One of the world’s four largest professional services networks employs more than 460,000 professionals across audit, consulting, tax, and advisory services in every major market, and nine in ten of the world’s largest companies rely on some part of it. Its competitive edge rests on one thing: the quality of the insight its practitioners bring into the room.

Research is not a supporting function here—it is part of what clients are buying: the evidence behind the recommendation. When a partner presents strategic recommendations to a client’s C-suite, the intelligence behind those recommendations has to be rigorous, traceable, and fast, because unlike a product company that can iterate after release, the firm’s deliverable is the analysis itself. By the time recommendations reach the boardroom, the evidence behind them has to be clear, defensible, and ready to withstand scrutiny.

One of those practices is Customer Strategy—a consulting group that helps clients understand and act on their customers—and for that practice, research operations were more than an internal workflow. The team needed a way to turn client research into insight practitioners could trust, reuse, and bring into high-stakes client conversations.

As the practice expanded across onshore, nearshore, and offshore teams, that need became harder to meet through individual tools and local ways of working.

Running client research across a distributed consulting practice creates a predictable problem: a practitioner in one city closes out an engagement and files their notes, a nearshore colleague picks up a related client a few months later, and an offshore analyst synthesizes findings for the final deliverable. They may collaborate on the engagement, but not in a shared research environment, and so the intelligence rarely compounds.

Each project started from scratch—practitioners had their own methods for capturing research, transcripts from client interviews sat on individual drives, and findings from one engagement rarely surfaced in the next. Teams spent hours recreating context that a colleague on another continent had already established.

For a firm whose competitive advantage is knowing more than everyone else in the room, that liability mattered. Intelligence accumulated through individual practitioners was evaporating after each engagement, rather than building into something the whole team could draw from.

The practice needed a single home for client research that would outlast the engagement that produced it—structured enough to search, flexible enough to work across regions, and trustworthy enough to meet the firm’s data governance requirements. The enablement lead who would go on to manage the platform for the next five years evaluated the options and made the call: Dovetail was built for exactly this problem.

Dovetail is purpose-built for research operations at scale—it handles the volume of transcripts, workshop notes, and interview recordings that client delivery generates, and its tagging and synthesis tools let practitioners turn raw data into findings that hold meaning beyond the engagement. Its sharing model is flexible enough to accommodate not just internal teams across three regions, but eventually clients themselves.

The Customer Strategy practice adopted Dovetail as its shared research environment. What it built from there was something more durable than a shared folder: a living repository of client intelligence that practitioners could search, extend, and reuse across every new engagement.

The firm has since stood up a dedicated Customer Delivery Transformation team, charged with making the delivery engine more efficient and productive. For client research and intelligence workflows, that team has standardized work in Dovetail: practitioners across the three-region team now use one workspace to capture, tag, synthesize, search, and share research from every client engagement.

The practical shift is immediate: when a new engagement begins, practitioners search existing research before starting fresh, and when a consultant needs to understand how clients in a particular sector have responded to a recurring challenge, they can query structured findings from previous engagements directly.

AI Analysis is increasingly important to the team’s efficiency goals—client research typically involves dense interview transcripts, workshop notes, and survey responses, and AI-powered synthesis reduces the time practitioners spend organizing raw data and accelerates the path to the insights clients depend on.

What started in the Customer Strategy practice is expanding—other parts of the firm, including Human Capital and the Strategy practice, have begun to take notice, and expanding Dovetail’s reach across these areas is an active priority for the Customer Delivery Transformation team.

The more significant development is external: the firm has begun sharing structured research outputs directly with clients by configuring folder-level permissions in Dovetail, allowing clients to review findings as viewers within the platform itself. Dovetail has become a customer intelligence platform in the most practical sense: the place where the firm organizes research from client engagements, then shares structured intelligence back with clients.

Internally, practitioners have pointed to the automation and efficiency Dovetail adds to client delivery work, and they have started positioning it to their own clients as a standard for how customer intelligence should be organized and shared.

For a network of this scale, that is a meaningful shift: from treating research as a byproduct of individual engagement work to treating it as a shared asset that compounds over time. After five years and three continents, the platform that started in one practice now helps define how the firm captures, shares, and delivers customer intelligence.