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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

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...