Why you need an AIOps strategy today

author imageBy Ajay Pandya|Oct 12, 2021|7:30 am CDT

IT departments are overwhelmed, even on a good day. There are so many systems to monitor and manage—along with constant pressure to make everything run smoothly and more efficiently. It’s not unusual for IT engineers to spend 20+ hours every week troubleshooting the network.

Artificial Intelligence (AI) and data science have the power to revolutionize IT operations by automating many of the daily tasks responsible for slowing the pace of IT, corporate change, and digital transformation. Algorithms powered by AI and machine learning make it possible to supplement the brainpower of IT staffers, enabling them to troubleshoot the network faster and accelerate security threat identification and response.

So, how are IT executives implementing AI technologies and what are they recognizing as a result? AI-powered automation is often applied via the maturing field of Artificial Intelligence for IT Operations (AIOps). In fact, a new study by ZK Research finds that 64% of companies are already using AIOps technologies, with 55% leveraging this technology for benefits across both the network and security. The era of AIOps is here, and the business benefit of AIOps is a topic we explore in depth in webinar, The State of AIOps: Why You Need a Strategy Today.

The webinar, featuring Zeus Kerravala, Principal Analyst at ZK Research, and Ray Watson, V.P. of Innovation at Masergy, will examine why more decision makers are adopting and trusting AIOps technologies in 2021. The webinar will delve into AIOps use cases, as well as the essential investments IT leaders must make to help prepare for AIOps. Attendees will get:

  • Top use cases for AIOps
  • Trends in deployment and success measurement
  • Tips for preparing for AIOps

What is AIOps?

The Gartner analyst firm originally coined the term AIOps in 2016 to categorize solutions that analyzed the “big data” generated by IT systems. Interestingly, some refer to AIOps as “Algorithmic IT Operations,” which is arguably a better way to think about the technology, which applies AI to the problems of IT operations. Specific focus areas for AIOps include automating IT ops processes, getting better at monitoring performance and correlating events that occur within the extended IT ops landscape.

The goal of AIOps is to facilitate transformative efforts in IT. As systems and data migrate to the cloud, IT ops teams are expected to deliver a more agile, intelligent, and efficient form of IT. This means handling the outputs of new, fast-paced development processes such as DevOps — coupled with responsibility for new devices, e.g., the Internet of Things (IoT).

With AIOps, the IT ops team can become a bigger force for transformation. How does that happen, exactly? AIOps is more than just an insights generator. It can also control network and security systems, which means it not only generates continuous insights, it also recommends improvements and then has the power to act on it’s own recommendations. Thus, it can automate both system analysis as well as the repair. In this sense, AIOps is analogous to the lifecycle best practice of “continuous integration / continuous deployment (CI/CD),” only in this case, it’s applied to core IT functions rather than the world of software development.

How AIOps works

AIOps is all about analyzing data that emerges from the entire IT environment, inclusive of applications, servers, storage, networks and IT operations management systems themselves. In practical terms, this usually means allowing it to access a company’s main big data platform. Too much analytical horsepower is required for AIOps to work in a segregated analytics environment. These engines can be easily stymied by patchworked legacy networks that haven’t been modernized with virtualized technologies, cloud-based systems, and a software-defined infrastructure.

Regarding process, AIOps solutions take normalized data from IT systems and put it through machine learning algorithms that detect abnormal behavior from devices, applications or users. The objective is to reduce noise, accelerate the work of IT staffers, and pinpoint with accuracy the root causes of outages and incidents. This typically involves anomaly detection, wherein the AIOps engine looks at current events in the context of past behaviors. It’s this automated analysis that saves staffers hours of detective work. And with today’s complex IT environments, this value cannot be understated.

Using this intelligence, the AIOps engine can then make recommendations for network configuration fixes or security threat responses. And the most advanced engines have access to network and security control panels, allowing the IT manager to approve of the engine’s proposed repair and boom — the changes happen automatically. As AIOps engines are fine tuned, they can even be trusted to act alone. Here’s how analysts at Nemertes Research guide IT leaders in using AIOps to build an autonomous network.

Understanding the state of AIOps

ZK Research surveyed over 500 US IT executives on their use of AIOps at companies with revenue between $250 million and $10 billion. More than six in ten companies surveyed now use AIOps, while a further 12% are interested in exploring the use of the technology. Retailers turned out to be the most common users of AIOps, followed by healthcare, which led the way in the process of evaluation.


of companies are already using AIOps technologies


leverage AIOps across both the network and security

With adoption reaching the majority, AIOps is well on its way to becoming a core element of a successful IT operations program. Embrace it or be left behind. Organizations that have not yet started planning for AIOps would be wise to begin thinking about an AIOps strategy. The benefits to the business are many. Indeed, AIOps capabilities may soon be seen as essential to the pursuit of successful digital strategies—if they are not already viewed this way.

Learning how other businesses are already benefiting from AIOps is the best way to shape your own strategy. What is driving companies toward AI, and what are the main reasons companies are adopting AIOps? Exactly how many trust AIOps to manage network and cloud application performance? And how do they deploy and quantify their AIOps return on investment? The new study from ZK research answers all of these questions and more.

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