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How a New Approach to AIOps Can Help Organizations Reduce Network Costs and Improve Business Outcomes

Kelly Baig • August 05, 2021
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AIOps has caught the attention of many organizations seeking to harness artificial intelligence (AI) and machine learning (ML) approaches to reduce operating costs and burdens while raising service levels and outcomes. AIOps is an especially good fit for infrastructure which produces high volumes of complex data at velocity. No set of infrastructure fits this profile better than modern networks.

Modern networks are richly diverse with multiple brands, devices and software spread across many sites and geolocations – all of which must work together to enable efficient and secure use of the network. This complexity can challenge IT teams who need comprehensive data telemetry and correlated analysis to diagnose root cause problems, apply effective remediation, and enable a constantly improving and proactive approach to network operations.

How can AIOps transform network operations?

There are a lot of different factors that can contribute to network problems, and especially to network slowdowns. These are particularly difficult for network administrators to troubleshoot and remediate. According to a Gartner study, 70% of network admin time is spent trying to diagnose and troubleshoot network issues.

Many teams never get to the bottom of the issues they encounter. They often give up before they can figure out what’s going on and they end up with a network that’s not optimized to provide the most value. That productivity issue not only affects the IT teams, but also the people trying to use the network. It slows everything down from a productivity standpoint, including the applications and end-users.

AIOps when implemented well, can provide the opportunity to take a more automated, systematic, and guided approach to understanding what’s going wrong in the network. Teams can save that time and apply it to something more valuable.

How AIOps Can Help Organizations

Networks generate a lot of data. The problem is there’s too much data, making it difficult to sort through. How do you know what’s important to look at? How do you know what data point will help you figure out what happened and how to fix it?

That’s where AIOps can be valuable because an effective automation approach can screen through all that data, by knowing in advance which indicators are key to examine and which thresholds matter. It already understands the most common problems and fixes so it can guide the network admin through the process of remediation without having to understand for themselves how to fix the problem.

Network admins don’t want to lose control and visibility. By leveraging AIOps solutions, they don’t have to: they can access a step-by-step approach that recommends what step they should take, and then validate it before any action is taken. Once the green light has been given, it will automate the step, so it removes the human error factor.

This is a great way to train newer people, helping to have a less skilled, less experienced type person to be successful.

For more details, please visit our BPE microsite.

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