Operations | Monitoring | ITSM | DevOps | Cloud

One of the core value propositions of AIOps platforms is to increase IT efficiency & productivity by applying AI & ML techniques to perform Alert Noise Reduction. This in turn translates to direct cost reduction due to savings in IT man-hours. In this approach, the AIOps platform kind of becomes like a gatekeeper for all the IT alerts/events, and it can help effectively, reduce and correlate such events, so as to send meaningful incidents to NOC or Service Desk.

Gartner introduced the word ‘AIOps’ back in 2017, and ever since, the enterprises have been adapting to the various strategies to streamline their IT operations. AIOps is a successful venture because it meets the challenges and tackles the amount of data created in the tech infrastructures amid complex architectures.

Making IT operations simpler – which AIOps does by helping teams to make smarter, more informed decisions about complex monitoring and APM problems – is great. But what would be even greater is eliminating the need for IT teams to make decisions at all – a prospect known as NoOps. By automating application management to the point that human involvement is no longer necessary, NoOps offers tantalizing possibilities for the IT operations teams of the future.

To date, AIOps has been a solution first and foremost for IT operations teams. In other words, AIOps has been used primarily to help IT teams manage what happens in the post-deployment part of a CI/CD pipeline, when they need to detect and remediate issues in production environments. That doesn’t mean, however, that AIOps leaves developers out of the picture. Although the conversation surrounding AIOps hasn’t paid a lot of heed to developers so far, it’s perhaps time to change that.

According to a public sector survey of federal employees, some useful insights were discovered.

The security stack is a crucial part of any company’s IT infrastructure. However, Security teams increasingly report that traditional SIEM solution approaches are “costly, complex, and resource-consuming,” according to a recent ESG survey. Fortunately, there has been significant innovation in how firms approach cybersecurity with new cloud-native technologies stack and breaking free from vendor lock-in and giving themselves more flexibility, cost advantage, and future-proofing.

Modern workflows are primarily aimed at one thing—reducing operational complexities so that stakeholders can focus on initiatives that boost business and innovation. For IT teams, Artificial Intelligence and Machine Learning play key roles in bringing this goal to life. And even though AIOps is considered to be not yet in mature stages, there is no denying that IT teams that do not adopt AI processes will be left behind. By 2023, the market for AIOps tools is predicted to reach $11.02 B.

As an idea conceived by Gartner four years ago, AIOps is already a mature practice. But it is also one that continues to evolve as businesses turn to AIOps to support new use cases, and as AIOps vendors build better and more efficient AIOps tools. That fact begs the questions: what’s next for AIOps? What are the relevant trends that will shape the future of AIOps over the next several years, and how will AIOps use cases evolve going forward?