Research that earns trust,
and gets adopted.

Act I, Setup

[Client sector redacted per NDA] launched AI-assisted clinical support expecting a uniform global rollout. The product tested well in headquarters; the field told a different story.

Act II, Confrontation

In two regions, clinicians quietly worked around the tool. Contextual inquiry revealed the friction wasn't the AI, it was the mismatch with local documentation rituals, hierarchy, and liability norms. Trust was a cultural variable, not a feature flag.

Act III, Resolution

We ran co-design sessions in-market and produced a localized trust playbook: 14 patterns mapping when to surface, soften, or hide AI confidence. Adoption in the resistant regions climbed as the tool began to respect how each clinic actually worked.

The moment

We watched the bounce happen live, a real customer mid-redemption, giving up three taps from success. Starting there, instead of a clean benchmark intro, made the business cost viscerally clear to stakeholders within the first minute.

What we did

A baseline usability benchmark (SUS + task analytics), a heuristic teardown, and three rounds of moderated testing on redesigned flows. Every change was validated against the same metrics the baseline established.

What changed

SUS climbed from 58 to 81, task completion rose 53%, and redemption drop-off fell 47%. Because we measured before and after with the same yardstick, the redesign was unkillable in review, the numbers did the persuading.

Act I, The problem (late 2019)

A Le Pain Quotidien in Brentwood, LA was in trouble. Sales were down, regulars were fading, and shutdown rumors were circling. As Assistant General Manager I could have guessed at fixes, new menu items, discounts, more marketing, but I chose to treat it as a research problem instead.

Act II, The research & action

Over several weeks I conducted direct customer interviews to understand their mental models. Three findings emerged:

1. Quality perception gap. The actual product quality didn't match the "high-quality" mental model customers held of the Le Pain Quotidien brand.

2. Missing personalization. The experience lacked the individualized touches that competitors like Starbucks offered as standard.

3. Menu ceiling. Customers wanted higher-end options the menu didn't carry, and were going elsewhere to find them.

I retrained staff on personalized communication, pitched management with evidence from the interviews, and secured approval for cost-effective localized product changes that closed the quality gap.

Act III, The results

Sales increased 20–30% for three consecutive months. Regional churn dropped 6%. Positive customer feedback surged. The research worked, the location had reversed its decline.

Then COVID-19 forced the closure in early 2020. The restaurant didn't survive the pandemic.

The pivot

This experience didn't end with the building. It proved I could uncover root causes through direct user research, translate insights into business action, and deliver measurable outcomes, core UX research skills I carried into roles with Helping Hands Community, Flyhomes, Winston Francois, Applause, Orbello, and beyond. The restaurant closed, but the methodology survived.

The problem

Home delivery was the program's hardest-working channel and its leakiest. People signed up with good intentions, completed one delivery run, and never came back. Nobody knew exactly why, so I treated retention as a journey problem, not a recruiting problem, and went straight to the volunteers themselves.

The research

I ran in-depth interviews with home-delivery volunteers, walking each one through their full experience, from "I wonder what volunteering here is all about?" to the moment they drove away from the last drop-off. Synthesis turned the transcripts into POV statements, service blueprints, and Crazy Eights ideation, then into a future-state volunteer flow the team could build against.

What volunteers told us

1. Flexibility comes first. Busy people needed control over when they volunteered, where they delivered, and how many boxes they took on. "There's just certain neighborhoods that I'm not comfortable delivering to alone."

2. Know before you commit. Volunteers walked in blind to what the job actually required. "I don't recall being warned about how heavy the boxes are."

3. The job needs tools. Heavy boxes, stairs, towing anxiety, and language barriers made shifts physically and mentally harder than they had to be, especially for volunteers over 55.

4. Volunteering felt lonely. "I think the biggest thing missing is a sense of camaraderie. It's great knowing it's not just you and that you're part of a group."

5. There was no ending. Shifts ended with silence instead of closure or impact.

It was just kind of like you finish and then you drive away. No reporting back. Thought it was a bit odd, it did feel off. I was just like, oh, what should I do?

6. Routing broke trust. "The first time when I put in the link it didn't even show me the stops. And so I'm like, I'm not going to bother with this."

The recommendations

The 18 recommendations mapped each theme to a fix: availability schedules visible to partner organizations, box counts volunteers choose themselves (3 to 10, with time warnings), a four-step explainer carousel and wiki that answer most questions before commitment, dolly-and-partner prompts at sign-up, shared community spaces for volunteers, a "done" checkout that triggers impact stories and recipient thanks, and just-in-time routing with the stop order curated in advance.

The strongest signal in the data was the one the program had never designed for: volunteers stay when they can feel the impact of their work.

When people directly thank you, that makes everything worth it. It gave me the sense of THIS IS WHY.