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Driving Business Value with Production-Ready AIOps - Part 2

Vinay Chandrasekhar
Elastic

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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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

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Driving Business Value with Production-Ready AIOps - Part 2

Vinay Chandrasekhar
Elastic

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

Hot Topics

The Latest

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...