You've probably heard about Atlassian Rovo. It's being pitched as the new AI teammate that slots right into Jira, Confluence, and your other tools. The promise is big: finding info faster, automating annoying tasks, and just generally making teamwork smoother with a bit of AI magic.
But let's cut through the hype for a second. What can Rovo actually do for your team right now? This guide takes a practical look at the Rovo Use Cases Library. We'll dig into its key features, see what it can do for different teams, and, most importantly, talk about the serious limitations around its pricing, flexibility, and data privacy that you need to be aware of.
What is Atlassian Rovo and the Rovo Use Cases Library?
Think of Atlassian Rovo as an AI assistant that lives inside your Atlassian tools. It can search for information, answer questions, and handle tasks by drawing on your company's knowledge base. The tech behind it is what Atlassian calls the "Teamwork Graph," which sounds fancy but just means it tries to understand the relationships between your projects, docs, and teams to give you better answers.
Rovo is essentially broken down into three main parts:
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Rovo Search: This is an AI-powered search bar that digs through your Atlassian tools and can even connect to third-party apps like Google Drive and Slack to find what you're looking for.
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Rovo Chat: A chat interface where you can talk to the AI in plain English. Ask it to summarize a document, pull some insights from your data, or answer a question.
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Rovo Agents: These are little AI assistants built for specific jobs. You can use pre-built ones or try to create your own to automate things like triaging tickets or planning out development sprints.
Exploring the Rovo Use Cases Library: What can it actually do?
The official Rovo Use Cases Library shows off a bunch of potential applications for different departments. It looks pretty impressive at first glance, but it's worth understanding how these use cases play out in the real world, warts and all.
For software development and engineering teams
Rovo has several agents that aim to speed up the day-to-day grind of software development.
A popular one is the Code Planner & Implementor. These agents can look at a Jira issue, scan the requirements in linked Confluence pages, and spit out a technical plan. The Implementor agent can even take a shot at writing the first draft of the code for a developer to look over.
Then there's the Code Reviewer, which automatically checks pull requests. It compares the code to the acceptance criteria in the Jira ticket and flags any obvious syntax errors or overly complex bits. Atlassian even claims their own teams saw a 45% improvement in pull request cycle time using it.
For IT operations and service management
For the folks in IT and support, Rovo offers agents to help manage the flood of incoming tickets and incidents.
The Triage Assistant, for instance, can automatically sort and prioritize new support tickets in Jira Service Management. The Service Request Helper jumps in to suggest solutions and draft replies to help your team close tickets faster, while the Ops Guide can pull up relevant documents during an incident to guide the next steps.
The problem is, Rovo often spits out generic responses or creates tickets that someone has to go back and clean up manually. If the AI doesn't have enough context, you might find it just creates more downstream work instead of reducing it.
For business and marketing teams
Rovo isn't just for tech teams; it has some tricks for business and marketing folks, too.
For example, the Release Notes Drafter can generate release notes by summarizing a list of completed Jira issues. The Comms Crafter is supposed to help you write content that matches your company's brand voice.
These are handy for getting a first draft on the page, but getting an AI to consistently nail a specific brand persona is tough. You need a level of control that most out-of-the-box tools just don't offer. If your team needs to fine-tune the AI's personality and tell it exactly what to do, a tool like eesel AI gives you that power with a straightforward prompt editor. You can define the tone of voice and set up custom workflows without needing a developer to get involved.
A screenshot showing eesel AI's prompt editor, which allows for fine-tuning the AI's brand voice and personality, a key limitation in the Rovo Use Cases Library.The reality check: Key limitations and hidden costs
While the use cases sound promising, early feedback from users has uncovered some pretty significant problems with Rovo that could affect your budget, security, and whether it's actually useful.
The confusing and unpredictable pricing model
Okay, let's talk about the elephant in the room: Rovo's pricing. This is where things get... confusing. On the surface, it looks like Rovo is now included in some Atlassian plans, which sounds great. But the catch is a weird "AI credit" system that governs how much you can actually use it.
To make matters worse, Atlassian's own documentation says it "reserves the right to add an associated credit charge" for new features, which makes budgeting for this thing a nightmare.
This kind of pricing model can be really frustrating. In contrast, platforms like eesel AI have clear, predictable pricing based on how many AI interactions you need, with no hidden fees per resolution. You can scale up your AI use without getting a nasty surprise on your bill.
A visual of the eesel AI pricing page, which contrasts with the confusing Rovo Use Cases Library pricing model by showing clear, public-facing costs.| Feature | Atlassian Rovo | eesel AI |
|---|---|---|
| Pricing Model | Confusing "AI credits" per user | Transparent interaction-based tiers |
| Predictability | Low (limits are tight and subject to change) | High (clear monthly interaction limits) |
| Value | Limited to 7 agent uses/user/month on Premium | Generous interaction limits on all plans |
| Billing | Tied to Atlassian subscription | Flexible monthly or annual plans |
Setup complexity and data privacy concerns
Getting AI up and running safely and effectively is another big hurdle. With Rovo, a couple of major concerns pop up right away:
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Data Privacy: Rovo sends your company's data to third-party AI models from OpenAI and Google. For a lot of companies, the idea of sensitive information from Jira and Confluence leaving the Atlassian ecosystem is a complete deal-breaker.
