Why AIOps is Crucial for Continued Operations and Future Success

Why AIOps is Crucial for Continued Operations and Future Success-01

Artificial intelligence for IT operations (AIOps) has long been thought of as a way for IT teams to automate mundane tasks. Automation, however, is only a fragment of the benefits that AIOps delivers to the company. AIOps combines the power of AI, big data, and machine learning to offer businesses an innovative and competitive edge while keeping them agile.

The main reason for switching to an AIOps approach is to better manage the massive increase in apps, devices, bandwidth, cyber-attacks, and users brought on by the cloud and the use of cloud applications for work and play. The omnipresence of the cloud was never in doubt, even before the pandemic and the increased reliance on it for survival. With the increasing reliance on AI and cloud technologies, operations must step up and become AI-inclusive.

Using artificial intelligence (AI) in IT operations has a number of advantages for the entire organization and the business as a whole. To promptly troubleshoot issues in today’s digital world, artificial intelligence and machine learning has become essential.

Here are three reasons why AIOps is critical to an organization’s long-term success.

  • Complete visibility is achieved with AIOps

AIOps big data platforms provide organizations with total visibility across systems and the ability to correlate a wide range of operational data and metrics. IT leaders can use an AIOps platform to get advanced analytics and deeper insights across an application’s lifecycle.

IT operations teams can identify an issue faster, ideally before it occurs, with complete visibility into real-time operations data. In other words, with real-time anomaly detection, businesses can be more predictive in their problem solving and take action faster. This observability and complete visibility into operations data is also required for organizations to optimize by using AIOps tools such as machine learning to put automations to work.

  • Increased operational efficiency

IT teams require as much time as possible to innovate and improve the product in order to have a significant impact on a business. They do not have the time to go through mountains of data, analyse it, and decide what to do with it. AIOps can automate these and notify IT leaders when something goes wrong, enabling them to collaborate and troubleshoot to solve the most pressing problems first.

There will always be more data resulting in more work for IT teams as long as there is software — and the amount of data going into enterprise systems is unimaginable. The human brain is incapable of comprehending it on its own. AIOps can automate tasks and boost operational efficiency throughout the entire company by promoting collaboration among teams to establish what’s going on and who’s accountable. With AIOps, events that could take days to handle can be resolved in minutes.

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  • Predicting and forecasting challenges

Teams can use machine learning and AIOps to identify where repeated events occur and analyse trends or similarities across different types of incidents. The most noteworthy benefit of AIOps is that it provides teams with the data and insight they need to collaborate and address future issues before they reach the customer. A system failure can cost a business thousand, if not millions, of dollars in lost time, as well as a negative customer experience. Businesses may lose both revenue and long-term consumers if this happens even once a quarter – or even once a year.

At the end of the day, many IT leaders use AIOps to automate tasks and allow their employees more time to innovate. What started out as a nice-to-have is now a requirement for modern businesses to stay agile, innovative, and competitive. Adopting a long-term AIOps approach and incorporating organizational change management throughout the company can pay off for years.

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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.