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5 Ways DevOps Teams take Advantage of AI

AI this, AI that – it seems that nowadays you can’t get your Starbucks order without overhearing a conversation about AI. From lazy students insta-generating their term papers to multi-billion-dollar companies creating the next generative AI tool to sweep the nation, it seems that AI has permeated every facet of our lives.

So why should DevOps be any different?

Today, we are talking about the use of artificial intelligence in DevOps, and how it’s making a big impact on the industry.

Let’s dive right in!

Will AI Replace DevOps Teams?

Before we get to the concrete examples of how DevOps teams can use AI to their advantage, let’s address the elephant in the room. Will AI replace DevOps teams?

In other words, will machines steal your job? The short answer is, you guessed it – no.

AI is far from that point of complete process automation in any field, let alone complete autonomy. Artificial intelligence in DevOps is not about replacement, it’s about empowerment.

You can use AI to support your entire process, from planning and ideation to deployment and monitoring. For example, 62% of surveyed developers say they use AI to test code, while 52% use bots in their testing process.


That’s not replacement, that’s team efficiency and empowerment.

In other organizational sectors, it’s important to note that the current state of AI at work suggests that new AI tools like ChatGPT are gateway tools that allow teams to start using these technologies. This can help bring more efficiency between DevOps and other teams, further enabling collaboration, cohesion and communication across your organization.

Now that we have addressed that concern, here’s how DevOps teams can use AI in 2023.

Examples of AI Applications in DevOps Processes

Improving the rate of automation

DevOps relies heavily on automation in order to ensure efficiency throughout the process, cut extraneous costs, and maintain peace between the team and operations. What’s important to note is that the rising use of AI allows DevOps automation tools to take center stage and boost efficiency.

Automation in DevOps permeates every part of the project development cycle to some extent. For example, when the team wants to build a web app that produces a consistent user experience across platforms, AI can help with everything from ideation to testing and rollout.

DevOps teams can automate, at least in part, things like:

  • Conceptual art and UI design
  • Simultaneous testing on different platforms
  • Reporting and data communication between teams
  • Accountability measuring and workflow
  • Real-time trend and market monitoring which allows frictionless pivoting

Aside from general automation, there are some other important use cases to examine.

Continuous monitoring and alerting

There are some problems that AI-based systems can solve with little to no oversight, and then there are those that demand human attention. The beautiful thing about AI-based automation is that through continuous monitoring, AI systems can alert human developers of the problems that truly require their attention.

DevOps teams can use AI automation and smart tools in general for real-time ping monitoring as well as status updates, incident monitoring, and much more. An AI tool can prioritize issues based on their severity as well as the team that’s best equipped to handle it.

Repetitive tasks and issues that come up on the daily can be easily fixed with minimal oversight, however. This kind of prioritization feature allows two things.

First, it allows the DevOps team to minimize time waste and resolve issues successively, in order of relevance. And secondly, it minimizes financial waste while keeping the project on track and allowing the team to focus on development.

Leveraging AI for continuous testing

Probably one of the biggest use cases for AI in software development nowadays is for the purpose of continuous testing. This process is something that the DevOps principle relies on heavily in order to ensure that the project keeps moving forward while code is being tested at the same time.

This strategy results in fewer setbacks and sets the stage for micro improvements every day. For those software development companies that focus on educating their DevOps teams to write better code while making incremental improvements every day, AI-driven testing is the way forward.

Code testing done via artificial intelligence is also one of the best ways to deliver a relatively bug-free experience on launch day.

Building cost-effective projects

Every DevOps operations and project manager knows that a successful project rests on the ability to stay within budget. After all, if you run out of money halfway through, you’ll inevitably run into development delays and other issues.


Conducting a thorough cost and needs analysis is not easy, but AI can help. This analysis is done mostly through the AI‘s ability to collate vast amounts of market and consumer data, which can help project managers gauge the scope and cost of a new development project.

Aside from project costs, you also need to be wary of software costs. If you are using DevOps as a service, for example, you need to tend to cloud cost management to minimize how much you pay for DevOps tools and services that you may not need in the first place.

AI tools can help with cost management across the board and also ensure continuous cost monitoring and even suggest resource allocation.

Post-launch optimization

Lastly, it’s important to note that post-launch optimization, bug-fixing, and improvements can take a lot of time and resources. This is especially true when the team is set to start a new project shortly after release, when the resources you’re working with are stretched thin.

If you want to improve your DevOps strategy across the board but especially for the post-launch stage, then using AI-driven tools is a safe bet. An AI-based model can collect and analyze user data automatically and then prioritize tasks for your team.

This process lets you provide continuous support for the product without wasting time on repetitive issues that you can automate.

Over to You

AI is not going to take people’s jobs in the DevOps sector, it’s going to enhance their work. What’s more, you might finally be able to achieve that work-life balance you’ve been striving for with the use of AI tools and AI-driven processes.

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