Accelerating UX Delivery with AI: 40% Faster Research in Fast-Paced Sprints
Tools
Clueify, UX Pilot and QoQo
Focus
Research acceleration and usability optimization
Year
2024-2025
Role
UX/UI Designer
Context
In both Pod1um and Slick Plus, I served as the lead designer across the entire product experience.
Working in fast-paced agile environments with 2-week sprints, I was responsible for delivering an average of 2 product tasks per sprint, for both projects, at the same time—from quick interface iterations to end-to-end flows for new features.
With no dedicated UX research team and limited support for traditional testing methods, I needed a smarter way to accelerate decision-making, validate ideas and maintain design consistency across both platforms.
Challenge
As the only designer leading all product design efforts, I faced the constant pressure of delivering high-impact solutions every sprint, while juggling multiple streams of work. Each sprint required full ownership of tasks—from early-stage discovery to developer handoff.
Time constraints, fast release cycles and asynchronous communication (especially at Slick Plus) made it difficult to conduct real-time user interviews or usability tests.
This meant I had to optimize the way I approached research and ideation, ensuring I could generate strategic, user-centered outputs fast enough to meet the demands of the team—without sacrificing quality or usability.
Strategy
To address this, I integrated AI tools at each stage of the Double Diamond model.
QoQo helped me quickly generate user personas, journey maps, and user flows based on business context and stakeholder input.
UXPilot supported idea generation, offering interface suggestions and inspiration aligned with usability heuristics.
Clueify provided rapid clarity testing with heatmaps, gaze plots, and focal point analysis—making interface decisions more data-driven without waiting for A/B tests or live feedback.
This approach reduced time spent in the discovery and definition phases by up to 40%, helping me meet sprint demands with a clearer vision and stronger design rationale.
Process overview
Discover
Used QoQo to generate and refine personas, flows, and journeys—cutting discovery time from days to hours.Define
Mapped pain points from journeys and flows, validating assumptions with internal team insights. Used AI to structure problem framing quickly and visually.Develop
Created design hypotheses with support from UXPilot to explore multiple UX patterns and flows, especially useful for feature ideation under time pressure.Deliver
Ran Clueify clarity tests to validate design comprehension before handoff. Gaze plots and focal point maps confirmed usability and reduced the need for revisions.Observe
Post-launch, I monitored user behavior using Microsoft Clarity. Weekly reports and session recordings helped me identify real-world friction points and behaviors, feeding back into future sprints.
UX improvements backed by data
I tested multiple screen variations using Clueify to understand user focus and comprehension. For example:
One version had a clarity score of 53%, while a more focused iteration achieved 74%.
Heatmaps and gaze plots clearly showed reduced visual noise and improved guidance in the final design.
Focal point maps validated whether CTAs and key information were being perceived correctly.
Outcome
40% faster
discovery and definition time, reducing UX planning
from ~10 hours to 6 hours per sprint.
21% increase
in clarity scores (from 53% to 74%) for key flows,
measured via Clueify.
Fewer iteration loops
as initial designs were more aligned with user
expectation from the start.
Faster decision-making
and improved confidence in deliveries, even in
asynchronous work setups.
AI tools didn’t replace my UX thinking. They enhanced it.
With smart support, I was able to deliver faster, more confidently and with data to back up design decisions.
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