How AI-powered DevOps is setting new standards for efficiency
Published on: March 26, 2025
Last update: March 26, 2025
Software development should be fast, but for many teams, it still feels like wading through quicksand. Engineers spend more time managing tickets, troubleshooting deployments, and fixing security gaps than innovating. DevOps was meant to speed things up, but inefficiencies remain. Even after years of modernizing workflows, 78% of developers still spend a third of their time on repetitive tasks.
These challenges won’t be solved by replacing your entire toolchain. The key is making better use of what you already have—integrating AI into tools like Jira, GitHub, AWS, and Compass to unlock their full potential.
Let’s talk about how this can be done.
The problem with most DevOps workflows
Ask any DevOps team about their biggest pain points, and three challenges come up repeatedly:
- Fragmented workflows: Development, security, and operations teams use separate tools that don’t integrate well, leading to inefficient handoffs and misalignment.
- Manual bottlenecks: Code reviews, security checks, and deployments still require too much human intervention, causing delays.
- Lack of real-time insights: Engineering leaders rely on outdated reports instead of having an up-to-the-minute view of system performance, security risks, and team productivity.
Legacy on-prem solutions only make things worse. They’re rigid, tied to outdated processes, and struggle to keep up with the growing demands on engineering teams.
To move faster, DevOps needs more than just better tools. It needs a smarter approach. That’s where AI comes in.
3 ways AI can upgrade DevOps
AI isn’t here to take over DevOps teams, it’s here to make them faster, smarter, and more secure. Instead of replacing engineers, AI eliminates repetitive tasks, surfaces real-time insights, and strengthens security practices, allowing teams to focus on innovation rather than busywork.
Here’s how AI can supercharge DevOps:
1. Automate your repetitive DevOps tasks
For a practice designed around speed, DevOps teams still spend a surprising amount of time on repetitive tasks such as writing boilerplate code, managing deployments, and triaging tickets. AI helps by taking care of repetitive tasks so teams can focus on high-impact work.
And again, you don’t need to reinvent your stack. Several popular solutions have upgraded their capabilities with AI in recent years. Take GitHub as an example:
GitHub Copilot cuts down repetitive coding by up to 55%, generating entire functions based on minimal input. It also helps engineers detect syntax errors, suggest best practices, and improve code quality on the fly. GitHub Actions automates CI/CD workflows, eliminating the need for engineers to manually push updates or configure build environments.
Similarly, Atlassian has introduced Rovo to access organizational knowledge with generative AI. It helps DevOps teams quickly discover knowledge across Atlassian and third-party SaaS apps. This is particularly helpful for new engineers ramping up quickly or teams needing immediate answers without digging through documentation.
2. Get real-time visibility into DevOps performance
Many DevOps teams struggle with decision-making simply because they lack real-time visibility into performance, bottlenecks, and risks. AI-powered analytics change the game, turning scattered data into insights that help teams stay ahead of issues instead of reacting to them.
For example, tools like Atlassian Compass, AWS CloudWatch, and GitHub Insights help you get real-time insights across different stages of the development process. Compass consolidates engineering health metrics, giving teams a unified view of project progress, system performance, and areas needing improvement.
Similarly, AWS CloudWatch and GitHub Insights provide real-time monitoring of application infrastructure health and development velocity respectively.
AI-driven analytics help DevOps teams stop firefighting and start optimizing, making smarter decisions faster and keeping systems running smoothly.
3. Enhance security throughout the development lifecycle
Security is often treated as an afterthought in many development cycles, leading to last-minute patches and increased risk exposure. To make matters worse, hackers are increasingly using AI for their malicious actions. So, if you aren’t using AI to bolster your DevOps integrity, you will always be a step behind.
AI can help you bolster your DevOps security on three levels:
- You can use tools like GitHub Advanced Security (GHAS) to scan each commit for vulnerabilities, allowing developers to fix issues before they reach production. This ensures secure code from day one, reducing the risk of post-deployment security incidents.
- AWS Security Hub can aggregate security data from multiple sources and provide you with a centralized dashboard for identifying and mitigating threats.
- Jira Service Management, empowered with Rovo, can help security teams respond to incidents more efficiently by automating remediation workflows and enforcing compliance policies.
AI-driven security automation reduces risk and builds security into the DevOps lifecycle, ensuring compliance without slowing down innovation.
A phased approach to AI-powered DevOps helps your organization see quick efficiency gains while setting the stage for long-term success. It also keeps your team flexible and ready for future AI advancements.
How to start using AI in DevOps
You don’t need a massive overhaul of your existing DevOps process to start making improvements with AI. You simply need a phased approach to make the most out of the tools you already use.
- Find the bottlenecks: Analyze where teams spend the most time on manual tasks and determine which workflows would benefit the most from automation.
- Automate what makes sense: Use AI-powered tools like GitHub Copilot for coding, Atlassian Rovo for knowledge discovery, and GitHub Actions for CI/CD. The goal isn’t automation for its own sake but freeing your team to focus on real engineering challenges.
- Measure and refine: Use real-time analytics from tools like Atlassian Compass, GitHub Insights, and AWS CloudWatch to measure improvements and refine automation strategies.
A phased approach to AI-powered DevOps helps your organization see quick efficiency gains while setting the stage for long-term success. It also keeps your team flexible and ready for future AI advancements.
The future of DevOps is smarter, not just faster
Investing in AI-driven DevOps today builds resilient, high-performing engineering teams that can adapt to the fast-changing demands of software development.
Leaders must choose: Stick with inefficiencies or use AI automation to help teams do their best work.
Companies that integrate AI into their DevOps workflows will gain a lasting competitive advantage, improving efficiency, security, and developer experience. The ones that hesitate risk being stuck in reactive mode, constantly catching up while others move ahead.
You shouldn’t be debating whether or not AI will redefine DevOps. You should decide how soon your team will take advantage of it.
