In this module, participants will build a clear foundation for how AI fits into Agile ways of working. First, they will reflect on different Agile activities and identify which ones are creative, judgment-based, or repetitive. This helps them see where AI can realistically support their work without replacing human thinking. Participants will also capture real Jira pain points and get introduced to the Agile AI Suitability Map so they can make better decisions about AI use in Scrum. Next, they will explore how Jira has evolved from a simple tracking tool into an intelligence-enabled platform. Participants will learn what Jira AI and Atlassian Intelligence actually are, how Rovo supports search, chat, and agents, and what prerequisites exist, such as licensing and cloud-only availability. Throughout this discussion, the focus remains on Agile values; AI supports the team, but accountability and decision-making still stay with humans. Scrum Masters continue to own flow, outcomes, and team responsibility.

In this module, participants will learn how AI can streamline Agile work breakdown from the Initiative to the Story level. They’ll first understand why manual decomposition is often difficult, from cognitive overload to missed dependencies, and how AI can help create strong first-draft stories while humans still ensure context, value, and customer impact. Then, through a hands-on Jira lab, participants will use AI Work Breakdown on a real epic, refine the generated stories and subtasks, add acceptance criteria and points, and selectively accept what works. By the end, they’ll see how AI can create a ready-to-plan backlog slice in minutes, while still relying on human judgment for quality and accountability.

In this module, participants will learn how to search and filter work in Jira using plain English instead of complex JQL. They’ll see how Atlassian Intelligence converts natural language into JQL while still allowing full transparency and control. Through hands-on practice, participants will write real-world queries such as blocked issues, workload, deadlines, and personal tasks, then review and refine the AI-generated results. By the end, they’ll understand how natural-language search removes friction, speeds up reporting, and makes Jira more accessible for every team member — not just technical users.

In this module, participants will learn how to use AI to create high-quality user stories that follow the INVEST principles. They’ll revisit what makes a good story, including clear personas, benefits, and testable acceptance criteria, and see how AI can improve clarity, consistency, and completeness. Through a hands-on lab using a real epic, participants will generate and refine user stories, compare before-and-after quality, and produce a backlog that is ready for planning. They will also learn how AI can surface assumptions, suggest edge cases, and improve non-functional details, while teams still apply judgment and context to finalize the work.

In this module, participants will learn how AI can make sprint planning smoother and more confident. They’ll explore how AI helps teams understand workload, predict capacity, and spot risks early, while still leaving final decisions to the people in the room. Through a practical exercise, they’ll use AI to review the backlog, suggest a sprint plan, check dependencies, and compare results with a manual plan. They’ll also get introduced to Jira automation and Rovo agents, and see how simple rules can reduce repetitive work like flagging blocked tasks or creating summaries. By the end, participants will know how to use AI to plan smarter, save time, and keep the team focused on real work.

In this module, participants will learn how to use Rovo Chat like a virtual Scrum Master that can answer real project questions in seconds. They’ll see how it pulls context from tools like Jira and Confluence to summarize status, spot overload, highlight risks, and even create retrospective summaries. They’ll also learn how to ask better questions so the responses are more useful. Finally, participants will get hands-on practice by asking their own project-related queries and reviewing the responses. By the end, they’ll understand how Rovo helps teams save time, stay informed, and reduce the manual effort of tracking project health.

In this module, participants will learn how to use AI in Jira safely, responsibly, and in a way that truly supports the team. They’ll explore simple best practices like starting small, keeping humans in charge, using clean data, and measuring real impact. The module also reinforces governance basics, AI suggestions are drafts, not decisions, and team conversations still matter. Finally, participants will discuss real-world challenges, learn how to handle AI limitations, and ask questions about practical use in their own teams, so they leave confident about using AI the right way.