A global enterprise software company helps hundreds of thousands of organizations worldwide manage customer operations. Its platforms cover customer relationship management, service operations, and marketing automation for businesses across financial services, healthcare, manufacturing, and nearly every other major industry. With over 100,000 employees and a product portfolio spanning multiple cloud platforms, it’s one of the most recognized names in enterprise technology.

Inside that scale, the Research & Insights (R&I) organization works to understand what customers need across every product area. Researchers run moderated interviews, analyze survey data, and synthesize findings across multiple product lines. The quality of the work isn’t the problem. What happens to that work is.

For years, the insights from hundreds of research studies remained within the research team. Product managers, designers, and engineers shaping the company’s products made decisions without direct access to the customer evidence that their colleagues had already gathered. The research team had become a bottleneck on its own output.

That tension is what Dovetail was brought in to resolve.

Before Dovetail, the team’s research lived in what one member described as “basically a website with links to decks.” Researchers uploaded finished studies as presentation files. Anyone who needed to understand what the company had already learned about a customer problem had to track down the right researcher, ask them to locate the study, and wait for a summary.

The challenge compounded at scale. The company’s research spans many product lines, each with its own roadmaps, stakeholder groups, and customer bases. A product manager working in one area had no reliable way to know what researchers in another had already discovered about customers they shared. The data existed. The intelligence didn’t travel.

The harder problem was structural. Research had become mediated: to get customer insight, you asked a researcher, waited for a readout, and acted on a summary. The company needed its customer intelligence to behave like a living resource, not a service with a queue attached.

Dovetail replaced the website of links with a structured, searchable workspace. Every research session, whether a video interview, a batch of survey open-ends, or a moderated usability study, now lands in a project where AI analysis surfaces themes and patterns. The work that once required manual tagging in spreadsheets now produces organized insights as the data comes in. Any team member can then query that body of evidence directly with Chat.

AI-powered synthesis is the team’s primary use of Dovetail today. Researchers analyzing video transcripts and qualitative survey responses use it to compress the most time-consuming phase of the research cycle: turning raw observations into findings that other people can act on.

To support that reach, the team organized the workspace around the company’s major product lines, with dedicated Channels for the teams working in each area.

That structure makes the workspace easier to navigate, but speed is only part of the value. What changed more is who the research reaches. Dovetail moves customer intelligence out of static decks and into a workspace that product managers, designers, and other stakeholders can access directly, without waiting for a researcher to surface the right study. The research ops manager once described switching to Dovetail like “going from zero to sixty.”

The most telling signal at this company isn’t only what the research team has built; it’s how the platform has spread without anyone pushing it. While the R&I organization was building out its Dovetail workspace, the company’s go-to-market team, of their own accord, launched a voice-of-customer program on Dovetail as well. Different use case, different team, same conclusion: customer intelligence needed a home everyone could access.

That pattern has repeated across the business. Teams in product engineering, employee experience, and digital marketing have each independently evaluated or adopted Dovetail, drawn by the same core need: a structured place to put what they’re hearing from customers, and a way to surface it to the people making decisions. No central mandate brought them there.

For an organization at this scale, that kind of organic adoption matters. Hundreds of research studies already live in the Dovetail workspace, spanning multiple product lines. As more research flows into that shared database, the value compounds. That’s what a customer intelligence platform does at scale: it turns individual studies into organizational knowledge, and organizational knowledge into faster, better decisions.

The team’s senior product lead frames the requirement plainly: the goal was always a scalable, structured research database—one where “everything that sits on top of that now and into perpetuity has that source of fuel.”

Researchers who once spent their time fielding questions now spend it asking harder ones, with a richer foundation of evidence beneath them.