A practical guide to Atlassian Intelligence AI in Analytics

There’s a lot of talk about AI in project management and data analysis lately, and for good reason. Teams are always looking for smarter ways to get work done, and Atlassian's answer is Atlassian Intelligence. It promises to bring AI capabilities right into the tools you’re already using every day.

This post is a straightforward, no-fluff look at Atlassian Intelligence AI in Analytics. We'll get into what it actually does, what the real-world limitations are (based on feedback from people who use it), and how much it really costs. By the end, you should have a much clearer picture of whether it’s the right move for your team.

What is Atlassian Intelligence AI in Analytics?

Atlassian Intelligence is a collection of AI-powered features built directly into the Atlassian Cloud platform, which you probably know from tools like Jira, Confluence, and Jira Service Management. The main idea is to help your team be more productive by automating tasks, summarizing long comment threads, and even generating new content.

Under the hood, it’s a combination of Atlassian's own machine learning models and tech from partners like OpenAI. This allows it to do a bunch of different things, from drafting a quick project update to translating a comment into another language.

But here’s the catch: these features aren't available to everyone. They are mostly bundled with Atlassian’s Premium and Enterprise plans, which means getting access usually involves a pretty big upgrade and a larger budget.

Core features of Atlassian Intelligence

Let's break down what the AI can actually do inside the Atlassian Analytics tool. While these features sound impressive, how they perform in the real world can be a bit of a mixed bag.

Generating SQL queries from natural language

This feature lets you ask for data in plain English instead of writing code. For example, you could type something like, "show me all high-priority issues updated last week," and the AI is supposed to spit out a perfect SQL query.

On paper, this sounds amazing for team members who need to pull reports but don't know SQL or Jira Query Language (JQL). But if you look at what users are saying, the reality is a little different. A similar feature in Jira that converts natural language to JQL has gotten some pretty rough feedback. <quote text="Users on Reddit have called it things like "absolute terrible" and "useless" for anything beyond the most basic 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/1ch60b6/jira_cloud_ai_experiences/"> When a key feature isn't reliable, you start to lose trust, and you end up wasting more time checking the AI’s work than if you’d just written the query yourself.

Getting automated chart insights

With this feature, you can click an "Insights" button on any chart in your analytics dashboard, and the AI will generate a short text summary of what the data is showing. It might point out that a certain metric is trending up or flag a potential oddity.

This can be useful for getting a quick overview of a chart without having to squint at it to figure out the story. The main downside, though, is that these insights are descriptive, not prescriptive. The AI can tell you what is happening (like, "the number of open tickets increased by 20% this week"), but it can't tell you why it's happening or what you should do next. It just doesn't have the deeper business context to give you truly helpful advice.

Creating custom formulas

For more advanced users, Atlassian Intelligence can help build SQLite expressions for custom formulas. You can describe the calculation you need in plain English, and the AI will try to create the formula for you.

It's a handy little tool for folks who are already comfortable building complex reports. But just like the SQL generator, it all depends on the user knowing exactly what to ask for, and its accuracy can get shaky with more complicated requests. It's a nice assistant for a very specific task, but it probably won't revolutionize how most teams work.

Limitations and real-world challenges

Turning on Atlassian's AI isn't always as simple as it sounds. Teams often bump up against practical problems that limit its usefulness, especially when you compare it to more specialized AI platforms.

The "walled garden" problem

Atlassian Intelligence is built to work with data that lives inside the Atlassian world, like your Jira issues and Confluence pages. It does a decent enough job there, but what about all the other places your company’s knowledge is stored?

Most teams use a whole stack of different tools. Your most important documents might be in Google Docs, key conversations are happening in Slack, and your customer support history is logged in Zendesk. Atlassian's AI can't see any of that, which means its answers will always be missing a huge piece of the puzzle.

This is where tools built to connect all your knowledge sources really stand out. For example, eesel AI integrates with over 100 sources right away. It connects to your wikis like Confluence and Google Docs but also pulls information from your help desks and chat tools to create a single source of truth. That way, your AI has the full story and can give you answers you can actually rely on.

This infographic shows how eesel AI's Atlassian Intelligence AI in Analytics integrates with various data sources to provide comprehensive insights.

