What is Atlassian’s AI called? An honest look at Rovo and its alternative

Let’s be real, everyone is talking about putting AI into the tools we use every single day. If your team lives and breathes in Jira and Confluence, the idea of an AI that just gets your projects without a ton of setup sounds amazing. But like many things that seem a little too good to be true, AI that’s built right into a platform can come with some hidden catches.

Atlassian has officially entered the AI race, and a lot of teams are asking the same simple question: "What is Atlassian’s AI called?" This post is here to give you the straight scoop on their new AI, Rovo. We’ll cover what it is, what it promises to do, and, more importantly, where it falls short in the real world.

We’ll also look at why a dedicated AI platform is often a more practical choice for teams that need more flexibility, a quicker setup, or the ability to pull knowledge from all their tools, not just one ecosystem.

What is Atlassian’s AI called? Meet Rovo

Alright, let’s get right to it. Atlassian’s new AI is named Rovo. It’s the main product under a bigger umbrella of features they call Atlassian Intelligence.

Rovo is billed as an "AI teammate" that works across the whole Atlassian suite, including Jira, Confluence, Trello, and the rest. The goal is pretty big: to give you one AI that can dig up info from all your apps, answer questions with context, handle boring tasks, and even help you write new content.

The secret sauce here is something Atlassian calls its "Teamwork Graph." Fancy name, but it’s basically a map of how your teams, projects, documents, and goals are all connected, built from over two decades of data. In theory, this gives Rovo a deep understanding of how your company actually works, letting it provide smarter answers. It’s a great idea, but how does it hold up when you actually try to use it?

A breakdown of Rovo’s key features

To get a feel for what Rovo is supposed to do, it helps to look at its main parts. Atlassian has focused on two big things: bringing all your information together and automating your workflows.

Rovo search and chat: Unifying information

The first piece of the puzzle is solving the problem of knowledge silos. We’ve all been there, flipping between ten different tabs trying to find that one document or the latest update on a project.

• Rovo Search: This is Rovo’s attempt at a universal search bar. It’s built to look through not just your Atlassian tools but also other apps you connect, like Google Drive, Slack, and Figma. The idea is to have one place to find anything you need, no matter where you saved it.

• Rovo Chat: This is where you can "talk" to Rovo. You can ask it normal questions like, "What were the main decisions from the Q3 planning meeting?" and it’s supposed to pull answers from all your connected documents and tools. It can also do simple things, like creating a Jira ticket right from a chat, which is a handy way to turn conversations into actual work.

A look at the Rovo Chat interface, showing how a user can ask questions and receive AI-generated answers based on their internal knowledge. This is Rovo in action.:

Rovo agents and studio: Automating workflows

Beyond just finding stuff, Rovo wants to do stuff for you. This is where the automation side comes into play.

• Rovo Agents: Think of these as little AI helpers that are programmed to handle specific, repetitive jobs. Atlassian gives you some pre-built agents for common tasks, like drafting release notes from a list of Jira tickets or generating a post-incident review in Jira Service Management.

• Rovo Studio: For teams with more unique workflows, Rovo Studio is a tool that lets you build your own custom agents without needing to code. It’s meant for project managers or other non-devs who want to create their own AI automations.

On paper, this all sounds pretty fantastic. But what happens when you actually try to roll it out for your team?

The reality of implementing Rovo: Cost, setup, and limitations

Getting from a slick marketing page to a tool your team uses every day is where the reality check usually happens. Based on what early users are saying and what’s in Atlassian’s own documentation, putting Rovo to work comes with some serious hurdles.

Rovo’s pricing and platform requirements

First things first, you can’t just buy Rovo as an add-on. Access to Rovo and Atlassian Intelligence is bundled only with Atlassian Cloud Premium or Enterprise plans.

This is a really big deal. For the thousands of companies running on Standard or Data Center versions of Jira and Confluence, using Rovo means you’re forced into an expensive upgrade or migration. This isn’t just a few extra bucks; it’s a major price jump that can be a complete non-starter for a lot of teams.

Here’s a quick look at what that leap looks like, based on current pricing for Jira Software.

Plan Tier	Price per user/month (approx.)	Rovo / Atlassian Intelligence
Standard	~$8.75	Not included
Premium	~$16.75	Included
Enterprise	Contact Sales	Included with advanced features

For a team of 50, moving from Standard to Premium just to get Rovo could cost an extra $4,800 a year, and that’s just for one product. That’s a hefty price for an AI that’s still new.

Common challenges and real user feedback

Cost aside, early adopters have been pretty candid about the practical problems they’ve run into with Rovo.

