A few days ago, I asked AI to generate a landing page for an application I’m currently working on.
First, I prepared the content. Then I wanted to see a reference layout — something that could help me move faster from concept to an interactive prototype.

AI did it almost instantly.

And honestly?
The result was impressive.

There was a hero section, pricing, onboarding, CTAs, gradients, cards, modern layouts — everything looked like a polished startup website from 2026. But after a few minutes, another thought appeared:

“Okay. But how am I supposed to edit this now?”

Because very quickly, I realized that the generated interface was not yet a real project. It was an impressive visual outcome — a ready-made product skeleton that, at first glance, looked almost like a finished landing page. And that was exactly what impressed me the most.

At the same time, as a designer, I immediately saw things I would want to improve:

• content hierarchy,
• spacing,
• section proportions,
• CTA placement,
• layout rhythm,
• the behavior of certain elements within the overall user flow.

This was not a problem with the quality of the generation itself. Quite the opposite — the result was surprisingly good. The real issue appeared when I started exploring how to make adjustments.

And this is where the most interesting part of the entire process begins.

I didn’t want to generate everything from scratch again. The whole idea was different: generate a solid foundation that I could continue refining, improving, and adapting to my own needs as a designer. Something like a “living product sketch” — somewhere between a prototype, a component system, and a working interface.

And that’s when I realized that despite the impressive final result, one critical thing was still missing: control over the project structure.

AI Is Great at Generating. Terrible at Handing Back Control.

This wasn’t an aesthetic problem.
It was a systems problem.

I didn’t know:

• what the sections were called,
• which components had been used,
• which elements were shared,
• what component variants existed,
• what was a standalone module,
• what I could safely change,
• what would break the entire layout.

I could look at the final result.
But I couldn’t comfortably work with it.

And that’s exactly what real product workflows are about.

Designers do not create a single beautiful screen. Designers build systems that later:

• evolve,
• go through testing,
• change based on feedback,
• move into development,
• come back with revisions,
• receive new features,
• go through continuous iterations.

If AI only generates a “beautiful result” without giving control over the structure, it quickly becomes more of a demonstration than an actual working tool.

That’s When I Realized Something Important: Figma Still Makes Sense

In the AI era, many people are trying to skip the design phase entirely.

Prompt → finished app → code → production.

It sounds futuristic.
But real product work is far less romantic.

Because the moment you need to:

• move a CTA,
• adjust spacing,
• redesign onboarding,
• add a new component state,
• improve mobile behavior,
• change content hierarchy,
• run A/B tests,
• connect analytics,
• implement accessibility,
• maintain consistency across screens,

suddenly you realize you still need something very old-fashioned.

Structure.

Naming.

Logic.

Systems.

And this is exactly where Figma wins — not because it is “prettier,” but because it allows teams to maintain control over a product before it becomes code.

The Real AI Workflow Does Not Start With Code

The most reasonable workflow I can imagine today looks very different from most AI demos online.

Not:

Prompt → AI does everything

But:

Figma → component system → design tokens → variants → ready for dev → code generation → designer-driven refinements

That difference is massive.

Because in this model, AI is no longer guessing the structure.
AI works on top of an already defined system.

The designer creates:

• components,
• naming conventions,
• states,
• spacing,
• hierarchy,
• responsive behavior rules.

And AI becomes something much more useful:
not a chaos generator, but an accelerator for an existing workflow.

The Biggest Problem With AI-Generated UI Is Not Technical

It’s the lack of control after generation.

Because even the best AI-generated interface quickly becomes difficult to use if:

• components cannot be edited easily,
• the designer does not understand the structure,
• there is no shared naming system,
• design and code exist in two separate worlds.

And this is where things become particularly interesting.

Technical people often assume the biggest issue is code quality.

It isn’t.

Code can always be improved.

The real problem begins when the designer no longer understands the product they are helping create.

AI Needs a Shared Language Between Design and Development

The more I work with these tools, the more I believe the future does not belong to products that promise:

“AI will do everything.”

The future belongs to environments where:

• designers,
• developers,
• component systems,
• design tokens,
• documentation,
• prototypes,
• and AI

all work on top of the same source of truth.

Because then it becomes possible to:

• generate functional prototypes,
• run higher-quality UX tests,
• iterate faster,
• adjust layouts without developer support,
• edit components by name,
• synchronize design and code almost in real time.

And honestly?

For designers who can already read a bit of CSS or understand frontend structure, this could become a massive shift.

Because suddenly we stop being “people who make screens.”

We start controlling the entire product flow.