If you’re a Jira admin or project manager, your day is probably a juggling act. Between breaking down massive projects, writing the perfect query, and triaging an endless stream of support tickets, the "work about work" can easily swallow up the time you’d rather spend on actual strategy.
Atlassian has started adding its own AI features directly into Jira, which is a good start. But for many teams, they only scratch the surface of what’s possible. This guide will walk you through, step-by-step, how to use AI in Jira in a way that makes a real difference. We’ll start with the native tools that can help with project management and then get into how you can set up a smart, integrated AI agent to handle frontline support and actually get some time back in your day.
How to use AI in Jira: What you’ll need to get started
Before we jump in, just make sure you have these things ready:
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An active Atlassian Jira Cloud account.
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Admin or project admin permissions so you can set up automations and integrations.
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A rough idea of which repetitive tasks or support questions are driving you nuts.
A step-by-step guide to using AI in Jira
Here’s a practical, four-step approach to bringing AI into your Jira setup, starting with the simple built-in features and moving up to more powerful automation.
Step 1: Automate project management with native Jira AI
Atlassian has been integrating its own AI, called Rovo (or Atlassian Intelligence), to help with some of the more tedious parts of project management. These tools are a great place to start if you’re just dipping your toes into AI.
Generate JQL queries with plain English
Jira Query Language (JQL) is incredibly useful for creating custom filters, but let’s be honest, the learning curve is steep. Instead of trying to memorize the syntax, you can now just type what you want in plain English, like "unresolved issues assigned to me due this week." The AI translates that into a perfect JQL query. It saves a ton of time and makes advanced search available to everyone on the team, not just the JQL wizards.
Break down epics into smaller tasks
We’ve all been there: staring at a huge epic, feeling overwhelmed by the thought of manually creating every single user story and sub-task. With Jira’s AI, you can ask it to scan an epic’s description and suggest a logical breakdown of child issues. You can look over the suggestions, make a few tweaks, and then create them all with one click. It’s a massive time-saver during project planning.
Summarize long comment threads
Opening a ticket and being greeted by a novel’s worth of comments is a classic productivity killer. Instead of scrolling forever to find that one key decision made three weeks ago, you can use the AI summary feature. It reads the whole conversation and pulls out the key decisions and action items, so you can get the gist in seconds.
Create automation rules with a simple prompt
Jira’s built-in automation builder is useful, but putting together complex rules can feel like a chore. The AI helps simplify this. You can give it a prompt like, "When a high-priority bug is reported, assign it to the development team lead," and the AI will build the automation rule for you.
These native features are great for tidying up tasks that happen inside Jira. The main drawback is that their knowledge is stuck in the Atlassian ecosystem, which is a problem when your team’s real work and knowledge are scattered everywhere else.
Step 2: Beef up your knowledge base for self-service
One of the most common ways to use AI is to deflect repetitive questions by pointing people to the right documentation. Jira and Confluence are set up to do this, but it’s really just the beginning.
Use AI to draft and refine help articles
When you finally solve a tricky support ticket, that solution is pure gold. Instead of letting it get lost in the archives, agents can use AI right inside a Jira ticket or a Confluence page to brainstorm a new help article. Features like "improve writing" or "change tone" help you quickly turn a one-off solution into a professional, consistent article for your knowledge base.
Connect your knowledge base to Jira Service Management
Once you have a solid set of articles in Confluence, you can link that space to your Jira Service Management project. This allows Jira’s virtual agent to suggest relevant articles to users as they type in the help portal, hopefully answering their question before they even submit a ticket.
But there’s a pretty big limitation here: this only works if all your knowledge lives neatly in Confluence. What happens when the answer is in a Google Doc, a Notion page, a random Slack thread, or buried in the details of a dozen past tickets? This is where the built-in AI hits a wall and you start needing something more powerful.
Step 3: Set up a true AI agent for frontline support
Once you’ve squeezed all the value you can from the native features, the next logical step is to bring in an AI agent that can connect all your knowledge sources and actually take action.
Why native virtual agents often fall short
The simple virtual agents are good at matching keywords to a single, well-organized knowledge base. But they often can’t solve real problems because they are stuck in their own world. They typically can’t access information outside of Confluence, learn from the context of thousands of past tickets, or do things like tag a ticket, look up order details, or escalate an issue to the right person.
How to connect a powerful AI agent to Jira Service Management
This is where you need an AI that can break out of the Atlassian bubble. That’s what tools like eesel AI are built for. It’s designed to plug into the tools you already use, so you don’t have to deal with a complicated migration or rip out your existing helpdesk.
