Top 10 Ways To Use AI for DevOps

DevOps has transformed the way software can be built and delivered. It helps release software faster, develops collaboration between teams, and helps run operations more smoothly. But as systems grow more complex and with the rising demand for faster software delivery, DevOps teams are facing challenges with handling complex infrastructure, making sure deployments work as expected, keeping systems stable, and fixing issues quickly.

To solve these challenges, many companies are adopting AI in their DevOps lifecycle. In fact, a research by Gartner even shows that by 2027, almost 60 percent of DevOps teams will use AI in their workflows to work better, automate repetitive tasks, and improve system performance.

This article covers ten practical ways to leverage AI for DevOps work at different stages. Each point shows how AI can make work more reliable, automated, and effective.

What are the 10 Ways to Use AI for DevOps?

Here’s a detailed breakdown of the ten key ways you can use AI for DevOps and solve growing issues.

1. Automate Routine Tasks

AI can easily automate repetitive tasks like code deployment, testing, and monitoring. It uses data from past activities to improve at each task over time. This allows DevOps teams to focus on activities that require human involvement while AI manages the rest.

Benefits:

• Consistent Results: AI performs each task the same way every time, reducing human error.
• Faster Workflows: Automation removes delays caused by manual steps.
• Scalability: AI can support growing workloads without extra resources.

Tool to Use:

Jenkins X: Integrates machine learning to automate CI/CD for Kubernetes apps and streamline software delivery.

2. Active Issue Resolution with Analytics

AI tools can study system data and predict any potential issues before they cause any impact. Teams can fix these issues early, keeping the systems healthy and reducing outages.

Benefits:

• Less Downtime: AI alerts teams before users notice any issue.
• Better Performance: AI points out areas where systems slow down.
• Smarter Resource Use: Teams can assign resources based on actual needs.

Tool to Use:

Dynatrace: Offers AI-driven monitoring to help teams catch and solve problems early.

3. Incident Response Management

With AI, DevOps teams can detect any unusual activity, classify issues based on their importance, and automatically start generating responses. This helps reduce resolution times and keeps services running smoothly.

Benefits:

• Faster Fixes: AI reacts instantly and follows set workflows.
• Fewer Errors: AI applies accurate labels and actions.
• Continuous Improvement: AI gets smarter by learning from past incidents.

Tool to Use:

PagerDuty: Uses machine learning to route incidents and automate response actions.

4. Help Developers Write Better Code

With some AI tools, developers can write better code fast. These tools suggest code based on the context, detect possible bugs, and complete functions as developers type.

Benefits:

• Higher Productivity: Developers complete their code faster, which lets them code more in the same time.
• Fewer Bugs: AI finds errors early in the process.
• Unified Codebase: Teams follow common patterns across projects.

Tool to Use:

GitHub Copilot: Suggests code inside the editor based on your intent and past code.

5. Improve Testing and Quality

AI improves the software testing process by automatically generating test cases, spotting untested code areas, and identifying areas where issues may arise. AI-based testing tools analyze data from previous tests, learn patterns, and adjust their strategies to catch more issues over time.

Benefits:

• Wider Test Coverage: AI finds complex scenarios that may get missed with manual testing.
• Faster Test Cycles: Repetitive tests run automatically.
• Higher Quality: The system learns from each test, improving its ability to find bugs in future cycles.

Tool to Use:

Testim: Uses AI to build, run, and maintain automated tests with better stability.

6. Optimize Resource Usage

AI tracks resource usage and recommends better configurations. It adjusts usage based on actual demand, reducing waste and cost.

Benefits:

• Lower Costs: AI helps avoid overprovisioning resources.
• Smarter Scaling: It allocates resources where they’re most needed.
• Better Planning: Helps teams make better decisions with accurate usage data.

Tool to Use:

Turbonomic: Uses AI to manage app performance and resource usage across environments.

8. Speed Up Feedback Loops

With AI, DevOps teams can quickly analyse the code changes and their impact. Based on this, AI gives instant feedback, which lets teams know what works and what needs to be fixed.

Benefits:

Faster Learning: Teams act on insights immediately.
Improved Dev Cycles: Shorter feedback loops help release updates faster.
More Reliable Releases: Teams fix issues before they grow bigger.

Tool to Use:

**Harness: **Uses AI to analyze deployments and shorten feedback time.

9. Monitor User Experience

AI can review logs, user sessions, and app behavior to understand how users interact with your software. This helps DevOps teams understand what they need to improve for a better user experience.

Benefits:

• Better Products: AI-generated Insights help teams fine-tune features.
• Fewer Complaints: Early detection helps fix issues before users notice.
• Data-Driven Decisions: Teams use real-time user data to guide improvements.

Tool to Use:

New Relic: Tracks user experience and performance with AI-driven analytics.

10. Support Continuous Improvement

With AI tools for DevOps, teams can analyze past results, track progress, and adjust practices. It helps teams learn from their mistakes and keeps DevOps pipelines improving over time.

Benefits:

• Informed Decisions: Teams learn what works and what doesn’t.
• Smarter Strategies: Continuous learning leads to better planning.
• Cultural Growth: Teams embrace change and innovation.

Tool to Use:

Plutora: Provides insights across delivery pipelines to support ongoing improvement.

Conclusion

Using AI for DevOps can help automate tasks, reduce errors, and improve how teams manage software delivery. It supports faster releases, better testing, and stronger system reliability.

To apply these benefits effectively and to make the best out of this combination, you should work with a DevOps consulting company. Their team of experts will assess your setup, recommend the right tools, and help you adopt AI in a way that fits your goals.