According to Insight Partners, the AIOps platform market size is expected to grow from $2.83 billion in 2021 to $19.92 billion by 2028 at a compounded annual growth rate of 32.2%. This skyrocketing growth is fueled by the pace of the IT data deluge getting out of the human hand and the need for resource optimization. Every organization is increasingly producing more IT data, whether in a siloed or unified form.

Organizations today are pressured to keep their IT applications and infrastructure up and running and minimize their downtime. While this has always been a critical goal, it’s become harder to achieve with modern architectures, such as microservices, containerization, edge computing, hybrid-cloud deployments and the newer development methods such as agile DevOps techniques.

Enterprise data systems are like busy family households. You see a constant flow of activity to varying degrees from room to room. This activity includes people wandering, opening and closing doors. And then there are other streams constantly flowing through the household- electricity, water, Wi-Fi networks and more. In modern enterprises, the data deluge is a critical issue. While we take the complexity for granted in a household, such is not allowed in a connected enterprise.

According to Gartner, enterprise usage of AIOps is set to surge from a mere 5% in 2018 to a whopping 30% in 2023. To survive in an increasingly competitive market, MSPs must not only respond well to customer expectations but anticipate them. Another Gartner report states that by 2025, over 80% of public cloud managed and professional services deals will require both hybrid and multi-cloud capabilities from the provider, up from below 50% in 2020.

AIOps or Artificial Intelligence for IT Operations refers to a set of technologies that augment human decisions with autonomous decisions driven by AI and machine learning that learn patterns, relationships from data. AIOps is the term originally coined by Gartner, and pictorially illustrated in the following way.

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.

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.

Observability has gained a lot of momentum in the past year, be it full stack observability or data observability. Modern complex IT systems using clouds, microservices and serverless are easy to develop and deploy but extremely difficult to observe. These systems generate tremendous amounts of data and need an automated way of handling the volume. The next era of delivering customer experience is underpinned by the full stack observability capability.

You’ve probably seen the term AIOps appear as the subject of an article or talk recently, and there’s a reason. AIOps is merging DevOps principles with Artificial Intelligence, Big Data, and Machine Learning. It provides visibility into performance and system data on a massive scale, automating IT operations through multi-layered platforms while delivering real-time analytics.