AI Can Automate Your Network: Buyer’s Guide from ZK Research

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 information and digital services accessible anywhere at any time. But that has a negative impact on the ease of which the IT environment can be managed and controlled. According to research from Nemertes, more than 75% of WAN traffic starts and stops outside the enterprise infrastructure.
• 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 brushes. Complex environments make the human brain no longer the right tool to evaluate a web of distributed users and network traffic patterns in real-time all around the clock.
• 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.”

When innovation can’t come at the expense of operational excellence, artificial intelligence for IT operations (AIOps) is the emerging technology helping companies automate processes and manage increasing complexity. 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 what IT leaders find most important in their AIOps solutions. Plus, it explores the lessons learned from early adopters.

Here are the top three tips for IT decision makers.

AI Doesn’t Equate to Automation: Understand What’s Powering Your AI Engine

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

Companies looking to leverage AIOps for its automation power will want to explore what analytics are available, how they can be used to solve your business use case, and whether the toolset can actually improve autonomy. That requires some investigation. ZK Research says one way to tell is to look at the analytics inside the engine. The buyer’s guide recommends taking a deep dive into how the AIOps toolset:

• Uses machine learning, behavioral and predictive analytics to do more than point to problems but rather identify modifications to configurations and changes to application policies that can maximize 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 Solutions Ensure Challenges can be Overcome

AI engines thrive on large data lakes with a variety of data feeds, not to mention agile IT infrastructures. 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 at ZK Research.

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

Invest with Confidence: Knowing You Can Reach Full Autonomy

ZK Research shows 97% of respondents are confident AIOps engines can be trusted. IT leaders have confidence that solutions 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?

ZK Research explains that AIOps must have proper access to the control panel and permissions to make changes to network configurations, as this is the only way the 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 (if any) human interaction:

• 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 intelligence engines are no longer considered experimental. You’ll also learn more about:

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