Driving Business Value with Production-Ready AIOps - Part 2
September 19, 2022

Vinay Chandrasekhar

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Welcome to part two of a three-part blog series that explores how AIOps has become an increasingly important consideration for operations teams. In this second part of the blog series, we look at how adopting AIOps capabilities can drive business value for an organization.

If you missed part one, you can find it here.

Let's dive in.

How Does AIOps Drive Business Value for an Organization?

As with many IT and software development initiatives, AIOps benefits organizations and teams in multiple ways. While AIOps can significantly reduce the mundane and repetitive work required by IT operations (ITOps), site reliability engineering (SRE), and DevOps teams, the benefits also extend to other parts of the business:.

■ Reducing the mean-time-to-detection (MTTD) and mean-time-to-resolution (MTTR) ultimately means less service downtime, improved SLAs, and an enhanced customer experience.

■ Helping organizations deal with rapidly growing data volumes intelligently, reducing total cost-of-ownership (TCO), and alleviating scale challenges.

■ Reducing alert noise and implementing better automation can help free operations teams to take on higher-value initiatives.

■ Improving an organization's ability to handle ever-increasing IT complexity and the overall pace of change, AIOps allows businesses to bring value to customers more quickly and frequently.

Given the volume, complexity, and pace of change in today's cloud-native and hybrid application environments, AIOps is increasingly moving from a nice-to-have capability to a mission-critical competency for IT operations teams.

How Do You Build Trust in AIOps and Make Sure It Is Production-Ready?

IT personnel, SREs, and DevOps engineers have a couple of adoption hurdles they must cross to successfully utilize AIOps for their observability use cases.

On the one hand, there are significant buzzword challenges. The market for AIOps has a lot of buzzwords. Users can face questions such as what is the business value beyond those buzzwords? And whether AIOps will help them detect and remediate problems better and more efficiently than their current monitoring or observability setup. Beyond the buzzwords and hype, users may not always know if they will benefit from AI/ML for a specific use case.

And then there are trust hurdles. One hurdle is users' inability to tell whether the AIOps-based insights are accurate. Users might not even be aware of how comprehensive the analysis is, the information used, how the algorithms work, how conclusions are arrived at, or if those conclusions are relevant to their current investigation, resulting in a general distrust of black box AIOps systems. In some cases, organizational pressures or policies motivated by a lack of trust may also present barriers to AIOps adoption.

Our experience has shown that the best way for AIOps to provide its value is through its slow and steady adoption. First, identify specific, time-tested, and proven use cases to start adopting AIOps as a proof of concept (POC). Next, enable AIOps functionality on a smaller subset of your deployment while validating and socializing benefits and outcomes at each stage. Once you've seen some success, incrementally enable more AIOps functionality with a move towards production environments. This deliberate deployment path alleviates some of the traditional challenges associated with deploying new technology that can otherwise deter widespread AIOps adoption.

Testing and proving technology effectiveness in a smaller lab or non-production environment and measuring and showcasing results to management can help increase confidence and get buy-in before deploying AIOps in a real-world production environment. Such testing might unearth other gaps and requirements, such as missing or inconsistent data, shallow coverage, or insufficient storage or compute. As you deploy AIOps in production, check to see if your Observability solution can scale its features appropriately and handle your enterprise workloads. Certain AIOps features that work well in lab or POC environments may struggle to keep up with larger-scale requirements typically encountered in production environments.

In Part 3 of our AIOps beginners guide series, we'll talk about AI/ML capabilities beyond traditional AIOps that can further benefit Observability. And we'll take a peek into the future of AIOps. Until next time, keep observing!

Vinay Chandrasekhar is Sr. Principal Product Manager, Observability, at Elastic
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