UX RESEARCH CASE STUDY
Confirming what I already suspected — rigorously.
Brought into a mid-flight AI training pilot with limited control over participant selection, I documented early risk, set defensible success criteria with stakeholders, and when asked to investigate Engineering metrics (post-pilot) as ROI evidence, did the work anyway so I could provide answers that were concrete and actionable.
Role: Lead, UX Researcher & Strategist
Methods: SME Interviews | Data Investigation | Synthesis
Tools: Jira | GitHub | Code Climate
Outcomes: Strategic Measurement Framework & Recommendations
01 — PROJECT OVERVIEW & CONTEXT
Inherited constraints and a clear-eyed start
My involvement in this project began mid-stream. Engineering leadership had already hand picked groups of participants for the AI for Agile SDLC pilot before I cam on as project lead and researcher. So, the participant pool was set, chosen by leadership, not research criteria, which meant I was working within constraints that weren’t mine to define.
Rather than treat this as a blocker, I worked within those constraints thoughtfully. One of the pilot’s primary objectives