AI Planning with the Atlassian Teamwork Collection
Takeaways
- The Atlassian Teamwork Collection gives you AI planning grounded in your team's real work, not generic outputs
- Before Rovo creates anything, it searches your Confluence, Jira, and Loom for what’s already been done.
- Use Confluence Whiteboards to brainstorm and organize with Rovo before the plan takes shape.
- Describe what you need in plain language and get a Confluence brief or Jira work breakdown in seconds.
- Every work item links back to its source, so the plan is traceable from day one
- The Atlassian Teamwork Graph means the system gets smarter as your team does
Good project planning starts with good context. The Atlassian Teamwork Collection gives you Rovo, AI that draws on your team’s actual history, project context, and Teamwork Graph data to draft plans that reflect what your team already knows. From early ideation to a structured work breakdown, this is what AI-native planning actually looks like. It’s accountable and built for real workflows.
How to plan projects with AI using the Teamwork Collection
Step 1: Start with what your team already knows
Before Rovo creates anything, it looks at what your team already has. It searches your Confluence knowledge base, your Jira backlog history, and your Loom recordings to understand the context behind your goals: what decisions have already been made, which approaches have worked before, and your team's patterns.
Ask it to help plan a new feature, and it might surface a Confluence page with decisions from six months ago, a Jira epic that shipped something similar, or a Loom recording where the team debated trade-offs. Research that used to take a week happens in seconds, and your plan starts from what your team actually learned, not guesswork.
Step 2: Brainstorm and organize
With that context in hand, Rovo can help you think through the problem before the plan exists. Rather than facing a blank page, start on a Confluence whiteboard. Add sticky notes with ideas and ask Rovo to generate additional ones, drawing on your team's context to surface ideas relevant to your specific goals. Rovo can also group stickies into themes, visually organizing ideas so your team can decide what resonates, what gets cut, and what direction feels right.
Step 3: From idea to structured plan, in seconds
Once aligned on direction, Rovo can summarize the whiteboard into a Confluence brief, convert the ideas directly into Jira work items, or both, officially turning the team's thinking into a plan.
For teams coming in with a clear idea already, a Rovo prompt works just as fast. Describe the initiative or reference a meeting recording, and Rovo drafts the Confluence brief. The team can comment, refine, and align before anything moves forward. Once everyone is on the same page, Rovo breaks the brief down into Jira work items.
From there, everything stays connected. Every Jira work item links back to its source: the whiteboard, the brief, the prior epics it drew on. The plan is traceable from day one, so anyone joining the project later can understand not just what's being built, but why.
Tip: Orchestrate Rovo agents in Jira to automate parts of the planning process: suggest sprint composition, flag capacity conflicts, and feed retrospective learnings back into the next plan.
Step 4: The compounding advantage
AI-native planning compounds. Every Confluence page your team writes, every Jira work item that gets resolved, every Loom recording that gets made, all feed the Atlassian Teamwork Graph, the connected layer that powers Rovo's understanding of your team's work. Teams that have been using Jira and Confluence for years have a meaningful advantage: years of decisions, patterns, and institutional knowledge that no generic AI tool has access to. The system gets smarter as your team does.
That advantage shows up in real ways. After six months, Rovo's plans reflect your team's patterns: how you scope features, how you sequence dependencies, and who tends to own what. After a year, it's anticipating the same risks your team always hits. After two years, it surfaces past projects that tried similar approaches, including what worked, what didn't, and why. Generic AI starts from scratch every time. Your AI builds on everything your team has already figured out.
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