UI/UX Design · PM SalesForce
PMSF supports internal teams in managing product-related requests such as sampling, quotations, and approvals. Users consistently struggled with the Request Center, leading to high drop-off rates, incorrect submissions, and long onboarding times. I led user-centered design to simplify the workflow and align it with business goals.
Problem Overview
Users didn't know how to start or what information was required, especially for complex requests. The system was built for processes, not people.
Users abandoned submissions mid-process.
Operations team wasted time on clarification.
New users relied on colleagues instead of the system.
Goals & Success Metrics
Improve request accuracy and increase completion rate.
Reduce cognitive load; understand requirements without reading long documentation.
Research & Discovery
Stakeholder interviews with Product and Ops, 1:1 interviews with 8 internal sales users, shadowing real request submissions, and analysis of failed or rejected requests all pointed to the same root cause.
Users think in intent, not process: forms felt intimidating and "too technical."
Starting fresh was painful, so users duplicated past requests instead.
New users leaned on peers rather than trusting the system.
The Insight
How might we help users submit requests accurately without forcing them to understand the system logic? This led to an AI-assisted Request Flow that understands user intent, guides users step by step, and auto-fills and validates data dynamically.
Discovery & Research
PM SalesForce is a third-party tool, and I don't own the roadmap. Research had to separate what reps genuinely needed from what the platform's defaults assumed they needed.
Represents the regional manager who relies on PM SalesForce to track pipeline health and coach the team.
Updates deals between client visits and resents any field that makes her type the same thing twice.
Builds the reports leadership actually reads, and spends real time cleaning up what reps leave incomplete.
Wants a trustworthy forecast number without digging through the platform herself.
Assumption persona used to frame the rep side of the workflow ahead of field interviews.
Used to frame manager-level trust issues with the data, before manager interviews were scheduled.
Assumption persona framing the reporting workflow ahead of ops team interviews.
Assumption persona representing an adjacent team blocked by the platform's access model.
Synthesized from interviews with 8 territory reps and 3 regional managers.
Distinguishes internal users of PM SalesForce from the third-party vendor and adjacent teams.
Raw research notes clustered into themes during a synthesis workshop, surfacing the patterns behind user pain points.
Scored comparison against comparable enterprise platforms across usability, performance, and support dimensions.
Design Response
Instead of a list of request types, users see: "What would you like to request today?", with examples, or the option to upload a relevant document.
AI classifies intent into request type, required modules, and mandatory data, removing the need for users to decide upfront.
AI asks progressive questions only when needed: product category, quantity range, delivery urgency, reducing form fatigue.
The system builds the correct form, pre-fills known data, highlights required fields, and explains why information is needed.
Users review a clean, structured summary before submission, with the ability to re-edit, building confidence in their request.
Journey & Flow
Information architecture wasn't mine to redesign, so the flow work focused on the parts I could influence: how reps move through their day-to-day pipeline tasks.
End-to-end emotional curve across the pipeline update journey, from first touch to task completion, mapped against satisfaction (1–5).
Frontstage actions, backstage operations, and supporting systems mapped against each other to expose handoff gaps.
Primary task flow through the product, including key decision points and drop-off risk areas.
Outcome & Impact
Submission errors reduced significantly.
New users ramped up faster on the system.
Higher trust in the system meant less manual clarification work.
Impact
Scale and ownership across the PM SalesForce (Salesforce CRM) platform, from strategy through to delivery.
Strategy & Prioritization
Every recommendation had to be justified against what the vendor platform could actually support, so each metric below is reported as before → after against the pre-redesign baseline.
Share of support tickets and feedback tied to each theme, before the redesign shipped versus today.
Customer satisfaction climbed from 55% to 86% across 190 post-task survey responses.
Effort score improved from 3.2 to 5.7 out of 7 across 175 responses. Lower effort correlates with higher retention.
Monthly active users against conversion rate, tracked to monitor adoption after each release.
Candidate initiatives scored on impact vs. effort to sequence the roadmap.
North-star and supporting metrics compared against their pre-launch baseline to show whether the redesign is actually working.
Operational metrics compared before and after launch to quantify the business case.
One-page leadership readout summarizing problem, approach, and measurable outcome.
Key Learnings
Good AI UX doesn't feel like AI. It feels like clarity. The most successful part of this project wasn't showcasing AI capabilities, but removing friction from the user's mental model. Shifting from "process-first" to "intent-first" design fundamentally changed how users interacted with the system, reducing cognitive load and building confidence.