This company builds the hardware and software behind precise, real-world work, helping teams design, measure, and build the physical world around them. It operates across several distinct business units and dozens of markets, with roughly 10,000 employees. The company’s products are built around accuracy, reliability, and trust. Its customer knowledge needed to meet the same standard.

For all that external precision, their own understanding of customers was harder to pin down. Like many organizations its size, its leaders trusted a customer insight most when they heard it firsthand, in a meeting or on a customer visit. Secondhand summaries carried less weight, so the research team’s highest-value move was getting a decision-maker in the same room as a customer, one conversation at a time.

Before Dovetail, the company’s customer knowledge was scattered to the point of being unusable. Interviews and findings lived across personal drives, slide decks, and disconnected tools. One leader called the company’s shared drive “a place where great and important research goes to die.” They had terabytes of source material yet no one could discern what had already been studied.

That fragmentation was expensive. One researcher recalled running 56 fresh interviews to answer a question the company had almost certainly explored before, only because the earlier work wasn’t discoverable. Findings never accumulated; each team started over.

Then AI changed the stakes. As AI adoption spread across the company, some product managers and engineering leads started to wonder whether they needed research at all. Why not just ask an LLM?

The instinct made sense. But a general-purpose model answers with the same confidence whether or not real customer data sits behind it, and teams were beginning to act on answers that only sounded grounded.

For the research team, the danger was concrete: a company this size could move fast in exactly the wrong direction, convinced it was customer-led when it was really guessing. Another place to park files wouldn’t fix that. They needed a single, trustworthy source of customer evidence that teams across the business could actually rely on.

The company made Dovetail the single place its customer data lives, unifying six separate workspaces into one Enterprise platform spanning product, design, and UX teams across continents. SCIM provisioning and PII redaction cleared the security requirements that come with operating at enterprise scale.

Centralizing the data mattered, but only as a foundation. It’s what gives their customer intelligence platform real customer voice to work from, not just generative output. A principal researcher who has tried nearly every tool calls it the best customer information the company has, and the first time all of it is usable in one place.

The Head of Research, who now manages a team rather than running every study themselves, opens Chat to pull cited answers in seconds. “I’d rather use the AI features in Dovetail than Gemini deep research, where we have no idea where the answer’s coming from,” they explain.

Surfacing customer evidence no longer takes a researcher’s time or a scheduled readout. After years of scattered drives and disconnected tools, Dovetail became the company’s system of record for customer knowledge: an AI-native, evidence-based home for everything users tell it.

The change shows up most in how decisions get made. Customer evidence now carries weight because it’s cited, quantified, and tied to the customer’s actual words. Dovetail surfaces how often the same issue appears across customer conversations—the weighting the team used to tally manually—and turns raw interviews into the jobs-to-be-done frameworks product teams plan against.

That workflow is spreading. The questions change—which issues are surfacing most, what evidence supports the decision on the table—but the answer always traces to real customers. As leadership expands AI across the organization, that same trusted evidence is flowing into the tools other teams build with, so the company’s AI ambitions rest on real customer context rather than guesswork.