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

Vinay Chandrasekhar

Share this

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
Share this

The Latest

January 26, 2023

As enterprises work to implement or improve their observability practices, tool sprawl is a very real phenomenon ... Tool sprawl can and does happen all across the organization. In this post, though, we'll focus specifically on how and why observability efforts often result in tool sprawl, some of the possible negative consequences of that sprawl, and we'll offer some advice on how to reduce or even avoid sprawl ...

January 25, 2023

As companies generate more data across their network footprints, they need network observability tools to help find meaning in that data for better decision-making and problem solving. It seems many companies believe that adding more tools leads to better and faster insights ... And yet, observability tools aren't meeting many companies' needs. In fact, adding more tools introduces new challenges ...

January 24, 2023

Driven by the need to create scalable, faster, and more agile systems, businesses are adopting cloud native approaches. But cloud native environments also come with an explosion of data and complexity that makes it harder for businesses to detect and remediate issues before everything comes to a screeching halt. Observability, if done right, can make it easier to mitigate these challenges and remediate incidents before they become major customer-impacting problems ...

January 23, 2023

The spiraling cost of energy is forcing public cloud providers to raise their prices significantly. A recent report by Canalys predicted that public cloud prices will jump by around 20% in the US and more than 30% in Europe in 2023. These steep price increases will test the conventional wisdom that moving to the cloud is a cheap computing alternative ...

January 19, 2023

Despite strong interest over the past decade, the actual investment in DX has been recent. While 100% of enterprises are now engaged with DX in some way, most (77%) have begun their DX journey within the past two years. And most are early stage, with a fourth (24%) at the discussion stage and half (49%) currently transforming. Only 27% say they have finished their DX efforts ...

January 18, 2023

While most thought that distraction and motivation would be the main contributors to low productivity in a work-from-home environment, many organizations discovered that it was gaps in their IT systems that created some of the most significant challenges ...

January 17, 2023
The US aviation sector was struggling to return to normal following a nationwide ground stop imposed by Federal Aviation Administration (FAA) early Wednesday over a computer issue ...
January 13, 2023

APMdigest and leading IT research firm Enterprise Management Associates (EMA) are teaming up on the EMA-APMdigest Podcast, a new podcast focused on the latest technologies impacting IT Operations. In Episode 1, Dan Twing, President and COO of EMA, discusses Observability and Automation with Will Schoeppner, Research Director covering Application Performance Management and Business Intelligence at EMA ...

January 12, 2023

APMdigest is following up our list of 2023 Application Performance Management Predictions with predictions from industry experts about how the cloud will evolve in 2023 ...

January 11, 2023

As demand for digital services increases and distributed systems become more complex, organizations must collect and process a growing amount of observability data (logs, metrics, and traces). Site reliability engineers (SREs), developers, and security engineers use observability data to learn how their applications and environments are performing so they can successfully respond to issues and mitigate risk ...