Here’s a simple, practical workflow I use:
Every designer has faced this:
The UI looks clean.
The screens feel polished.
But something still doesn’t convert.
Users hesitate.
Flows feel confusing.
Feedback sounds vague.
That’s where AI-assisted UI analysis helps.
Here’s how to do it properly:
Step 1: Start With Real Screens
Not Dribbble shots.
Not random concepts.
Use real product screens.
Focus on actual user flows.
– Login
– Onboarding
– Checkout
– Core actions.
Garbage input always gives garbage feedback.
Step 2: Export With Intention
Export screens as images.
PNG or JPG works best.
Keep labels visible.
Avoid half-finished versions.
Clarity matters more than quantity.
Step 3: Clean the Noise
Before uploading, review everything.
Remove unused variations.
Drop old experiments.
Keep only final, connected flows.
Think like a reviewer, not a designer.
Step 4: Ask the Right Questions
Don’t just upload and hope.
Be specific:
“What usability issues do you see?”
“Where might users get confused?”
“What feels heavy or unclear?”
Good prompts unlock good insights.
Step 5: Spot Patterns, Not Opinions
One comment can be ignored.
Repeated feedback is a signal.
Look for:
– Friction points.
– Confusion.
– Hierarchy problems.
Patterns matter more than details.
Step 6: Structure the Feedback
Ask for summaries.
Ask for priority-based issues.
Ask for simple tables or lists.
This turns raw feedback into action.
Step 7: Design With Confidence
Now you’re not guessing.
You’re fixing real problems.
Improve clarity.
Refine flows.
Strengthen hierarchy.
That’s how AI becomes a design partner, not a shortcut.
If you’re still reviewing your UI only by gut feeling,
you’re leaving clarity on the table.
Save this.
Try it on your next screen.
And tell me what surprised you most.