While IT firms race to bring digital transformation, can they recognize how resourceful AIOps can be?

Thanks to disruption, digital business transformation forces are calling for a change in traditional IT management techniques. IT firms need to figure out what works for them and what turned out badly. But doing so is not an easy task. This is because traditionally, IT Ops teams lack appropriate resources to deal with such complexities. As a result, they end up employing more irrelevant resources to configure, deploy, and manage operations, which may not be very practical. Hence to prevent, identify and resolve high-scale blowouts quickly, IT firms are turning to AIOps.  

Why AIOps?

AIOps is multi-layered technology platform converging big data analytics, machine learning, and artificial intelligence to solve IT issues and enhance operations. It connects three different IT disciplines viz., service management (Engage), performance management (Observe), and automation (Act).  It also facilitates the intelligent sift of ‘signals’ out of the ‘noise’ to identify significant events and patterns related to system performance and availability issues. By replacing multiple separate, manual IT operations tools with a single, intelligent, and automated IT operations platform, AIOps enables IT operations teams to respond more quickly—even proactively—to slowdowns and outages, with a lot less effort. In other words, it has the capability to enhance automation, lessen redundant alerts, and spot irregularities by enabling workflows to be triggered with or without human intervention. AIOps also offers visibility and clarity for the most complex multi-cloud environments, bringing together data from disparate sources. This helps IT teams to see and understand what has happened and what is happening. In addition to that, employing an AIOps strategy can give better analytics for existing data that can be used to find best-suited practices to manage the intricacy and scale of modern businesses’ digital transformation initiatives. According to Gartner, 40% of companies will be switching to AIOps by 2020.  

Challenges and Concerns

Though AIOps shows real promise as a path to success and implementing it sounds logical, there are main hurdles that can deter it from delivering expected results. This is why businesses must address the layers of AIOps if they want to implement AI effectively. First, they need to analyze the challenges and opportunities across the IT operations landscape to identify the use cases that actually require AIOps. Based on the requirements, department leaders can proceed with leverage, best-of-breed big data, AI, and visualization stack for AIOps from vendors. Next, assemble an AIOps team and ask them to start creating technology-agnostic architecture, which has an agile approach. The team can then gather data, build artificial intelligence and machine learning models, gain insights and knowledge, and demonstrate end-to-end AIOPS use cases to deliver value. This includes running code for performance and regression, automatically analyzing the test traffic, early detection of anomalies, and more. There may be chances of AIOps strategy failure despite a meticulous and strong planning and the right team. And reasons for this instance may stem from mistakes during the implementation phase. For e.g., not measuring the business outcomes one wishes to achieve with AIOps, poor training AI dataset, deployment anomalies and so on. Also, execution of too many AIOps initiatives revolves around existing problems without thinking about how to reshape the approach, processes, and organizations to account for new challenges and possible bottlenecks. So, business heads must acknowledge the very real challenges they and their customers face with tools, knowledge, and culture. Then they can map that against maturity and expectations and build solutions that engage with customers in the right way for where they are while providing a path to their desired end state.  

Concluding Thoughts

While AIOps helps the IT Ops become part of the innovation center rather than a cost center, it also goes beyond preventing outages from stopping alarm fatigue, security, and policy violations. And it is also important that businesses pay attention to every aspect of AIOps strategy before moving ahead with implementation.