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 already well understood inside Atlassian. User research is taken seriously. Atlassian makes customer focus explicit in one of its named company values: “Don’t #@!% the customer.”

The blocker wasn’t a lack of commitment. It was making customer intelligence accessible once a study wrapped.

Findings landed in decks and shared drives—useful in the moment, but harder to find as months passed. New team members relied on word of mouth to understand what had already been learned. Engineers could still build on assumptions that research had already challenged, because the evidence lived somewhere they couldn’t easily reach. Knowledge accumulated, but not in a way teams could reliably return to. Lead Product Designer Martin Jerkovic saw that gap up close.

Dovetail gave Atlassian a way to turn completed studies into reusable customer intelligence—not by generating more research, but by making existing knowledge easier to find, query, and build on. With Chat, anyone can ask a question in plain language and get a cited answer drawn straight from that research.

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 compounded. A theme surfaced in one quarter is no longer trapped in that quarter’s deliverable—it can be queried later, cross-referenced against new data, and built on by teams who weren’t involved in the original work.

The people contributing to that intelligence are no longer only the research team. Neither are the people who benefit from it.

At Atlassian, customer intelligence reaches people across the organization, from the CEO to individual contributors embedded in product teams. Its value becomes especially clear when teams can engage with customer evidence directly, in the context of the work they are already doing.

In one instance, a content designer used Dovetail to share key customer research with a team of engineers. Rather than receiving a summary of what customers had said, the engineers could watch real people interact with the product and see their experiences firsthand.

They did not wait for someone to translate those observations into a list of requirements. They began identifying potential improvements themselves, connecting what they saw in the research with the parts of the product they understood best.

This example shows what broader access to customer intelligence can change. Research does not have to travel through a series of handoffs before it reaches the people shaping the product. Teams can encounter the evidence directly, bring their own expertise to it, and act on what they learn without requiring a researcher to mediate every interaction.

At the scale Atlassian operates, no single team can speak to every user or keep every past finding top of mind. Dovetail brings customer signals from research sessions, usability studies, recorded interviews, and ongoing feedback into a shared platform that teams can return to throughout the development cycle.

When a question or assumption surfaces, teams can first test it against the research Atlassian has already conducted. With Chat, they can ask questions in plain language, receive cited answers, and follow those answers back to the underlying evidence.

That gives teams a clearer starting point. They can see what customers have already said, understand the context behind a finding, and determine whether the existing evidence addresses the question or whether further research is needed.

New fieldwork still has an important role. The difference is that teams do not have to treat every question as though the organization is starting from zero. Existing customer knowledge remains available between studies, helping decisions build on what Atlassian has already learned rather than relying only on the most recent research deliverable.

Speed here is not about skipping research or shipping faster. It comes from shortening the distance between a question and the evidence that can help answer it.