4 Ways of Getting the Most Out of AIOps

Andreas Grabner
Learn more about Dynatrace

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
Learn more about Dynatrace

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.

Related Links

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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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