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Lack of Gradual Rollout: Turning on Rovo can feel like flipping a giant switch for everyone at once. There isn't a good way to test how the AI will behave with your specific data and workflows before you let it loose.
Nobody gets AI perfect on the first try. That’s why modern AI tools are built differently. For example, eesel AI has a powerful simulation mode that lets you test your setup on thousands of your past support tickets. You can see exactly how it would have responded, predict its resolution rate, and tweak its behavior in a safe environment before it ever talks to a real customer. eesel AI also offers EU data residency for companies with strict privacy requirements.
A screenshot of eesel AI's simulation mode, a feature that addresses the setup complexity concerns associated with the Rovo Use Cases Library.Is Rovo a true "agentic" AI?
Even with the "agent" name, some users feel Rovo is more like a fancy prompt library than a truly independent system. As mentioned before, the fact that it can't handle custom Jira fields is a huge limitation for many teams.
Real AI automation should bend to fit your way of working, not force you to change your processes to fit the tool. This means it needs to be able to do custom things, like checking an order status in Shopify or updating a user's account in an internal system. Rovo is mostly stuck inside the Atlassian world, whereas a platform like eesel AI gives you a fully customizable workflow engine. Its AI Actions can make API calls to any other system, opening up endless possibilities for automation.
A workflow diagram illustrating eesel AI's customizable automation capabilities, highlighting a key advantage over the more limited Rovo Use Cases Library.Rovo's pricing model explained
Atlassian has made Rovo available on some of its Enterprise, Premium, and Standard Cloud plans for products like Jira and Confluence. But "included" definitely doesn't mean unlimited.
Your usage is limited by a credit system:
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Premium & Enterprise Plans: You get a pool of AI credits. For example, a Premium plan user gets 70 credits per month.
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Credit Consumption: Different actions use up different amounts of credits. Asking a Rovo agent a question costs 10 credits, while a simpler task like summarizing a page costs less.
The biggest problem here is that there's no public price list for buying more credits. This makes it impossible to budget for Rovo if you plan on using it for more than just a few tasks each month.
A better alternative for automated support
For teams that need an AI solution that is powerful, flexible, and doesn't come with surprise costs, a dedicated platform built for control and ease of use is usually a much better bet. While Rovo is built into Atlassian products, eesel AI is designed to integrate just as smoothly with the tools you use, but with some major advantages:
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Go live in minutes, not months: eesel AI is built to be self-serve. You can connect your help desk, train your AI on your existing knowledge base, and launch a working agent in less than an hour, no sales calls required.
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Total control and customization: With a simple prompt editor and a powerful workflow builder, you get to decide exactly how the AI behaves, what its personality is, and what custom tasks it can perform. It adapts to your custom fields and processes, not the other way around.
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Predictable and transparent pricing: Our plans are straightforward: a flat monthly fee for a set number of AI interactions. You won't get penalized with surprise charges for being successful.
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Risk-free simulation: You can test, simulate, and see forecasts of your AI's performance on your own historical data, so you can launch with confidence.
Final thoughts on the Rovo Use Cases Library
Atlassian Rovo shows an ambitious vision for AI inside the Atlassian ecosystem, and its use cases library gives a glimpse into the future of AI-powered teamwork. If your organization is all-in on Atlassian's standard cloud products, it's a handy way to start dabbling in AI.
However, the murky credit-based pricing, data privacy questions, and real-world limitations, like the inability to handle custom fields, make it a risky and potentially expensive bet for many teams. The "free" AI promise wears thin pretty quickly when you run into the tight usage limits and unpredictable costs.
For companies that need transparency, control, and a fast path to actually getting value from AI, a dedicated platform is the smarter way to go.
Ready to see what a truly flexible and predictable AI support solution can do? Try eesel AI for free and automate your first workflow in minutes.
Frequently asked questions
The Rovo Use Cases Library is a collection of potential applications for Atlassian Rovo, showcasing how its AI features can be used across different departments. It highlights Rovo Search, Rovo Chat, and pre-built or custom Rovo Agents designed for specific tasks like summarizing documents, triaging tickets, or drafting code.
For software development, the Rovo Use Cases Library includes agents like the Code Planner & Implementor to generate technical plans and initial code drafts, and the Code Reviewer to check pull requests. These aim to automate repetitive tasks and speed up development cycles.
Key limitations include a confusing "AI credit" pricing model with unpredictable costs, significant data privacy concerns as data is sent to third-party AI models, and its inability to process custom fields in Jira. These factors can hinder its real-world usefulness and scalability.
A significant limitation is that Rovo agents currently cannot process custom fields in Jira. This can severely restrict its utility for teams with established, custom Jira workflows, potentially making it less helpful than expected.
Rovo's pricing uses a confusing "AI credit" system, where a limited number of credits are included in some Atlassian plans. For example, a single agent query can use 10 credits, quickly exhausting a user's monthly allowance, and there's no public price list for buying more, making budgeting difficult and unpredictable.
A major concern is that Rovo sends your company's data, including sensitive information from Jira and Confluence, to third-party AI models from OpenAI and Google. For many organizations, this raises significant data privacy and security questions.
Setting up Rovo can be challenging due to a lack of gradual rollout options, meaning it's often a "big switch" for all users without adequate testing on specific data. This makes it difficult to predict and refine its behavior before full deployment, potentially leading to manual cleanup work.
