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4 Ways of Getting the Most Out of AIOps

Andreas Grabner

As organizations strive to advance digital acceleration efforts, outpace competitors, and better service customers, the path to better, more secure software lies in AIOps.

Coined by Gartner in 2016, AIOps — or AI for IT operations — has become an IT best practice only in the past few years. In short, AIOps offers developers and their DevOps and SRE teams a fast and automated solution for delivering observability (and precise insights) into their production environments at scale — making it easier for those teams to troubleshoot problems, identify root causes and remediate issues before they can impact the end-user experience or hinder the business bottom line.

Over the course of the next year, organizations expect production deployments to grow by 10x the current deployment rates, but as those deployments skyrocket, output volumes and production processes will also grow in complexity. An AIOps solution should be able to scale to meet that volume and process accordingly, but the fact is, not all can. Scalability and operational efficiency are only as effective as the AIOps solution you're leveraging.

As DevOps teams continue to adopt progressive delivery models — like Canary, Blue/Green and Feature Flags for upgrading and replacing individual services — and the volume of production deployments and configuration changes sees even more growth, here are a few of the things that your DevOps teams should keep in mind, as they look to make the most of their IT toolkits via AIOps:

1. Create test-driven operations

Bolster your AIOps' resiliency by testing auto-remediation scripts before entering production, rather than reactively.

For example, SREs can orchestrate a pre-production environment that's monitored by the AIOps solution. By loading tests and injecting chaos into this "test-driven operations" environment, and using it to validate auto-remediation scripts, your AIOps solution's capability for deploying auto-remediation code when an issue (inevitably) arises is further validated. Instead of SREs scripting and deploying code reactively (once an issue has been experienced), AIOps can deploy it proactively — fixing the issue immediately, thanks to having been "battle tested" for those scenarios in advance.

2. Push deployment/configuration data to AIOps

Linking events to a monitored entity makes it easier for AIOps to analyze and correlate behavior — necessary for going beyond simple correlations, to provide instead, more precise root cause answers. Pushing contextual deployment information (i.e., deployment, load test, load balance, configuration changes, service restart, etc.) to AIOps makes it possible to immediately alert teams when behavior changes negatively affect users and service-level agreements (SLAs). Making it easier to raise awareness of and remediate the issue before it can impact the end user.

3. Let AIOps drive your decisions

Pushing deployment info and context to AIOps creates even more awareness around delivery activities, providing a new source of data for DevOps teams to draw from to better inform future decision making.

AIOps solutions, which can generate data within their own dashboards, can better provide teams with choices and context in comparing test run and baseline results — drawing from multiple tests and deployments to identify regressions occurring during or between tests. Pushing this information to AIOps, in turn, further accelerates the software delivery pipeline and facilitates quick remediation for the delivery process.

4. Generate automated, operational resiliency

Resiliency and adaptiveness to change are key indicators of production quality today. AIOps solutions can ensure continuous resiliency, availability, and system health by automating manual operational tasks.

What's more, integrating AIOps with delivery automation sends configuration and deployment context directly to the solution, further enabling AIOps to better pinpoint root causes of abnormal behavioral changes; alert teams if or when a load test in production starts to affect overall system health; alert app teams if new service iterations are causing high failure rates; and provide detailed root-cause analysis on impact.

Today, greater operational resiliency means fewer issues, more consistent and reliable performance, and more robust digital experiences — all wins for DevOps teams and their customers.

As today's IT environments become increasingly dynamic, containerized, multi-cloud and multi-cluster, it's more essential for DevOps teams to capitalize on the power and productivity afforded by AIOps: driving business results, customer experiences and critical business outcomes effectively and at scale. Make sure your teams are equipped with the right AIOps toolkits is the first step in optimizing your AIOps journey. From there, leveraging those toolkits effectively is the best way to ensure that you're getting the most ROI out of your AIOps.

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4 Ways of Getting the Most Out of AIOps

Andreas Grabner

As organizations strive to advance digital acceleration efforts, outpace competitors, and better service customers, the path to better, more secure software lies in AIOps.

Coined by Gartner in 2016, AIOps — or AI for IT operations — has become an IT best practice only in the past few years. In short, AIOps offers developers and their DevOps and SRE teams a fast and automated solution for delivering observability (and precise insights) into their production environments at scale — making it easier for those teams to troubleshoot problems, identify root causes and remediate issues before they can impact the end-user experience or hinder the business bottom line.

Over the course of the next year, organizations expect production deployments to grow by 10x the current deployment rates, but as those deployments skyrocket, output volumes and production processes will also grow in complexity. An AIOps solution should be able to scale to meet that volume and process accordingly, but the fact is, not all can. Scalability and operational efficiency are only as effective as the AIOps solution you're leveraging.

As DevOps teams continue to adopt progressive delivery models — like Canary, Blue/Green and Feature Flags for upgrading and replacing individual services — and the volume of production deployments and configuration changes sees even more growth, here are a few of the things that your DevOps teams should keep in mind, as they look to make the most of their IT toolkits via AIOps:

1. Create test-driven operations

Bolster your AIOps' resiliency by testing auto-remediation scripts before entering production, rather than reactively.

For example, SREs can orchestrate a pre-production environment that's monitored by the AIOps solution. By loading tests and injecting chaos into this "test-driven operations" environment, and using it to validate auto-remediation scripts, your AIOps solution's capability for deploying auto-remediation code when an issue (inevitably) arises is further validated. Instead of SREs scripting and deploying code reactively (once an issue has been experienced), AIOps can deploy it proactively — fixing the issue immediately, thanks to having been "battle tested" for those scenarios in advance.

2. Push deployment/configuration data to AIOps

Linking events to a monitored entity makes it easier for AIOps to analyze and correlate behavior — necessary for going beyond simple correlations, to provide instead, more precise root cause answers. Pushing contextual deployment information (i.e., deployment, load test, load balance, configuration changes, service restart, etc.) to AIOps makes it possible to immediately alert teams when behavior changes negatively affect users and service-level agreements (SLAs). Making it easier to raise awareness of and remediate the issue before it can impact the end user.

3. Let AIOps drive your decisions

Pushing deployment info and context to AIOps creates even more awareness around delivery activities, providing a new source of data for DevOps teams to draw from to better inform future decision making.

AIOps solutions, which can generate data within their own dashboards, can better provide teams with choices and context in comparing test run and baseline results — drawing from multiple tests and deployments to identify regressions occurring during or between tests. Pushing this information to AIOps, in turn, further accelerates the software delivery pipeline and facilitates quick remediation for the delivery process.

4. Generate automated, operational resiliency

Resiliency and adaptiveness to change are key indicators of production quality today. AIOps solutions can ensure continuous resiliency, availability, and system health by automating manual operational tasks.

What's more, integrating AIOps with delivery automation sends configuration and deployment context directly to the solution, further enabling AIOps to better pinpoint root causes of abnormal behavioral changes; alert teams if or when a load test in production starts to affect overall system health; alert app teams if new service iterations are causing high failure rates; and provide detailed root-cause analysis on impact.

Today, greater operational resiliency means fewer issues, more consistent and reliable performance, and more robust digital experiences — all wins for DevOps teams and their customers.

As today's IT environments become increasingly dynamic, containerized, multi-cloud and multi-cluster, it's more essential for DevOps teams to capitalize on the power and productivity afforded by AIOps: driving business results, customer experiences and critical business outcomes effectively and at scale. Make sure your teams are equipped with the right AIOps toolkits is the first step in optimizing your AIOps journey. From there, leveraging those toolkits effectively is the best way to ensure that you're getting the most ROI out of your AIOps.

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 ...