Atlassian builds software that powers how teams work. With products like Jira and Confluence used by hundreds of thousands of organizations globally, the stakes of every product decision are high—and the cost of guessing is higher.

That pressure was well understood inside Atlassian. Research was a priority, not an afterthought. Teams invested in understanding their customers. The problem was what happened to that understanding once a study was done.

Findings landed in decks and shared drives, organized by the team that ran them—useful in the moment, but progressively harder to access as months passed. New team members absorbed what their predecessors had learned through word of mouth. Engineers sometimes built on assumptions that research had already challenged, because that research existed somewhere they couldn’t easily get to. Knowledge accumulated. It just didn’t accumulate in a way teams could reliably return to.

Lead Product Designer Martin Jerkovic saw that gap up close.

The gap Martin saw wasn’t incidental. At Atlassian’s scale, it was structural: research produced by one team couldn’t easily serve another. The problem wasn’t a shortage of studies—it was the absence of infrastructure to make the knowledge in them compound.

Dovetail changed what research could do once a study was complete. Not by generating more of it, but by making existing knowledge reusable.

Customer signals from research sessions, usability studies, recorded interviews, and ongoing feedback were centralized into a single layer every team could query. Instead of a study ending when the researcher presented their findings, it became a resource that stayed useful over time. A theme surfaced in one quarter was no longer trapped in that quarter’s deliverable. It could be queried later, cross-referenced against new data, and built on by teams that weren’t involved in the original work.

The people contributing to that intelligence—and benefiting from it—are no longer exclusively researchers.

The impact of that shared foundation shows up in unexpected places. A content designer at Atlassian used Dovetail to share key customer research with a team of engineers. Watching real customers interact with the product firsthand, the engineers didn’t wait to be asked. They started identifying improvements on their own.

That’s what unrestricted intelligence looks like in practice: customer understanding that drives action without a researcher in the room.

With AI-powered analysis in Dovetail, Atlassian’s teams collaborate directly on findings, identify themes across large bodies of feedback, and move from raw data to shared understanding faster than manual analysis allows.

At the scale Atlassian operates, no team can speak directly with every user. With Dovetail, they don’t have to—because that knowledge is already there. Customer signals from research sessions, usability studies, and ongoing feedback are centralized in one platform, giving every team a continuous, up-to-date view of the customer at any point in the development cycle.

Product discussions aren’t anchored to the last study that shipped. Speed here isn’t about shipping faster. It’s about closing the gap between a question and an answer.

More than 160 designers and researchers now use Dovetail at Atlassian. Across design and product, teams have created and shared 1,700+ insights and surfaced more than 40,000 highlights from customer research. The result is a customer intelligence layer that keeps knowledge moving after each study ends—so teams can return to what customers said, connect it to new questions, and make decisions grounded in evidence already at hand.