Exploring AI in Agile Reporting: How Atlassian Rovo Enhances Team Efficiency

[Chialing Chien]

As agile teams continue to grow in size and complexity, one of the biggest challenges isn’t just tracking delivery progress — it’s interpreting data fast enough to make timely decisions. Teams spend hours gathering sprint metrics, updating dashboards, and ensuring reports are consistent across projects.

This is where Atlassian Rovo, the new AI-powered assistant, begins to reshape the way we manage information and collaboration.

1. The Shift Toward Conversational Analytics

Traditional reporting workflows often require users to navigate multiple dashboards, filters, and queries. With Rovo, teams can now ask questions in plain English and receive instant, context-aware responses drawn from Jira, Confluence, and Analytics data sources.

For example, a product owner can simply ask:

“Show me all stories at risk for this PI”
or
“Which teams have unresolved dependencies impacting release 25.2?”

Within seconds, Rovo returns results that would typically take minutes or hours to compile manually.

2. Making Metrics Accessible for Everyone

Rovo reduces the dependency on technical analytics experts by allowing any team member to access project insights through natural language.
This democratizes data — helping business stakeholders, scrum masters, and developers collaborate more effectively without waiting for manual reports.

In one of our enterprise use cases, AI-assisted queries helped reduce weekly status meeting preparation time by more than 40%, freeing leaders to focus on value delivery rather than data cleanup.

3. AI as a Catalyst for Continuous Improvement

What makes Rovo powerful isn’t just its ability to answer questions — it’s how it can help identify patterns and anomalies in delivery data.
For instance, by asking,

“Which teams had increased cycle time this quarter?”
Rovo can surface potential bottlenecks before they turn into full-blown delivery delays.

When paired with Atlassian Analytics, this becomes a self-learning loop — where data and AI together improve operational awareness across agile programs.

4. The Human Side of AI Adoption

Adopting AI in agile reporting isn’t just about technology — it’s about building trust and curiosity among users.
We’ve seen the best results when teams treat Rovo as a collaborative assistant rather than an automation replacement.

Training sessions, real-world examples, and internal champions all play a vital role in helping teams embrace this new way of working.

Reflection

AI tools like Atlassian Rovo are not replacing agile roles — they are augmenting human decision-making by simplifying access to insights.
As teams continue to scale, the ability to interpret data conversationally will be key to maintaining agility, transparency, and speed in delivery.

The future of agile reporting isn’t more dashboards — it’s smarter, AI-driven collaboration.

Chialing Chien
Technical Project Manager (Enterprise Agile & Analytics)
Disciplined Agile® Scrum Master | Founder of PupUp LLC
Yonkers, New York