Jan 8, 2026
Every week I talk to Sydney businesses who've built something with AI tools. The interfaces look incredible. Clean typography, consistent spacing, modern component libraries. And yet their users are bouncing, abandoning forms, calling support about things that should be obvious.
The pattern is clear: AI-generated products have a UX problem. Not a visual design problem - the visuals are often beautiful. The gap is strategic. AI can make things look good. It can't make them work well.
Here's what typically happens. A founder uses an AI coding tool to build an MVP in a weekend. The interface looks polished - modern cards, nice shadows, all the right UI patterns. They launch, get some traffic, and then watch the metrics fall apart. High bounce rates. Form abandonment. Users who sign up but never come back.
The founder's instinct is to make it look better. More polish. Better animations. Cleaner layouts. But the problem was never visual. The problem is that AI generates from patterns, not from understanding.
As a product designer in Sydney, I've audited a dozen of these AI-generated apps in the past six months. The visual execution is usually fine. What's broken is the gap between vibe coding and intentional design - the difference between generating screens and actually solving user problems.
Let's be fair to the tools. AI-generated design handles certain things well:
Visual consistency: Same spacing, same colors, same component styles throughout
Pattern replication: Standard UI patterns like navigation, cards, forms
Responsive layouts: Basic adaptations across screen sizes
Speed: What took weeks now takes hours
But here's what AI can't do. It can't watch a user hesitate. It can't notice that someone keeps scrolling past the CTA because they're looking for social proof first. It can't understand that your insurance customers need reassurance at step 3 because that's where anxiety peaks. It can't map the emotional journey from "I think I need this" to "I trust you enough to buy."
When I worked on complex insurance application flows, the visual design was straightforward. The hard work was understanding where users got confused, what information they needed at each step, and how to reduce the cognitive load of a 12-field form. AI generates the form. It doesn't know why field order matters.
There are five specific failure points I see repeatedly:
Error handling. AI generates happy paths beautifully. But what happens when someone enters an invalid email? When their session expires? When the payment fails? These edge cases are where users actually need help, and AI-generated designs rarely handle them well.
Information architecture. AI can create pages. It struggles to create coherent navigation that matches how users think about your product category. The hierarchy looks logical to the AI - it's not logical to your customers.
Trust-building sequences. For complex purchases, users need to build confidence progressively. AI doesn't understand that showing pricing before establishing value kills conversions. It doesn't know that testimonials matter more at checkout than on the homepage.
Form design. This is where the gap shows most clearly. AI generates forms with all the fields you asked for. A UX designer asks: do we need all these fields? In what order? With what validation? What happens when users make mistakes? The form looks fine. The completion rate is terrible.
Accessibility and inclusion. AI-generated designs often look accessible - good color contrast, readable fonts. But actual accessibility goes deeper: focus states, screen reader compatibility, keyboard navigation, error announcements. Looking accessible and being accessible are different things.
This is what makes designers valuable when AI does the visual work: the strategic layer that AI can't touch.
User research. Understanding who your users actually are, what they're trying to accomplish, and what gets in their way. AI generates from data patterns. Humans uncover why those patterns exist.
Journey mapping. Seeing the full picture of how someone discovers your product, evaluates it, commits to it, and (hopefully) recommends it. AI sees screens. Humans see stories.
Pain point discovery. The most valuable UX improvements come from identifying problems users can't articulate. They say "this is confusing." The designer figures out why and what to do about it.
Business alignment. Balancing what users want with what the business needs. AI doesn't know your conversion goals, your support costs, or your competitive positioning.
This is why I built LucidUX - to help founders with AI-generated products get the strategic UX layer they're missing. Not to replace what they've built, but to make it actually work for users.
If you've built something with AI tools and the metrics aren't where you expected, here's where to start:
Watch real users. Not in your head - actual screen recordings or user tests. The gap between what you think users do and what they actually do is always bigger than you expect.
Map the journey. Walk through every step from landing page to completed action. Where do people drop off? What questions aren't you answering?
Fix the forms first. Forms are usually the biggest conversion killer. Simplify fields, improve error handling, reduce friction.
Test your edge cases. What happens when things go wrong? Those moments define whether users trust you.
Get outside perspective. You're too close to see what's broken. Fresh eyes find problems faster.
The AI-generated design isn't the problem. The missing UX strategy is. And that's a problem worth solving, because underneath those beautiful interfaces might be a product that actually works.
If you're looking for expert UX validation for AI-generated designs, that's exactly what I help Sydney businesses with. But whether you hire help or do it yourself, the work is the same: close the gap between looking good and working well.
Gabriel Hidalgo is a freelance UX/UI and Product Designer based in Sydney, Australia with over 8 years of experience designing digital products for startups, scale-ups, and enterprise clients. He has worked with companies including CommBank, Qantas, and numerous Sydney-based startups, specializing in complex financial interfaces, mobile app design, and AI-integrated products.
Gabriel focuses on helping businesses turn user research into revenue-driving design decisions. Learn more about his approach or connect on LinkedIn.
