A practical guide to Atlassian Intelligence AI in Product Discovery

Let's be real: product discovery is a mess. Great ideas and customer feedback are all over the place, scattered across Slack threads, support tickets, emails, and who-knows-how-many docs. AI is supposed to help sort through this chaos, and Atlassian is throwing its hat in the ring.

Atlassian Intelligence is the company's attempt to weave AI into its entire suite of tools. Where it gets really interesting for product teams is its use inside Jira Product Discovery. This guide will give you a straight-up look at what it can do, what it costs, and, more importantly, where it hits a wall. We’ll explore what it does well and look at a more flexible option for teams whose knowledge isn’t all neatly tucked away in the Atlassian ecosystem.

What is Atlassian Intelligence AI in Product Discovery?

First things first, Atlassian Intelligence isn't a separate tool you can just go out and buy. It’s a collection of AI features baked directly into the Atlassian platform. Think of it as a little assistant that pops up right where you’re already working.

Under the hood, it’s powered by a mix of OpenAI’s models and Atlassian’s own "Teamwork Graph." The graph is basically a map of your company's projects, teams, and workflows, which is meant to give the AI some context so it doesn't spit out totally random suggestions.

Jira Product Discovery is where product managers are supposed to capture ideas, prioritize them, and build roadmaps before a single line of code is written. Adding Atlassian Intelligence is all about making that messy "before" phase a little less painful. It helps you generate, summarize, and tweak content inside your ideas and comments, so you can keep things moving without constantly switching tabs.

Key features and capabilities of Atlassian Intelligence

So, what can you actually do with this thing? In Jira Product Discovery, Atlassian Intelligence is mostly focused on helping you work with text. It’s about making the process of writing, refining, and understanding ideas a bit faster.

Generate and brainstorm new ideas

We’ve all been there, staring at a blank page with no idea where to start. Atlassian Intelligence can give you a little nudge. By typing "/ai" or clicking the AI icon in an editor, you can feed it a prompt to get the ball rolling.

For instance, a PM could type, "/ai brainstorm 5 potential solutions for improving user onboarding," and get a list of suggestions right in the idea's description. It’s a decent way to spark some initial thoughts for feature descriptions, talking points, or potential risks.

Summarize long threads and descriptions

This is probably one of its most practical uses. Product discovery often means digging through long documents, dense customer feedback, and comment threads that never seem to end. Instead of blocking off an hour to read it all, you can ask the AI to give you the short version.

This works on idea descriptions, comments, and even summaries of votes from your team. It saves a lot of time and helps new team members or stakeholders get the gist of a topic without getting bogged down in every single detail.

Transform and refine existing content

The AI isn’t just for creating new text; it also works as a writing assistant to clean up what you’ve already got. It can help you polish your ideas to make sure they're clear and ready to be shared with a wider audience.

A few things it can help with are:

• Fixing spelling and grammar: Just a quick and simple way to proofread your text.

• Changing the tone: You can ask it to make your writing more formal for an exec summary or more casual for a team update. It can even try to make it more empathetic when you’re sharing customer feedback.

• Finding action items: It can scan a block of text and pull out a to-do list, which is handy for turning meeting notes into actual tasks.

• Simplifying complex info: If you’re trying to explain a technical idea to a non-technical crowd, the AI can help you rephrase it in simpler terms.

The hidden limitations of Atlassian Intelligence

While these features are helpful, they all operate within the cozy confines of the Atlassian world. For a lot of modern product teams, these boundaries aren't just small annoyances, they’re real obstacles to building a product that truly reflects customer needs.

The 'walled garden' problem: Knowledge is siloed

The biggest issue is that Atlassian Intelligence mainly works with data that's already inside Atlassian products like Confluence and Jira. But let’s be honest, that’s not where all the good stuff lives.

The most valuable insights are usually buried in Zendesk tickets, Intercom chats, Slack conversations, and collaborative Google Docs. When your AI can’t see this data, its understanding of what your customers actually want is incomplete. You end up with a fragmented view that might lead you to prioritize the wrong things, simply because the AI is blind to what’s happening everywhere else.

