Depth became a decision.
I built a way to decide what research each request needed
Six product areas were generating research requests independently, and most arrived as urgent. Treating every request as a full study would have limited capacity and made the level of rigor depend on when a request arrived.
Discovery
Ambiguous or high-impact questions where the problem itself is still unsettled. I lead these directly and prioritize them by business impact and urgency.
Validation
Well-defined questions with a clear decision and known method. A product or design partner can run these using reusable templates, with me providing oversight.
The goal was not to say no to research. It was to match the depth of research to the decision.
I built systems around the bottlenecks.
I made the practice less dependent on my individual time
A behavioral analytics platform supporting a cross-product measurement initiative was discontinued mid-year. Rather than pause the work, I rebuilt the tracking setup on a replacement platform and reconfirmed the existing KPIs with product, keeping the original direction and timeline.
Standing synthesis guidelines
I turned recurring review feedback into written guidelines so the same framing issues did not need to be corrected study by study.
Reusable research formats
I built shared templates for participant tracking, journey maps, planning, and synthesis so new studies could start from a proven structure.
AI-assisted synthesis
I built a repeatable workflow that applies the research standards and business context across studies. The tool supports the work; the research judgment remains mine.
What the model made possible
Six monthsThe increase in capacity came from changing how the work was organized, not from working longer.
Studies sustained, eight completed and two in flight.
Journey-mapping efforts consolidated into one initiative, allowing workflows to be compared directly.
Comparing workflows side by side surfaced handoff gaps that were not visible when they were studied separately.
A strategic measurement initiative continued through a tooling disruption without changing the timeline.
Research became easier to prioritize, scale, and reuse.
From individual studies to a research system
The biggest change was not the number of studies. It was how research capacity was managed across the organization.
Better prioritization
Teams had a clearer way to determine whether a question needed direct researcher involvement or a lighter validation approach.
More reusable research
Shared formats and guidelines reduced repeated work and made findings more consistent across projects.
Research that scaled beyond one study
Multiple research questions could be combined into larger studies when that produced stronger evidence, and research findings could feed into ongoing measurement and design decisions.
The goal was not to make one researcher do the work of a team. It was to build a system that gave one researcher more reach without lowering the quality of the work.
Every number is a claim to verify.
I also challenged the limits of the evidence
During a related study, I re-checked raw session transcripts against an already circulated report and found several task failures that had not been reported. I raised the discrepancy, corrected the results, and the finding was later confirmed during formal review.
I bring the same approach to research decisions: be clear about what the evidence supports, what it does not, and when more research is needed.
Internal product, team, platform, and KPI details are intentionally abstracted. The case study focuses on the research operating approach and organizational impact.