The AIOps market share is dominated by the platform segment! Explore more here

AIOps merges artificial intelligence algorithms and human intervention to provide full visibility into the performance of IT systems. The global AIOps market size was valued at US$26.33 billion in 2020 and is projected to reach US$644.96 billion by 2030, registering a CAGR of 37.90%. Generally, DevOps emphasizes a change in culture and process. AI-powered DevOps in the IT ecosystem can ensure greater speed, better accuracy, consistency, and reliability, and multiplies the number of deliveries made. It minimizes the chances of manual errors and failure by expediting automated testing procedures and managing other tasks across the cross-project requirements.

However, in scenarios where the IT team needs to derive value from data aggregated from sources like cloud infrastructures, third-party systems, IoT devices, and mobile systems, relying only on DevOps-based automation is not possible. This is where AIOps comes to the rescue. It not only improves automation but also offers IT operations teams a real-time understanding of issues affecting the availability or performance of their systems. AIOps can analyze data about the current IT processes in the DevOps workflow and extracts significant events related to slow-downs or outages, using big data analytics and machine learning while also providing actionable and contextual intelligence.

In 2020, the global AIOps industry share was dominated by the platform segment and is expected to maintain its dominance in the upcoming years. Conversational AIOps platforms come in all shapes and sizes. Some are point solutions to address specific needs, and others such as Teneo provide enterprise needs to develop a global conversational AI interface. By application, real-time analytics is estimated to emerge as the fastest-growing application. It applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real-time simply means the analytics is completed within a few seconds or minutes after the arrival of new data. On-demand real-time analytics waits for users or systems to request a query and then delivers the analytic results. Continuous real-time analytics is more proactive and alerts users or triggers responses as events occur.