As artificial intelligence (AI) technologies advance rapidly, more and more industries integrate AI into their everyday operations. In the case of DevOps – an approach that combines software development (or Dev) and information technology operations (or Ops) to reduce the systems development life cycle – AI is taking center stage in changing the way DevOps engineers do their jobs, both day-to-day and into the future. From automating the mundane to streamlining decision-making processes, AI is becoming an integral part of the DevOps fabric, helping engineers work faster, make fewer errors, and free up more time to be innovative. Here’s how it’s doing it and why.
Top Five AI-Driven Improvements in DevOps
The current AI revolution is changing almost everything regarding technology, and DevOps travels right at its leading edge. Today’s maturing AI tech is starting to embed itself in many aspects of software delivery and development production lines, pushing automation forward and focusing more on predictive or proactive releases and updates.
The growing dominance of the cloud computing model in IT systems underlines the importance of AI in DevOps. Gartner predicts that by 2025, more than 85 percent of organizations will have a cloud computing strategy, and up to 95 percent of new digital workloads will be hosted in a cloud. Cloud-hosted processes are increasingly valued because they are more agile, scalable, and efficient than their on-site counterparts. Moreover, AI can enhance these benefits significantly.
Here are the top five transformations caused by technological advancements. Let’s explore them!
AI-assisted code
Generative AI will have its most excellent transformative effect on operations because it can quickly generate the highest-quality Bash or PowerShell scripts, which should be welcomed for many reasons.
Firstly, much of the scripting work could be more varied. Learning how to script can be pretty exciting, at least for a while, but it gets old after writing 300 scripts to automate file transformations. Generative AI makes the work much faster – sometimes 70 percent faster. A much more significant part of the work becomes productive.
Secondly, only some DevOps engineers have development experience: many come from SysOps and NetOps backgrounds, where they are often expected to create more complex scripts but need help with them. The use of these AI tools has not just given people confidence it’s also allowed them to produce more sophisticated scripts than they could before.
Also, it allows fixing edge cases that often end up at the bottom of the backlog: for example, writing a script to move corrupted files from a Microsoft Windows server (the primary location) to a Linux system (the backup location) and incorporating a naming policy to account for file names that were acceptable in Windows but problematic in Linux. It had been a much more difficult task in the past; with generative AI, an extra line was included in the script, bypassing cumbersome code that would have dealt with that output only once an actual problem had occurred.
Navigating public cloud services and documentation
Coming in a strong second is the number-crunching ability of chatbots such as ChatGPT, which can parse copious amounts of data from the web in seconds. Generative AI has become a must-have, especially with the pace of change in the public cloud. Understanding new services and the latest updates was a time-consuming challenge.
It meant going back to the same documentation for services that needed to be called every six months and looking for minor changes often missed, potentially misleading development teams into faulty deployments to production that brought unforeseen customer issues. Now, with the help of AI, it’s possible to browse all services and related documentation with their troubleshooting guides simply at the press of a single button. It means a lot of saved time. But it’s not perfect. For instance, initial cost estimations for services need improving.
AI-assisted CI/CD
Fourth place is connected with AI-powered coding but earns its distinction for good and bad reasons. The good news is that AI has changed how developers think about their work so much that, with the help of AI, they can frame their pipelines in ways that were not previously possible, empowering a creative dynamic that dramatically increases speed and enhances quality.
However, the path hasn’t been smooth. A key issue is that AI sometimes ignores templates for pipelines and instead creates tasks based on the models and data it has been trained on, meaning that it might defer to its judgment and standards. Despite these problems, the ability to involve AI throughout CI/CD processes is opening up options that were otherwise deemed too difficult or expensive to be feasible – representing a substantial increase in developmental capacity.
Automating Tasks
The classic DevOps debate – whether it’s faster to do something by hand or script it – applies to many tasks (like renaming, splitting, and project set-ups), some of which existed in limbo between the two approaches. Code-completion tools have been around for a long time, and AI-written code redefines the extent of these, with almost any reasonably describable task now able to be automated and sped up.
Some of this script-centric workflow will also start percolating to other more back-office job functions. For example, when it comes to IT, low-code and no-code skills will become more widespread as they are used to generate this script. In many within-organization knowledge jobs, workers depend on generative AI to develop the scripts they need and verify their accuracy and effectiveness. It frees developers to work on a whole new category of high-impact ideas that keep the organization in business. It is your world in the early days of the generative AI work world. The generative AI sharply fundamentally changes how things get done. It changes how work roles are constructed or reconstructed.
Bonus: Dashboards, Monitoring, Logs
Now, let’s look ahead to a world where generative AI capabilities expand into other areas beyond simply generating code and script answers. Generative AI can create layouts and configurations for dashboards. It provides a great tool to develop dashboards adapted to a particular event. For example, if you want to monitor peak hours and resource usage on Black Friday, generative AI could configure this capability without a program or engineer and ensure that you’re covering the right services and that timings are suitable.
Another example would be when you’ve had issues with a particular service or are gearing up to upgrade or maintain it. With this tool, you can focus on that service. This way, it becomes an operational enhancement tool, but you can also focus on what matters most.
Embracing Tomorrow's Innovations Today
These top five improvements currently transform day-to-day work. As change is constant and rapid, we also expect some new features this year, especially in tools and capabilities related to FinOps, governance policies, and architectural guidance for applications in the cloud.