A practical guide to Atlassian AI project management (2025)

It feels like every app on my phone is getting an AI-powered facelift these days, and the world of project management is no different. Platforms we use every day are getting smarter, and Atlassian is right at the front of the pack, building artificial intelligence directly into its workhorse tools, Jira and Confluence. The goal is to help teams like yours cut through the noise, automate the boring stuff, and actually collaborate without booking yet another meeting.

If you’re a project manager, a developer, or anyone whose life revolves around the Atlassian ecosystem, you’re probably curious (and maybe a little skeptical) about how these new AI features will really change your day-to-day grind. Are they actually useful, or just shiny new buttons to ignore?

Let's dig into what Atlassian AI project management really means. We'll break down the key features, look at how the AI pulls in information from your existing documents, and talk honestly about the limitations and costs you need to be aware of before you jump in.

What is Atlassian AI project management?

First off, "Atlassian Intelligence" isn't a single new product. It's the umbrella term for all the AI-powered features sprinkled across their tools. Think of it less like a standalone app and more like a helpful assistant that now lives inside Jira, Confluence, and the rest of the family. This is all powered by their AI engine, called Rovo, which is designed to help your team find, understand, and act on information, no matter where it's buried.

Instead of making you learn a whole new system, these AI features are woven directly into the interfaces you already know.

• In Jira: The AI helps you do things like draft new tasks just by describing what you need. It can also read through those impossibly long comment threads on a ticket and give you the short version so you can catch up in seconds. It can even help you build complex JQL queries using plain English, which is a lifesaver if you’re not a JQL wizard.

• In Confluence: Here, the AI acts like a writing partner. It can summarize a 10-page project brief, generate a first draft of a new document from a few bullet points, and scan a page to pull out key action items. You can also ask it questions and get answers based on the content within that specific Confluence space.

At the end of the day, what Atlassian AI project management is trying to do is simple: reduce the amount of manual busywork that drains your team's energy. It’s about speeding up decisions and keeping everyone aligned without having to constantly check in with each other.

Key features of Atlassian AI project management

Atlassian’s AI features are designed to pop up and help you at pretty much every stage of a project. Let’s look at what it can actually do, from the first brainstorm to the final report.

AI-powered planning and task creation

We all know that the initial project setup can be a slog. You’ve got a huge idea, and now you have to break it down into a million tiny pieces. This is where the AI first steps in to help.

• Breaking down work faster: Let’s say you create a big parent task in Jira like, "Launch new marketing website." Instead of you manually creating 20 sub-tasks for design, copy, development, and QA, the AI can read your description and suggest a logical list of sub-tasks. It’s not always perfect, but it can save a ton of time during that initial brain dump.

• Generating content from scratch: Over in Confluence, you can use AI prompts to draft entire project plans, creative briefs, or technical specs. Imagine taking your messy whiteboard notes from a brainstorming session, snapping a picture, and having the AI turn it into a neatly structured Confluence page with clear action items and owners. That’s the idea.

• Finding related info automatically: The Rovo AI engine works in the background to connect the dots. When you’re looking at a Jira ticket, it can surface relevant documents from Confluence or data from other tools, giving you the full context without you having to go on a digital scavenger hunt.

Smarter tracking and automation

Once a project is underway, the real challenge is keeping it moving. Atlassian’s AI introduces some clever ways to automate workflows and stay on top of updates.

• Automation for everyone: In the past, setting up automation rules in Jira often felt like you needed a computer science degree. Now, admins can just describe what they want in plain English, like "When a ticket's status changes to 'Done', post an update in the project's Slack channel." This makes some of Jira’s most powerful features way more accessible.

• Summaries that actually save time: This might be one of the most practical features. We've all opened a Jira ticket with 50+ comments and felt our soul leave our body. Atlassian Intelligence can summarize those threads for you, as well as entire Confluence pages. It’s perfect for getting the gist of a long discussion without reading every single reply.

Better collaboration and access to knowledge

A huge chunk of project management is just making sure everyone has the information they need, when they need it. The AI aims to make this a whole lot easier.

• Search like a human: You no longer need to remember exact keywords or master JQL syntax to find what you’re looking for. You can search across Jira and Confluence using normal language, like, "Show me all the high-priority bugs in the mobile app project that were updated last week."

• An AI agent for your support desk: For teams using Jira Service Management, there's an AI-powered virtual agent. It can answer common customer or employee questions by automatically finding answers in your connected knowledge bases, freeing up your human agents to handle the trickier stuff.

• Connecting projects to your chat tools: Let’s be real, a lot of important decisions happen in chat. Atlassian’s AI helps bridge that gap. It can summarize an ongoing incident directly in a Slack channel or help you turn a conversation in Microsoft Teams into a structured Jira ticket so that great idea doesn't get lost in the scroll.

Integrating Atlassian AI project management with your knowledge sources

An AI assistant is pretty useless if it can't see all your stuff, right? For an AI to give you genuinely helpful answers about a project, it needs to understand your team’s processes, past decisions, and overall history. Here’s how Atlassian approaches this.

Confluence: The brain of Atlassian AI

• The native connection: Not surprisingly, Confluence is the central nervous system for Atlassian Intelligence. The AI is built to search, summarize, and create content based on everything your team has stored in your Confluence pages and spaces. This integration is deep and works well right out of the box.

• Reaching into other tools (slowly): Atlassian knows your work doesn't only live in its products. They've started adding connectors for some external knowledge sources, allowing the AI to pull information from places like SharePoint and Google Drive. This is a good first step, but it’s still pretty limited.

