Posted on September 8, 2022

In today’s digitally driven economy, the IT network has become a high-stakes asset and yet it’s more difficult to manage than ever. Consider that:

  • Distributed workforces and cloud technologies are the best strategies for making digital services accessible anywhere at any time, but those strategies can have a negative impact. The corporate IT team is rapidly losing visibility and control with more than 75% of WAN traffic now starting and stopping outside the enterprise infrastructure, according to research from Nemertes.
  • More than 50% of a network engineer’s time is spent troubleshooting network systems and managing cloud application performance. That’s like a painter spending 50% of their time washing their paint brushes. These complex environments make the human brain no longer the right tool to effectively evaluate the web of distributed users and network traffic patterns in real-time all around the clock.
  • AI-powered automation is the answer. According to Gartner “…organizations that automate more than 70% of their network change activities will reduce the number of outages by at least 50% and deliver services to their business constituents 50% faster.”

Artificial intelligence for IT operations (AIOps) is the emerging technology helping companies automate processes and manage increasingly complex environments. But what is AIOps and what do you need to build a fully autonomous network? A new buyer’s guide from ZK Research uses research data to shed light on how IT leaders are using AI-based automation and what they find most important in their AIOps solutions. Plus, it explores the lessons learned from early adopters.

Here are the top three tips from the buyer’s guide.

AI doesn’t equate to automation

A majority of respondents (65%) chose their AIOps provider based on features including analytics, predictions, recommendations, and integration.

The top criteria IT leaders use to select an AIOps provider

Companies looking to leverage AIOps for its automation power will want to explore what analytics are available inside the engine, how they can be used to solve business use cases, and whether the toolset can actually reduce the need for human interaction. That requires some deep investigation from the IT decision maker.

How to understand what’s powering your AI engine

ZK Research says one way to tell is to look at the analytics inside the engine, understanding how it:

  • Uses machine learning, behavioral and predictive analytics to do more than point to problems but rather offer up proactive repairs and recommendations, identifying modifications to configurations and changes to application policies that can maximize network and cloud app performance
  • Observes real-time data 24/7 to learn from the current environment and uses predictive analytics to extrapolate what future traffic patterns will look like
  • Serves as more than a ticket generator — it should forecast bandwidth and come with a intelligent history of behavioral data front-loaded into your system, so it’s not starting at zero on day one of the implementation

Collective AIOps solutions overcome challenges

AI engines thrive on large data lakes with a variety of data feeds, not to mention agile IT infrastructures supporting all that data crunching. That’s why SD-WAN and SASE solutions serve as the perfect pair for AIOps, advises ZK Research. “When AI is fragmented across multiple tools, data has to be reconciled or even corrected,” warns Zeus Kerravala, Founder and Principal Analyst.

In evaluating AIOps tools, consider the operating platform supporting the analytics engine and what access it has to both network and security data. Don’t forget training and any ongoing management, as network and security operations teams will need leadership and monitoring teams.

Investing with confidence

ZK Research shows 97% of respondents are confident AIOps engines can be trusted. IT leaders have confidence that these advanced tools can act on their own recommendations and create fully automated systems. But where does that confidence come from, and how do IT decision makers take a page from the book of their peers?

Knowing that you can reach full autonomy

ZK Research explains that decision makers can rest easy when AIOps has proper access to IT control panels and permissions to make changes to network configurations, as this is the only way the AI engine can act on its own prescriptive recommendations. Known as closed-loop automation, this is the key in building fully autonomous systems. Closed-loop automation is the key in reaching the highest levels of automation, which require little human interaction.

The journey to full automation: Four levels of AIOps maturity

  • Level 1: “Here’s the problem I found”
  • Level 2: “Here’s the problem and my recommendation”
  • Level 3: “Here’s my recommendation — approve and I will apply the changes”
  • Level 4: “Here’s what I just fixed for you”

Don’t miss the complete buyer’s guide, which explores the latest trends in AIOps and why these intelligent engines are no longer considered experimental. You’ll also learn more about:

  • How IT leaders evaluate AIOps and overcome other challenges
  • The critical relationship between AIOps, SASE, and SD-WAN
  • A checklist of criteria that can kickstart your AI program with confidence

Download the ZK Research report: Buyer’s Guide for AIOps

Ajay Pandya

Ajay Pandya is Director of Product Management and leads the team for Network Solutions team at Masergy. He has over 20 years of telecom experience in product development, engineering, consulting, and sales and has helped global service providers, utilities, transportation companies, public sector entities, and enterprises for their networking and IT needs. He has an MS in Computer Engineering from the University of Manitoba, and an MBA from the University of Ottawa. He is based in the Dallas-Fort Worth metroplex.

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