires the automation that only AIOps can deliver.
Along similar lines, by the end of the 2010s most organizations had migrated a significant portion of their application architectures to microservices. Like containers, microservices applications (which are often hosted using containers, but don’t have to be) are fundamentally more complex than their predecessors because they consist of multiple services, each starting and stopping at different times. They also typically involve complex internal networks that allow communication between microservices using an array of dynamically configured endpoints.
This complexity, too, can be mastered only with the help of AIOps. Attempting to configure and monitor microservices applications manually is just not practical, at scale.
I’d argue, however, that it has only been in the last few years that DevOps’s cultural impact has become truly pervasive. For a while there was pushback against DevOps from folks who claimed that it undermined developers or harmed QA.
But that sort of talk has largely disappeared over the past two or three years. At this point, few people question the cultural priorities that DevOps promotes, which include automation and seamless collaboration between all stakeholders within the application delivery and management process. AIOps promotes both of those goals, especially in complex, fast-moving environments where collaboration between teams is impossible without the visibility and automation that AIOps adds to traditional IT operations.
The final reason why AIOps has become so critical today involves security. Identifying and remediating security threats is only one of the use cases for AIOps, but it’s a powerful one in an age when the cost of security breaches is steadily increasing, and security incidents seem to be a never-ending challenge for businesses large and small.
AIOps probably won’t be a silver bullet that makes all security threats disappear overnight. But it is poised to help push the needle in the battle for keeping applications and data safer from attackers – a battle which, to be frank, the IT industry has done a pretty poor job of fighting using other methodologies over the past couple of decades. By helping to automate not just threat detection but also remediation, even in highly complex environments, AIOps may just prove to be the solution for actually making progress against pervasive security threats.
AIOps has already started transforming the way IT operations teams work and collaborate with other stakeholders. Going forward, as applications become even more complex and the demand for automation and collaboration grows more important, AIOps tools and methodologies will become inseparable from successful IT strategies.