The reality of where your work actually lives

• Knowledge is everywhere: Let's be honest for a second. Your company’s real "knowledge base" isn't a single, tidy library. It's a chaotic mess spread across dozens of tools. You have product specs in Notion, customer feedback in Zendesk tickets, design files in Figma, and critical decisions buried in old Slack threads.

• Atlassian’s blind spots: While Atlassian is trying to expand, its list of third-party connectors is still small and often requires an admin to do a bunch of technical setup. This creates huge blind spots for the AI. It might give you an answer based only on what it sees in Confluence, completely missing the more up-to-date context from a Google Doc or a recent conversation.

• How eesel AI tackles this: This exact problem is why eesel AI exists. It's a platform built specifically to unify all of that scattered knowledge. It connects to over 100 sources, including the big ones like Confluence, Notion, and Google Docs, but also historical data from helpdesks and chat tools. It creates a single, comprehensive brain for your AI assistant so it always has the full story.

Limitations and pricing for Atlassian AI project management

Okay, the features sound promising, but it’s not all perfect. There are a few important catches you should know about before going all-in.

Data privacy and third-party models

• The OpenAI connection: It's important to know that Atlassian Intelligence sends some of your data to OpenAI's large language models to power certain features. Atlassian has policies in place saying that OpenAI won't store your data long-term or use it to train their public models. However, for companies with extremely strict data security rules, having data processed by another company can be a deal-breaker.

• Permissions matter (a lot): The AI is smart enough to respect your existing user permissions in Jira and Confluence. This is great for security, but it can lead to weird situations where two people on the same team ask the AI the exact same question and get different answers because one of them doesn't have access to a specific Confluence space. It can create confusion and make the AI feel inconsistent.

The hassle of setup

• Not a simple on/off switch: You can't just wake up one day and decide to turn on AI for yourself. An organization admin has to activate the features product by product. And for the more advanced stuff, like connecting external knowledge sources for the virtual agent, it requires a decent amount of configuration.

• The contrast with self-serve tools: This is where things can get a bit clunky. A platform like eesel AI, on the other hand, is designed to be completely self-serve. You can sign up, connect your company’s Google Drive, Notion, and Confluence, and have a working AI assistant ready to answer questions in your team's Slack in just a few minutes, all without needing to file a ticket with IT.

A murky pricing model

• It's bundled in: Atlassian Intelligence is automatically included for any product on a Standard, Premium, or Enterprise plan. If your team is on a Free plan, you're out of luck.

• You can't buy it separately: Because the cost is baked into your overall subscription, it’s hard to know how much you're actually paying for the AI features. This makes it tough to calculate the return on investment. Are you paying a lot for features your team rarely uses? It’s hard to say.

• Watch out for usage costs: Some eagle-eyed users in the Atlassian community have raised concerns that Rovo, the AI engine, might have usage limits. This could mean that if you rely on it heavily to search a large volume of external content, you might hit a quota and face extra charges. This lack of clear, predictable pricing can make budgeting a real headache.

Feature	Atlassian AI Project Management	eesel AI
Setup Time	Admin-led, can take hours or days	Self-serve, get started in minutes
Knowledge Sources	Primarily Confluence; limited connectors like SharePoint and Google Drive	100+ integrations (Confluence, Google Docs, Notion, Zendesk, Slack, etc.)
Primary Use Case	AI features embedded inside Jira and Confluence	A central AI chat brain for your company, accessible in Slack/Teams
Pricing Model	Bundled into paid Atlassian plans; potential for hidden usage costs	Predictable monthly fee based on interactions, no surprise charges

The eesel AI alternative to Atlassian AI project management: Connecting all the dots

For Atlassian AI project management to really work wonders, the AI needs a complete picture of your project knowledge. But as we’ve discussed, that knowledge is almost never stored in just one place.

This is where a tool like eesel AI's Internal Chat can make a huge difference, especially for teams that live in Atlassian but also rely on a dozen other tools. It acts as that central AI brain, connecting to all of your knowledge sources, not just the couple that Atlassian supports out of the box.

Imagine a project manager typing a question into Slack: "What was the final decision on the Q3 marketing budget and who was the final approver?" Instead of just searching Confluence and coming up empty, eesel can pull together a single, accurate answer from three different places: the project brief in Confluence, the final budget numbers in a Google Sheet, and the approval confirmation from a Slack channel. That’s a level of context Atlassian AI, on its own, would probably struggle to provide.

With its dead-simple setup, transparent pricing, and obsessive focus on creating a single source of truth, eesel AI gives your team the ability to get instant answers to their questions, right inside the chat tools they’re already using all day long.

What's the verdict on Atlassian AI project management?

Atlassian is making a serious push into AI for project management, and there's no doubt that the new features in Jira and Confluence can help your team be more productive. They can genuinely help you plan faster, automate repetitive tasks, and find information more easily within their ecosystem.

However, the effectiveness of these tools is still pretty tied to how much of your work lives inside Atlassian products. And they come with real things to think about, like data privacy, a sometimes-clunky setup process, and a pricing model that can feel a bit like a black box.

This video showcases how Jira's AI-powered workflows and agents can reimagine Atlassian AI project management.

For most teams that work across a variety of tools, the smartest approach might be to use Atlassian's built-in AI for what it's good at (like summarizing Jira comments) and supplement it with a dedicated knowledge platform. This ensures your AI assistant has access to the whole story, allowing it to give your team the truly helpful and accurate answers they need to keep projects moving.

Ready to give your team a single brain that knows everything about your projects? Explore how eesel AI unifies your knowledge from Atlassian and all your other tools.