AIOps is certainly not a wholesale replacement for traditional DevOps tools and processes, but it is an enabler of major boosts in efficiency for DevOps teams.
AIOps, the practice of enhancing IT and DevOps with help from AI, is not an especially new idea. It has been nearly a decade since Gartner coined the term in 2016.
Yet, the growing sophistication of AI technology is making AIOps much more powerful. Gone are the days when AIOps was mostly a way to speed up monitoring. AIOps, in conjunction with modern generative AI technology, allows DevOps teams to do much more.
To prove the point, here’s a look at how AIOps is reshaping DevOps, along with guidance on how DevOps teams can evolve their tools and strategies to take full advantage of AI.
What is AIOps for DevOps?
AIOps is a generic term that refers to the use of AI to accelerate or add efficiency to DevOps processes.
More specifically, AIOps focuses on leveraging AI to improve DevOps workflows, such as application deployment and troubleshooting. It does this by correlating large volumes of data to identify problems and suggest remediations. It can also automate responses to errors by providing preliminary investigations and suggested remediations.
Going further, modern AIOps tools can leverage generative AI capabilities to support tasks like summarizing lengthy alerts or consolidating multiple related alerts into a single notification, helping to reduce the time it takes engineers to parse alert data.
How AIOps enhances DevOps
It’s certainly possible to practice DevOps without help from AI – and indeed, the tools and processes at the heart of DevOps, like CI/CD and Infrastructure-as-Code, remain the same whether or not DevOps practitioners choose to embrace AI.
However, by integrating AI-powered capabilities into software development, deployment, and management workflows, it can help DevOps teams do what they’ve always done with much greater speed, accuracy, and efficiency.
For example, consider how a DevOps team responds to an outage. Without AIOps, the team would receive an alert saying a service is no longer responding. The engineers would then open monitoring dashboards, parse log files, and possibly run some traces in a bid to find the root cause of the problem. After figuring out what’s wrong, they manually apply a fix. While all of this is happening, the service remains down, and users are frustrated.
With help from AIOps, however, remediating an outage can be a much faster process. AIOps-capable monitoring tools could send not just an alert about the outage but also a report that identifies the likely root cause and suggests remediation, giving engineers the insights they need to fix the issue in minutes instead of hours. In some cases, AI tools could potentially even resolve the problem automatically.
The result is less downtime and happier users – not to mention happier DevOps practitioners, who are able to avoid the tedium of having to investigate the outage manually and can focus their time on other, more rewarding tasks.
See also: AIOps: What It is, When to Use it, and How to Get Started
How to add AIOps to DevOps
Because AIOps builds upon rather than replaces foundational DevOps resources, getting started doesn’t mean upending the way your organization currently “does” DevOps. But it does require some smaller changes in the following areas:
- Tools: DevOps teams that want to practice AIOps need software development, deployment, and monitoring tools that integrate AI features. For example, they might use generative AI to write scripts that enable automated testing during the development process. Additionally, DevOps teams can employ AI extensions for monitoring products, such as Dynatrace and DataDog, to consolidate error reports and perform root cause analysis more efficiently.
- Roles: Operational roles and responsibilities change somewhat when a DevOps team embraces AIOps due to the need to implement and support AI-powered capabilities. For instance, IT operations teams must deploy and validate AI tools, and Site Reliability Engineers (SREs) must write automation policies that incorporate AI.
- Processes: Although DevOps processes don’t fundamentally change in response to AIOps, they must evolve in some ways. For example, rather than following manual runbooks when responding to incidents, teams can rely on automated response processes that use AI to remediate issues.
- Data resources: Because AI is fueled by data, AIOps works best when organizations can expose the broadest and richest sets of data possible to AI tools. To this end, it’s important to unify logs, metrics, and events that are accessible to AIOps tools.
It’s also important to start small when it comes to implementing AIOps. Begin with minor AI capabilities, such as using AI to correlate alerts, before moving on to higher-stakes features like automated remediation. And be sure to deploy AIOps for apps running in dev/test environments before entrusting it to support production workloads.
See also: A Guide to Generative AI for DevOps Teams
Conclusion
As AI capabilities grow increasingly powerful, they have positioned AIOps as a natural evolution of DevOps. AIOps is certainly not a wholesale replacement for traditional DevOps tools and processes, but it is an enabler of major boosts in efficiency – not to mention major reductions in busywork – for DevOps teams.
This means that the question facing organizations is no longer whether they should take advantage of the technology to enhance DevOps, but how they can get started integrating AI into their DevOps workflows. The best approach, as I’ve outlined above, is to start with small, targeted improvements. Shifting fully toward an AIOps-based workflow overnight is not realistic; instead, DevOps teams should plan to progress slowly but surely toward AI-enhanced operations.
