Atlassian intelligence: A realistic guide to AI in Jira and Confluence

Atlassian has officially jumped on the AI bandwagon, and it’s hard to miss the buzz around Atlassian Intelligence. They’re pitching it as a "virtual teammate" that will change the way you work across their entire suite of products. But once you get past the marketing splash pages, what’s it actually like to use day-to-day?

This guide is a straight-up, no-fluff look at what Atlassian’s AI features can really do inside Jira and Confluence. We’ll dig into the core features, unpack the pricing, and talk about the real-world limitations that teams are already running into. The goal is simple: to help you decide if Atlassian Intelligence is the right move for your team, or if you might be better off with a more focused AI tool.

What is Atlassian Intelligence?

First things first, Atlassian Intelligence isn’t a standalone product you can just go out and buy. It’s a set of AI-powered features that have been woven into their cloud products, like Jira, Confluence, and Jira Service Management. Think of it less as a new tool and more as an AI layer sitting on top of the apps you already use.

The whole thing is powered by a new platform Atlassian calls Rovo. They claim Rovo uses a "Teamwork Graph" to get the lay of the land, your company’s data, projects, and how your teams are structured. This context is supposed to serve up more relevant, personalized answers. Under the hood, it’s a mix of Atlassian’s own models and large language models (LLMs) from third parties like OpenAI.

That last bit is worth paying attention to. To generate a response, your data (say, the text from a Confluence page you want to summarize) gets processed by these outside services. While Atlassian has data protection policies, it’s something to keep in mind if your team has tight security or privacy rules.

Atlassian Intelligence for Jira: What it can and can’t do

For many of us, Jira is the daily command center, and the idea of AI streamlining that work is pretty tempting. Let’s look at the main features and see how they actually hold up, based on what real users are saying.

Asking Jira questions in plain English

The pitch is great: instead of wrestling with Jira Query Language (JQL), you can just ask a question like you would a person. For example, typing "show me all high-priority bugs in Project Phoenix assigned to Sarah this month" should, in theory, generate the correct JQL query.

In practice, it’s a bit of a mixed bag. For simple queries, it works pretty well and can be a nice little assistant for new users who haven’t memorized JQL syntax. But as soon as things get a bit more complex, it tends to stumble. Plenty of experienced Jira admins have noted that it often produces incorrect or incomplete queries. <quote text="As one user bluntly put it, "it's been absolute terrible every time... it botches the simplest of questions."" sourceIcon="https://www.iconpacks.net/icons/2/free-reddit-logo-icon-2436-thumb.png" sourceName="Reddit" sourceLink="https://www.reddit.com/r/jira/comments/1e7ihwj/jira_cloud_ai_experiences/">

If you already know your way around JQL, you’ll probably find it’s faster and more reliable to just write the query yourself. It’s a feature built for beginners, not power users.

AI-powered summaries and task breakdowns

This is where Atlassian Intelligence starts to feel a bit more useful. A couple of features really stand out here.

First, the summarization tool. The AI can read through a long, rambling comment thread on a Jira ticket and spit out a concise summary. Honestly, this is one of its most genuinely helpful features. It’s a massive time-saver for anyone who’s ever had to jump into a ticket mid-conversation and figure out what’s going on.

A screenshot showing the AI summarization feature in action on a Jira ticket, which is a key part of Atlassian Intelligence.

Second is the work breakdown feature. You can ask the AI to suggest how to break down a big epic into smaller stories, or a user story into sub-tasks. The catch is that its suggestions are only as good as the information it’s given. If your epic description is vague, you’ll get generic, cookie-cutter sub-tasks that aren’t very useful. It can give you a starting point, but you should expect to do a fair bit of manual editing to make the tasks truly actionable. The same goes for creating automation rules from natural language, it’s a neat idea, but users have found it doesn’t always get the rules right.

This video demonstrates how you can use Atlassian Intelligence to break down epics into smaller, manageable stories within Jira.

Atlassian Intelligence for Confluence: The highlights and holes

Confluence is where your company’s knowledge is supposed to live, making it a prime candidate for some AI assistance. Atlassian Intelligence aims to make all that information easier to create and find. Here’s a look at what works well and where the gaps start to appear.

Finding answers with Q&A search

One of the big-ticket features is the ability to ask questions in the search bar and get a direct answer, not just a list of links. Asking "what’s our policy on parental leave?" should pull the answer directly from your HR space in Confluence.

A screenshot of the Atlassian Intelligence Q&A search feature providing a direct answer to a user's question.

But here’s the major "if." This feature lives and dies by the quality of your Confluence knowledge base. If your documentation is out of date, poorly written, or scattered across dozens of disorganized spaces, the AI is just going to confidently serve up bad answers. It’s the classic "garbage in, garbage out" problem.

