Deciphering how AIOps is evolving and businesses can benefit from it

The modern business landscape has transformed entirely with the likes of emerging technologies such as artificial intelligence, machine learning and automation. Such technologies have also transfigured modern application architectures as well as IT operations. Integrating AI into IT operations enables IT teams to perform more complex tasks and automate problem resolution in complex IT environments. This integration of AI into IT operations has led to the emergence of the term, AIOps that leverages big data, analytics and machine learning capabilities to simplify IT ops management.

According to Gartner, 50% of organizations will use AIOps with application performance monitoring to provide insight into mission-critical applications and IT operations. AIOps vows to help change the operation of IT systems and ensure IT staff focus on more valuable tasks. With increasing interest in AIOps, the global AIOps market is forecast to reach US$3127.44 million by 2025 at a CAGR of 43.7% during the period 2020 to 2025.

While AIOps solutions have the potential to add immense value to a business' IT operations, here are the top AIOps trends business should look for in 2021.

Heightened Enterprise Adoption of AIOps

With growing IT complexity to support business model changes, disruptive technologies have emerged as crucial assets to aid IT teams, so that they can better navigate such changes and foster innovation and growth within their companies. AI in IT operations will help glean and assess voluminous amounts of data and identify both existing and potential performance issues to ensure organizational growth. According to a study, enterprise adoption of AIOps has seen an 83% increase since 2018. This number will continue to surge as the interest in AIOps is showing an upward trend.

AIOps Transition Toward Multiple Data Types

As the AI approach to IT operations can help businesses solve problems before they occur, AIOps solutions use big data platforms to aggregate siloed IT operations data in one place. According to LogicMonitor, data scientists will use AI algorithms that can converge multiple data sets together such as metric, log, and transaction data. This will enable data scientists to understand how they correlate, and what signals can be filtered out from all that noise to troubleshoot the issues.

AIOps Will Bolster Threat Response

Cybersecurity teams can get huge benefits from the implementation of AIOps as it provides them a significant amount of data security visibility and intelligence. As it applies AI and analytics, AIOps automates common IT operational processes, detects and resolves problems, and ensures security. It helps bridge the gap between IT operations and security operations, strengthening system uptime and reliability and detecting anomalies.

Increasing Role of Observability in IT Operations

The concept of observability has recently been applied to the IT ecosystem and cloud computing. It is crucial to IT teams that are tasked with supporting digital business. There are numerous architectures and algorithms required to accomplish true observability. As noted by LogicMonitor, observability in IT defines a system's ability to collect actionable data and examine what is happening and where, and most importantly, interpret why an issue occurred within the system. The fusion of AIOps and observability into one unified platform could allows IT teams to predict problems faster and resolve them before it impacts the business.

AIOps Will Become a Part of DevOps Tool

AIOps provides secured solutions to complex infrastructure management and cloud solution monitoring tools. It can help automate data analysis and routine DevOps operations. As the traditional system monitoring tools are not able to deal with the 3 V's of big data, the emergence of advanced analytics tools, AI algorithms and deep learning models make this effectively happen for DevOps professionals. AIOps help IT departments by processing all the data rapidly, performing deep data analysis and automating routine tasks. It assists DevOps engineers in monitoring and managing testing, performance and security.