The research team at this global life sciences company makes it their job to understand how their organization works, from the inside. Not its research programs or scientific operations—its own internal machinery: the tools employees depend on, the processes that connect business functions, the systems that ultimately accelerate or slow their scientific work.

Every study they complete adds to a growing body of knowledge about how the organization operates and where it needs to change. Their findings matter to people who haven’t commissioned the work: senior leaders making decisions, colleagues evaluating new systems, teams planning the next cycle.

The intelligence was there. The challenge was making it available—and safe to share.

This enterprise’s accumulated knowledge was fragile. “We’re losing all this research and we cannot leverage it,” the Research Operations Lead said. “We don’t know where it is. People leave the organization.”

A second problem compounded the first: much of the team’s research involved sensitive content, including employee feedback, vendor evaluations, and internal assessments of systems used in scientific operations.

Sharing findings more broadly required removing names, roles, and identifiable details before anyone outside the original project could see them. Done manually, that step created a bottleneck serious enough to limit how much research could circulate at all.

When people left, knowledge left with them. What remained sat behind a compliance process nobody had the bandwidth to run at scale.

The team wanted everyone to feel “comfortable and confident in extracting what they want from Dovetail.” That started with giving its research a single, permanent home.

After Dovetail passed a rigorous third-party security and compliance review, the organization introduced a non-negotiable policy: all research must live in the platform.

Every study requires a transcript and an AI-generated summary at minimum. Research conducted by contractors gets uploaded under the same standard.

Now all three groups work from the same source of truth—R&D, patient and healthcare, and internal operations each contributing to a shared base that no one has to maintain separately. When a researcher leaves, or a project closes, the intelligence lives on. AI Analysis became central to handling the volume, surfacing patterns across studies so researchers can focus on the findings that most need human interpretation.

Senior leadership uses Chat to pose questions directly. When a decision-maker needs input, the team can point them to the data rather than interrupt active projects to produce a summary on demand.

The second problem—sensitivity as a distribution bottleneck—required a different solution. The team upgraded to Enterprise in 2025, and redaction was the primary driver.

Dovetail’s AI-powered redaction works across video, audio, and transcripts in two modes: suggest mode flags potential redactions for a human reviewer to confirm; auto mode applies them automatically as new content arrives. For a team managing a continuous intake of research, removing that manual checkpoint changed what was possible.

With sensitive content handled reliably at ingestion, findings can reach stakeholders without individual review of each piece. The move to Enterprise came down to trust rather than seat count: the ability to handle sensitive content confidently enough to share it broadly across the business.

Within months, AI Agent usage grew from near zero to thousands of queries. Analysts no longer have to navigate project by project to find what they need. They can ask questions across their Dovetail workspace and receive traceable answers drawn from two years of accumulated research.