Getting it up and running is surprisingly simple:
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Sign up and connect Jira: You can get started in a few minutes with a one-click integration, not months of development work.
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Unify your knowledge sources: This is the most important part. You can connect not just Confluence, but also Google Docs, Notion, SharePoint, and, crucially, all of your historical support tickets. This gives your AI a complete picture of your business.
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Customize your AI’s persona and actions: With a simple editor, you can define your AI’s tone of voice and give it the power to do more than just find answers. You can configure your AI Agent to triage tickets by adding the right tags or even make API calls to other systems to check a customer’s subscription status.
A flowchart showing the simple process for implementing an AI agent, which is a key part of how to use AI in Jira effectively.Step 4: Test and roll out your AI with confidence
Letting an AI talk directly to your customers can feel a little scary. The key is to test it, make adjustments, and roll it out in a controlled way.
Use simulation to see how it will perform
One of the biggest anxieties with AI is not knowing how it will behave in the wild. That’s why a tool like eesel AI includes a simulation mode. You can run your newly configured AI agent on thousands of your past tickets in a safe environment that doesn’t affect anything.
This shows you exactly how it would have responded to real customer questions, giving you an accurate forecast of its resolution rate and pointing out any gaps in your knowledge base. You can tweak its behavior and prompts with zero risk before it ever interacts with a real customer.
This screenshot shows the simulation mode in eesel AI, a critical step for how to use AI in Jira for customer support automation.Roll out gradually and keep an eye on things
You don’t have to flip a switch and automate everything at once. Start small. You could configure your AI agent to only handle a few specific, high-volume ticket types (like "password reset" or "how to update billing info") and automatically send everything else to your human team. As you get more comfortable and see the results, you can gradually let it handle more.
Tips for getting the most out of AI in Jira
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Start with the biggest time-wasters. Before you build anything, talk to your team. Figure out the top 3-5 most repetitive, soul-crushing questions or tasks they deal with every day. Automating those first will give you the biggest win right away.
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Focus on support, not just configuration. As many experienced Jira admins will tell you, today’s AI is much better at handling conversational support tasks than it is at configuring complex, multi-step Jira workflows from a text prompt. Use AI where it shines.
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Keep improving your knowledge base. Your AI is only as smart as the information you give it. Use the insights from your tools, like the knowledge gap reports in eesel AI, to see what your users are actually asking about and keep your documentation fresh.
An image of eesel AI's reporting dashboard, which helps teams understand how to use AI in Jira to identify and fill knowledge gaps.Moving beyond basic automation
As you can see, using AI in Jira is a journey. You can start small today with the native features to streamline your project management. But to really free up your team and provide instant, helpful support, you need an AI that can learn from all your scattered knowledge and work seamlessly with your helpdesk.
Ready to see how a fully integrated AI agent could work with your Jira Service Management? Get started with eesel AI for free.
Frequently asked questions
Learning how to use AI in Jira can significantly reduce time spent on repetitive tasks like generating JQL queries, breaking down epics, and summarizing comment threads. For support teams, it enables faster ticket resolution and improved self-service, freeing up human agents for more complex issues.
Atlassian’s native AI features primarily enhance internal project management tasks within Jira and Confluence, like drafting content or building automation rules. A dedicated AI agent can connect to external knowledge sources (like Google Docs or Slack) and perform actions like triaging tickets or making API calls, extending AI capabilities beyond the Atlassian ecosystem.
A robust knowledge base is crucial because AI agents rely on accurate and comprehensive information to provide relevant answers. By using AI to draft and refine help articles and connecting your knowledge base to Jira Service Management, you empower the AI to deflect common inquiries and provide immediate self-service solutions.
Integrating an external AI agent typically involves connecting it directly to your Jira Cloud account, then unifying all your scattered knowledge sources (Confluence, Google Docs, past tickets, etc.). You can then customize the AI’s persona and define specific actions it can take, often without complex coding or lengthy development cycles.
Before full deployment, utilize simulation modes offered by tools like eesel AI to test your agent against thousands of past tickets in a risk-free environment. This allows you to forecast its performance, identify knowledge gaps, and refine its behavior. Gradually roll out the AI, perhaps starting with high-volume, low-complexity ticket types, while continuously monitoring its effectiveness.
Native Atlassian Intelligence often falls short when knowledge resides outside of Confluence or Jira. It typically cannot access information from other platforms like Google Docs or Notion, learn from the context of historical tickets across various systems, or perform actions like calling external APIs to fetch customer details or update records.
