Most AI design tools wait for a prompt. This one starts a step earlier: it watches the business numbers, and when one slips, it helps the designer diagnose why in UX terms, then turns that judgement into a design-system-scoped prompt an AI tool can build, consistently, and measured end to end.
The current generation of AI design tools (Figma Make, First Draft, Cursor) is genuinely good at turning a written prompt into a screen. But two things are still left entirely to the person: knowing what to change and why, and keeping every generated screen consistent with the rest of the product. Ask ten designers to prompt the same fix and you get ten different screens.
After twenty years building design systems and governance (from LG's UX Lab to Samsung Card), the interesting problem to me isn't "can AI draw the button." It's this: the senior judgement that decides which button, why, and within which system is exactly the part that never gets written down. So I tried to write it down, as a working system.
Tools react to what a person types. Deciding the screen needs changing at all still depends on someone noticing a number moved.
"Conversion is low" has many causes: urgency, social proof, choice overload. Jumping straight to a pattern skips the diagnosis that makes it the right pattern.
Each AI generation is a fresh guess. Without the design system in the loop, screens drift apart and the system decays.
The system keeps the design system stable and uses AI to move between the numbers and the screen. It doesn't replace the designer: it front-loads the monitoring and the boilerplate, and hands the judgement calls back at exactly the right moments.
A product is an ecosystem. Each stage of the funnel throws off its own business data, and each maps to a moment in the experience that a designer can shape. The framework lays that out end to end: for every stage, which data to watch, which UX principles could explain it, and which design-system components are legitimate responses.
The framework only works because the responses aren't improvised. Each pattern is built from a small, governed set of tokens and components, the same ones the AI tool is told to use. That's what keeps a fix for one metric from breaking the look of everything around it.
If view-to-purchase conversion drops, the lazy move is to make the buy button bigger. But conversion can fall because there's no urgency, because buyers can't see social proof, or because the action simply doesn't stand out. Each cause points to a different design, and only a designer who knows the stream should decide which.
So the system never jumps from number to pattern. It proposes causes drawn from UX principles: Hick's Law, Scarcity, Social Proof, Von Restorff, Jakob's Law, and the designer selects, combines, or writes in one the system didn't suggest. Then, for the chosen cause, it offers directions for how to express it, and again leaves room for the designer's own idea. Judgement stays human at every branch.
Once the cause and direction are set, the system assembles a prompt carrying the diagnosis, the direction, and the exact design tokens to use, the constraints that AI tools need but people usually leave out. It targets whichever tool the team uses: Claude Code, Figma Make, or Cursor. Because the tokens are named, the generated screen stays consistent with everything else.
The prototype makes this whole path live. Move a metric, diagnose it, and the seller screen rebuilds with only the patterns your diagnosis called for, then an impact panel shows the metric being re-measured and the pattern promoted if it holds.
The trigger is a business signal, not a prompt. Design begins where the numbers move.
AI can generate; it can't diagnose intent. The cause-and-direction step keeps the senior call human.
Every generation is scoped to named tokens, so speed never costs consistency.
The metric that triggered the change is the metric that judges it. Patterns earn their place.
The goal was never a machine that designs on its own. It's a system that does the watching and the wiring, and asks the designer to do the one thing only they can: decide what the experience should mean, and why. That's the version of AI-era design work I want to do, and this is me building it, not just talking about it.