Limited control and no-risk testing

With Atlassian Intelligence, the features are pretty much either on or off, depending on your subscription. There isn't a lot of room to tweak things, which brings up a big question: how do you know if the AI will even work well with your data before you go all-in?

There’s no simulation mode to test the AI on your past data. You can't see how it would have summarized last year's feature requests or what ideas it might have pulled from last quarter's feedback. This lack of a safe testing ground means you’re essentially flying blind. You can't predict how it will perform, find its weak spots, or build any confidence before you roll it out to your entire team.

A closed loop on content creation

The AI is pretty good at summarizing information you feed it, but it doesn't do much to help you proactively fill your knowledge gaps. For example, it can't automatically review resolved support tickets and then suggest new articles for your help center.

This means your team still has to manually figure out what customers are struggling with and then create the content to help them. It feels like a missed opportunity to turn your team’s hard-earned knowledge into a resource that helps customers help themselves.

Getting started with Atlassian Intelligence: Prerequisites and pricing

Atlassian Intelligence isn't an add-on you can purchase separately. Access is tied directly to your subscription plan, which can be a major hurdle for many teams.

Here’s what you need to know:

• Prerequisites: You have to be on a Jira Product Discovery Premium plan. The AI features are not available on the Free or Standard tiers. An organization admin also needs to go into the main settings and officially activate Atlassian Intelligence.

• Pricing: Since you need the Premium plan, you're not just paying for AI. You often have to upgrade your entire Jira Software subscription to a Premium or Enterprise plan to get it.

This pricing model can feel pretty rigid. If your team only needs the AI for product discovery but doesn't need all the other features in Jira's top-tier plans, you're stuck paying for a big, expensive bundle.

Plan	Cost (per user/month)	Atlassian Intelligence Access
Jira Product Discovery Free	$0 (up to 3 creators)	No
Jira Product Discovery Standard	$10	No
Jira Product Discovery Premium	(Part of Jira Software Premium/Enterprise)	Yes

A better way: Unify product discovery with a flexible AI

For teams that need to break out of these silos, a more open and controllable AI platform is the way to go. Instead of getting locked into one ecosystem, you need a tool that can connect to all your sources of information.

This is where eesel AI comes into the picture. It was built from the start to work across all your different tools, giving you a single, complete view of your product landscape.

Here’s how eesel AI tackles the shortcomings of a siloed approach:

• Unify all your knowledge: eesel AI connects to Jira Service Management, Confluence, and all the other places your insights are hiding. With over 100 integrations, including Slack, Zendesk, and Google Docs, it creates one source of truth for your AI. No more walled gardens.

• Go live in minutes with total control: You don’t have to wait for an admin or commit to a massive, platform-wide rollout. eesel AI has a self-serve setup you can get done in a few minutes. Its simulation mode lets you test the AI on your historical data, so you can see exactly how it will perform and figure out your ROI before you ever go live. You also get an intuitive prompt editor that gives you full control over the AI's personality and tone.

• Close knowledge gaps automatically: eesel AI doesn't just summarize what’s already there; it helps you build what’s missing. It can analyze support conversations from your help desk, spot common problems with proven solutions, and automatically generate draft articles for your knowledge base. This helps you build out a useful help center based on real customer issues.

Build what matters with the right intelligence

Atlassian Intelligence is a decent step forward for teams who live and breathe the Atlassian ecosystem. It has some genuinely useful features for brainstorming and summarizing ideas right inside Jira Product Discovery.

But its usefulness is ultimately limited by its data silos and all-or-nothing controls. An effective product discovery process has to be informed by every customer conversation and every piece of internal knowledge, no matter where it’s stored.

To get the full picture, you need a tool that can bring all of that intelligence together.

Ready to unify your product insights and focus on what truly matters? Get started with eesel AI for free and see what a connected AI can do for your workflow.