🤖 Jira + AI: What’s Actually Useful?

[Akankshya Mishra]

AI is everywhere in the Atlassian ecosystem right now, from Rovo and automation to AI-powered workflows and agents.

But beyond the announcements and feature lists, I’m curious about something more practical:

How are Jira and Confluence users actually using AI in their day-to-day work?

Are you using it to:

→ Summarise long Jira issues or project updates?
→ Find information faster?
→ Write stakeholder updates?
→ Create reports or documentation?
→ Automate repetitive tasks?
→ Or solve an entirely different problem?

As I am working in B2B SaaS marketing, I’m especially interested in the gap between what AI can do and what teams are actually finding useful in their daily workflows.

So, community, over to you:

What’s one AI feature, workflow, or use case that has genuinely saved you time?

And if AI hasn't been useful for you yet, what’s stopping it from being useful for you?

[Nikola Perisic]

To me, Rovo is great when it comes to vibe coding simple Forge applications. It doesn't require code knowledge - you just mention the use case and it does everything for you.

[Akankshya Mishra]

That’s an interesting use case!

I like how vibe coding lowers the entry barrier for people who may not have a strong coding background. Being able to describe a use case and quickly turn it into a simple Forge app could make experimentation much easier.

[Mia Tamm _Simpleasyty_]

Hey @Akankshya Mishra one thing I’m finding increasingly important is the quality of the context behind the AI.

Summaries and generated content are useful, but the really interesting use cases start when Jira and Confluence contain structured, consistent information that AI can reason over reliably. Otherwise you can make the AI faster without necessarily making the result better.

[Akankshya Mishra]

I think it’s the quality of the context that gives AI its usefulness. Structured and consistent Jira and Confluence data allows AI to provide meaningful, reliable insights rather than summaries. Otherwise, we’re basically just getting faster answers out of messy data.

[Mia Tamm _Simpleasyty_]

Exactly @Akankshya Mishra. I think the next step is making that context not only structured, but also trustworthy over time.

Things like ownership, review dates, status, source and audit history can make a huge difference once AI starts relying on the data for decisions, not just summaries.

Otherwise, even well-structured data can become confidently outdated.