• It’s not exactly plug-and-play: <quote text="As one user on Reddit pointed out, Rovo is "super useful just not out the box."" sourceIcon="https://www.iconpacks.net/icons/2/free-reddit-logo-icon-2436-thumb.png" sourceName="Reddit" sourceLink="https://www.reddit.com/r/atlassian/comments/1nxfjsu/anyone_here_tried_atlassians_new_ai_rovo_from/"> To get it to do what you want, you have to get good at writing natural language prompts and building your own workflows. <quote text="Another user summed it up well: the people who need these tools the most "are too busy doing their day jobs to spend hours learning" them." sourceIcon="https://www.iconpacks.net/icons/2/free-reddit-logo-icon-2436-thumb.png" sourceName="Reddit" sourceLink="https://reddit.com"> It’s a classic "too busy to invent the wheel" problem.

• It feels a bit "half-baked": Reliability seems to be another issue. <quote text="One product owner mentioned that their attempt to do a simple backlog analysis failed, even after they gave Rovo the correct JQL query. They ended up feeling like it was "another Atlassian half-baked feature."" sourceIcon="https://www.iconpacks.net/icons/2/free-reddit-logo-icon-2436-thumb.png" sourceName="Reddit" sourceLink="https://reddit.com"> When you’re trying to automate important tasks, "it mostly works" just doesn’t cut it.

• There are gaps in compliance and data security: This one is huge. Tucked away in the documentation is the fact that Rovo is not HIPAA compliant. This immediately takes it off the table for any company in healthcare or a related industry that deals with protected health information. Atlassian talks a lot about trust, but this gap shows Rovo isn’t quite ready for businesses in regulated fields.

• It’s very Atlassian-focused: While Rovo can connect to a few outside apps, its heart and soul are firmly in the Atlassian world. If your company’s knowledge is spread across Notion, Google Docs, your Zendesk history, and other tools, Rovo will probably have a hard time giving you the full story.

A more flexible approach: Unify knowledge with a dedicated AI platform

The problems with Rovo point to a common issue with platform-specific AI: you’re stuck working inside their world, on their schedule, and at their price. For teams that need an AI solution that’s agile, affordable, and works with all the tools they already use, a dedicated, tool-agnostic platform often makes a lot more sense.

Go live in minutes, not months

While getting real value from Rovo can take hours of tinkering and learning, a platform like eesel AI is built to be self-serve from the ground up. You shouldn’t have to become a prompt engineer just to automate Tier 1 support tickets.

With eesel AI, you can sign up and get going for free without ever having to talk to a salesperson. Its one-click integrations with help desks like Zendesk, Freshdesk, and Jira Service Management mean you can have a basic AI answering questions in minutes. This simplicity gets around the "too busy to learn a new tool" problem and starts delivering value right away.

Unify all your knowledge, wherever it lives

Rovo is designed to keep you inside the Atlassian bubble. But your company’s collective brain is probably scattered across a dozen different apps.

A dedicated platform like eesel AI is built to be the glue for your entire knowledge base. It doesn’t care where your info is. You can easily train it on your Confluence pages, but you can also pull in knowledge from your Google Docs, Notion wikis, past support tickets from any help desk, and even conversations from Slack. This creates a single source of truth, leading to an AI that gives much more accurate and complete answers because it’s not stuck in one vendor’s world.

A look at eesel AI's extensive integration library, a key advantage for companies wondering what Atlassian's AI is called and its limitations.:

Test with confidence and deploy gradually

Nobody wants to unleash a "half-baked" AI on their customers or their own team. The feedback on Rovo shows that people are hesitant to trust a new and sometimes flaky tool with live work.

This is why testing is so important. eesel AI has a simulation mode that lets you test your setup on thousands of your own past tickets in a safe environment. You can see exactly how it would have answered, get solid forecasts on resolution rates, and tweak its behavior before a single customer ever interacts with it.

This risk-free approach helps you build confidence and roll it out slowly. You can start by having the AI handle just one or two specific types of tickets, sending everything else to a human. As you see it working well, you can expand what it does at a pace that works for you.

Rovo is promising, but readiness matters

So, what is Atlassian’s AI called? It’s Rovo, and it’s a big, ambitious vision for how AI can help teams work together. But the reality today is that it’s a pricey, complicated, and sometimes unreliable tool that just isn’t a good fit for many teams. The high cost, learning curve, and platform lock-in mean you could be waiting a long time for it to deliver on its promises.

For companies that need to solve support automation and knowledge management problems today, a dedicated, flexible AI platform is the smarter move. You don’t have to wait around for platform-native AI to get its act together.

You can start automating your support and bringing all your company knowledge together in minutes with eesel AI.