CASE STUDY · MICROSOFT-SPONSORED CAPSTONE · UW HCDE
What finance teams needed before they'd trust AI
A Microsoft-sponsored capstone: from an open prompt to a validated product definition. I scoped the bet, designed and led the research, and turned one finding into the requirements a four-person team built from. Prototype testing pointed to roughly 30% less documentation overhead.
Preparer
1
Updates workbook, makes changes
2
Adds notes and comments by hand
3
Shares over email or Teams, no standard summary
4
Meets with the reviewer to walk it through
HANDOFF
Reviewer
1
Digs through sheets to find what changed
2
No logic trail or version history
3
Compliance notes scattered across emails and chats
4
Follows up for clarification — again
How SOX documentation actually moves between preparer and reviewer — mapped from fifteen research conversations. This diagram is the project's real hero artifact.
The prompt was wide open: use AI in B2B financial services. I scoped it to Excel + SOX compliance, ran the research program, and one finding — efficiency only counts if you can check it — wrote the requirements for three features. A year later, that finding became the thesis I shipped at LearningPulse.
Scoping was the first design decision
The sponsor prompt gave us everything and nothing: "How might we leverage AI in B2B financial services?" My call was Microsoft Excel + SOX compliance — and the reasoning mattered more than the answer. Excel is finance's default tool, so we'd study real workflows, not hypotheticals. SOX documentation is genuinely painful, so relief would be felt, not imagined. And compliance is trust-critical, so if AI could earn adoption here, the lessons would travel. A good scope makes everything downstream testable.
Default tool → real workflows
Real pain → felt relief
Trust-critical → lessons that travel
Research I designed and led
7 semi-structured interviews (45–60 min) with analysts, risk managers, and auditors — I wrote the protocol and ran the sessions; Riva took notes and pressure-tested my synthesis.
8 exploratory discussions + a 25-thread netnography (r/excel, r/accounting) for unfiltered frustrations and workarounds.
Secondary research on AI adoption in finance to find where the limits actually were.
Ops: I also ran the team's Notion operating system — deliverables, tasks, meeting notes, research artifacts, and a sponsor database Microsoft used to track progress and leave feedback. Alignment was a designed artifact, not a hope.
Efficiency only counts if you can check it
Usability testing’s core finding: participants valued the AI’s speed — but only with the power to review, verify, and tailor every output. Trust and control weren’t polish. They were the adoption gate.
Workbook Summary read as hours saved; Formula Explanation unlocked complex logic without tracing; participants liked that Copilot's comments were visually distinct from human notes.
Every participant wanted to edit AI output before sharing; icons and labels confused more than they charmed; traceback needed precise cell origin — sheet names, location links.
“This would save me hours.”
— Participant, on Workbook SummaryOne finding wrote the requirements
Edit before share
AI output is editable, always
Human vs. AI
Copilot comments visually distinct
Trace to the cell
sheet + location links, not vibes
Plain words
labels over clever icons
1
FINDING
Efficiency only counts if you can check it.
4
REQUIREMENTS
Edit before share · human vs. AI distinct · trace to the cell · plain words
3
FEATURES
Formula Explanation · Trace Document · Workbook Summary
The definition chain: what testing proved, what I specified, what the team built.
Built from the definition — interface design: Christina Sa & Michael Bui.
~30%
less documentation overhead — prototype testing with finance professionals; directional, honestly framed.
Who did what — and what I'd defend
THE TEAM
Interface design — Christina Sa & Michael Bui.
Research support — Riva Li.
ME
Scoping, research protocol and sessions, synthesis, requirements, usability protocol, sponsor management, team ops.
I credit precisely because the question every panel should ask is “what did you do?” This page is built to survive it.
Scoping is a design act.
The Excel + SOX call did more for the outcome than any screen — it made every later decision testable against real workflows and real pain.
Ops is invisible until it isn’t.
A sponsor database sounds unglamorous; a Microsoft team leaving feedback in our system is why alignment never slipped.
The finding outlived the project.
“Review, verify, tailor” became the thesis I shipped at LearningPulse a year later — research that ages into production conviction is the best kind.
Metrics reflect actual project results; the ~30% figure is from moderated prototype testing. Demo shows the validated prototype.