UX Designer
Usability Testing
What You Do Today
Run usability tests with real users — moderated or unmoderated, in-person or remote. You're watching people use your designs, identifying where they struggle, and turning observations into design changes.
AI That Applies
AI-enhanced usability testing that records sessions, tracks eye movement and click patterns, auto-identifies usability issues from behavioral data, and generates highlight reels of key moments.
Technologies
How It Works
The system ingests eye movement and click patterns as its primary data source. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The output — highlight reels of key moments — surfaces in the existing workflow where the practitioner can review and act on it. The test facilitation and interpretation.
What Changes
Session analysis accelerates. The AI identifies that users consistently miss the CTA, that average task completion time doubled on the new flow, and creates a highlight reel of struggle moments for stakeholders.
What Stays
The test facilitation and interpretation. Knowing when a user's confusion is a design problem versus a learning curve, and deciding which findings warrant design changes versus documentation — that's UX judgment.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for usability testing, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long usability testing takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Product or CPO
“What data do we already have that could improve how we handle usability testing?”
They're deciding how AI capabilities show up in the product roadmap
your lead engineer or tech lead
“Who on our team has the deepest experience with usability testing, and what tools are they already using?”
They can tell you what's technically feasible vs. what sounds good in a demo
a product manager at a company that ships AI features
“If we brought in AI tools for usability testing, what would we measure before and after to know it actually helped?”
Their experience with user adoption and expectation management is invaluable
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.