Even more importantly, there’s the silo problem. Atlassian’s AI only knows what’s in Confluence. But let’s be honest, where does your company’s knowledge actually live? It’s probably spread across Google Docs, random Slack threads, Notion pages, and old support tickets. Atlassian’s AI is completely blind to all of that, which means its answers are always going to be incomplete.

Generating and summarizing content

This is another area where the AI feels pretty handy. Inside the Confluence editor, you can ask the AI to draft meeting notes, summarize a lengthy project plan, switch a paragraph’s tone from formal to casual, or brainstorm ideas for a blog post.

These features are solid time-savers. They’re great for getting past that "blank page" feeling or churning out a rough first draft of a document. Just remember to treat the AI as a writing assistant, not a replacement for a human. It’s great for getting the ball rolling, but you’ll always need someone to go back and edit for accuracy, voice, and company-specific details.

This video provides an introduction to how Atlassian Intelligence can assist with content creation and formatting within Confluence.

The real cost of Atlassian Intelligence

So, what’s this all going to set you back? It’s not as simple as you might hope. Atlassian Intelligence isn’t a separate add-on; it’s bundled into their cloud subscription plans.

The biggest wrinkle is the "AI credits" system. If you’re on a Standard or Premium plan, you don’t get unlimited use of the AI features. Instead, each user gets a monthly allowance of credits. Every time you generate a summary or ask a question, you spend some of those credits. This can lead to some unpredictable costs, and you could find your team hitting a usage wall before the end of the month.

Here’s a quick look at how the Jira and Confluence plans compare:

Plan	Jira Price (per user/mo)	Confluence Price (per user/mo)	Key AI Features & Limitations
Free	$0	$0	No Atlassian Intelligence features.
Standard	$7.53	$5.16	Rovo Search, Chat & Agents. Limited to 25 AI credits per user/month.
Premium	$13.53	$9.73	Everything in Standard. Increased to 70 AI credits per user/month.
Enterprise	Contact Sales	Contact Sales	Everything in Premium. Increased to 150 AI credits per user/month.

The takeaway is pretty clear: if you want to use Atlassian Intelligence without constantly checking your credit balance, you’re basically nudged toward the more expensive Premium and Enterprise plans. When you add in the cost of the full Rovo platform ($24/seat/month), the price tag for Atlassian’s AI ecosystem can climb quickly.

Is there a better way to connect your knowledge?

After looking at the features and costs, a clear picture starts to form. Atlassian Intelligence is a convenient add-on with some neat tricks, but it’s held back by some big limitations. It’s locked into the Atlassian ecosystem, it relies on a perfectly curated knowledge base, and its pricing can be both confusing and expensive.

This is where a dedicated AI platform like eesel AI offers a different approach. Instead of trying to be a little bit of everything, eesel AI is built to do one thing exceptionally well: connect all of your scattered company knowledge to give your team fast, accurate answers.

It directly addresses the main weaknesses of Atlassian Intelligence:

It brings all your knowledge together. Atlassian’s AI lives in a silo. eesel AI connects to Confluence, sure, but it also plugs into Google Docs, Notion, Slack, and even pulls from past support tickets in help desks like Zendesk or Freshdesk. This means the answers your team gets are based on the whole picture, not just a small piece of it.

An infographic showing how eesel AI connects various knowledge sources, a key advantage over the siloed Atlassian Intelligence system.

You can get started in minutes. You don’t need a pricey plan upgrade or a complicated data migration. eesel AI is designed to be self-serve. You can connect your knowledge sources with a few clicks and have your first AI agent up and running in minutes, not months.

A workflow diagram illustrating the simple, self-serve setup process for eesel AI.

You get full control and safe testing. This is a huge one. Before your AI ever talks to a real user, you can test it on thousands of your past support tickets or internal questions. This gives you a clear, accurate forecast of how it will perform, letting you de-risk the whole process, something Atlassian just doesn’t offer.

A screenshot of the eesel AI simulation feature, which allows teams to test AI performance before deployment, a feature not available in Atlassian Intelligence.

Is Atlassian Intelligence the right AI for your team?

At the end of the day, Atlassian Intelligence offers some nice, built-in perks for teams that live and breathe the Atlassian cloud ecosystem and are okay with its limitations. The summarization and basic content drafting tools can definitely shave some time off your daily tasks.

However, if your team needs truly reliable automation, comprehensive answers from all your company’s knowledge, and a predictable price, it just doesn’t quite hit the mark. Its reliance on a pristine Confluence setup and its inability to see anything outside the Atlassian world are major drawbacks that limit its real-world impact.

If you’re serious about using AI to answer internal questions or automate support, a dedicated platform is likely a more powerful and flexible way to go. It might be worth exploring how eesel AI can unify all your scattered knowledge to provide accurate, reliable answers right where your team already works.