The constraint
Research demand was growing faster than the team could deliver it.
Recruitment could take months. Study setup was repeated by hand. Insights were difficult to reuse. Even strong research arrived too late for some product decisions.
The bottleneck was not research quality. It was throughput.
What I built
I treated research like an operating system, not a queue of projects.
Standing participant panel
Expanded the customer panel and made recruitment a reusable capability instead of a new project every time.
Standard workflows
Created repeatable processes for screening, scheduling, consent, incentives, and study setup.
Shared evidence
Used a research repository and Power BI to make customer evidence easier to find and combine with product data.
Teach-to-Fish
Built training, templates, and guidance so product teams could run appropriate research without waiting for a specialist.
Scale
The system changed which research was possible.
Faster recruitment did more than save time. It allowed research to fit inside product decision windows instead of arriving after the decision.
What I learned
Scaling judgment means designing the system around it.
The same pattern appears in my newer AI evaluation work: high-volume inputs, a triage layer, human judgment where it matters, and tooling that makes the judgment sustainable.
What this demonstrates
Program management, operating-model design, cross-functional enablement, analytics, process automation, and the ability to scale a specialist capability across an organization.