Inconsistent accuracy: The high cost of "good enough"

As we touched on earlier, users have found that the AI can be hit-or-miss, especially when generating complex JQL or automation rules. This means teams have to constantly double-check what the AI is doing, which really defeats the whole point of automation. It’s one thing if an internal tool is a little flaky, but you can’t risk rolling out an unreliable AI to your customers. That’s a quick way to damage trust.

The best AI platforms are designed to prevent this exact problem. For instance, eesel AI has a simulation mode that lets you test your AI agent on thousands of your own past tickets. You can see exactly how it would have responded, review its answers, and adjust its behavior before it ever talks to a real customer. It's all about building confidence and making sure things go smoothly.

A screenshot of the eesel AI simulation mode, a key feature for ensuring the accuracy of Atlassian Intelligence AI in Analytics before deployment.

The hidden costs of adoption

Getting access to Atlassian Intelligence isn't cheap. <quote text="It often means moving to Atlassian Cloud's Premium or Enterprise tiers, a process one user on Reddit described as a "long, expensive, and royal PITA."" sourceIcon="https://www.iconpacks.net/icons/2/free-reddit-logo-icon-2436-thumb.png" sourceName="Reddit" sourceLink="https://www.reddit.com/r/jira/comments/1ch60b6/comment/l007x77/">

On top of that, Atlassian is launching a new, premium add-on called Rovo. Users in the early access program have said it might cost around $24 per user per month, which adds another significant, and maybe unpredictable, expense to your software budget.

This is a big difference from platforms like eesel AI, which has clear, predictable pricing with no fees per resolution. You can get it set up in just a few minutes by connecting it to your helpdesk, no months-long migration project needed.

A screenshot of eesel AI's clear and predictable pricing page, which is a key advantage when considering the cost of Atlassian Intelligence AI in Analytics.

Atlassian Intelligence pricing and availability

So, how do you actually get your hands on Atlassian Intelligence? It’s not something you can just buy off the shelf. The core AI features are bundled exclusively with their Cloud Premium and Cloud Enterprise plans. If you're on a Standard or Data Center version, you won't have access.

To give you an idea of the cost, let's look at the Jira Software plans. If you're on the Standard plan (around $8.15 per user), you can't use the AI. To get it, you'd need to upgrade to the Premium plan, which jumps to about $16.00 per user, per month. That's a pretty steep price hike just for AI features. And if you want the Enterprise plan, you'll have to contact their sales team.

And don't forget, that's just the starting price. More advanced AI tools, like the new Rovo platform, are expected to be a separate, premium add-on. This can make the total cost much higher and turn budgeting into a real headache.

Beyond analytics: A better way to use AI

Looking at charts and data is helpful, but it's a passive activity. The real value of AI comes from putting it to work, actively automating tasks, resolving issues, and answering questions for your team and your customers.

This is where a dedicated AI platform like eesel AI really shines. It’s not just another feature added to an existing product; it’s a fully customizable engine built for taking action.

• Bring all your knowledge together. Don't let your AI only learn from Jira tickets. Let it learn from past customer conversations in Zendesk, internal guides in Notion, and all the knowledge shared day-to-day in Slack. A smarter AI starts with better, more complete data.

• Automate with confidence. eesel AI’s workflow engine gives you full control. You can define exactly which types of tickets the AI should handle and what actions it can take. It can triage a request, escalate it to a specific team, or even look up order information in Shopify with an API call. Best of all, it learns from your actual past tickets to respond in your company's unique voice.

• Go live in minutes, not months. You don't need to plan a massive cloud migration to get started. eesel AI has one-click integrations with the tools you're already using, including Jira Service Management, so you can start seeing a return on your investment almost immediately.

Atlassian Intelligence: Good for basics, but limited for serious automation

So, what's the verdict on Atlassian Intelligence AI in Analytics? It offers some nice-to-have features for teams that are already paying for premium cloud plans and are all-in on the Atlassian ecosystem. It can make data analysis a bit more accessible and provide quick summaries, which is great.

However, the limitations are pretty significant. The walled-garden approach to data, inconsistent accuracy that users have pointed out, and a complicated and expensive pricing model make it a tough sell for teams serious about automation. If you're looking to use AI to actually resolve tickets, answer tough questions, and streamline workflows using knowledge from all your company's tools, a dedicated, integration-first platform is a much better bet.

Ready to see what AI can really do for your support and IT teams? See how eesel AI works seamlessly with your existing tools like Jira Service Management and Confluence to deliver actual results. Start your free trial today.