O
Osman Gunes Cizmeci
Guest
When I think back to my first design job, prototyping was the part of the process that consumed most of my nights and weekends. Iβd spend hours moving boxes around in Figma, tweaking flows, and stitching together clickable paths just to show how a single feature might behave.
Now, with AI entering the toolkit, that work looks completely different.
AI isnβt replacing design, but it is replacing the blank page. Instead of staring at an empty frame, I can describe the flow Iβm imagining β βa mobile checkout with three steps and an upsell modalβ β and within seconds I get a working draft.
That draft isnβt perfect. But it gives me a head start. Itβs easier to edit something that exists than to invent it from scratch, and that saves me time I can spend refining interaction details or testing variations with users.
What AI canβt do β and probably wonβt for a long time β is apply judgment. Iβve seen it spit out checkout flows that are technically βfunctionalβ but completely ignore accessibility, or recommendation screens that feel more like manipulative traps than user-centered guidance.
Thatβs where I come in. My job isnβt to rubber-stamp whatever the algorithm gives me. My job is to ask: Does this respect the user? Is it consistent with the brand? Does it actually solve the problem?
AI speeds up the βwhat ifβ stage. But Iβm still responsible for the βshould we?β
One of the unexpected benefits of AI prototyping is that it encourages more exploration. Before, I might only mock up two or three variations because of time pressure. Now I can generate ten, keep the two that feel promising, and discard the rest.
This abundance means I can test more hypotheses earlier. It makes the design process less precious, more playful. Failure costs less β and that makes me bolder.
Another shift is how AI is changing collaboration. Instead of waiting days for me to prepare a polished prototype, I can generate a draft in a meeting and invite feedback right away.
That immediacy helps stakeholders feel like co-creators, not just reviewers. It also shifts the conversation: weβre not debating whether my prototype is βfinishedβ enough, weβre discussing the idea itself.
Still, Iβm careful about how far I let AI into the process. When it comes to fine-tuning motion, writing microcopy, or designing error states, I want human intentionality. Those moments are where users feel whether a product respects them or not.
I donβt want to outsource that responsibility.
I donβt think AI is the future of prototyping. I think itβs the future of starting. Itβs the fast-forward button that gets me to the interesting parts sooner.
But the craft β the empathy, the judgment, the care β thatβs still on us. And honestly, I wouldnβt want it any other way.
Continue reading...
Now, with AI entering the toolkit, that work looks completely different.
From Blank Canvas to Starting Point
AI isnβt replacing design, but it is replacing the blank page. Instead of staring at an empty frame, I can describe the flow Iβm imagining β βa mobile checkout with three steps and an upsell modalβ β and within seconds I get a working draft.
That draft isnβt perfect. But it gives me a head start. Itβs easier to edit something that exists than to invent it from scratch, and that saves me time I can spend refining interaction details or testing variations with users.
The Judgment Layer
What AI canβt do β and probably wonβt for a long time β is apply judgment. Iβve seen it spit out checkout flows that are technically βfunctionalβ but completely ignore accessibility, or recommendation screens that feel more like manipulative traps than user-centered guidance.
Thatβs where I come in. My job isnβt to rubber-stamp whatever the algorithm gives me. My job is to ask: Does this respect the user? Is it consistent with the brand? Does it actually solve the problem?
AI speeds up the βwhat ifβ stage. But Iβm still responsible for the βshould we?β
More Room for Exploration
One of the unexpected benefits of AI prototyping is that it encourages more exploration. Before, I might only mock up two or three variations because of time pressure. Now I can generate ten, keep the two that feel promising, and discard the rest.
This abundance means I can test more hypotheses earlier. It makes the design process less precious, more playful. Failure costs less β and that makes me bolder.
Collaboration in Real Time
Another shift is how AI is changing collaboration. Instead of waiting days for me to prepare a polished prototype, I can generate a draft in a meeting and invite feedback right away.
That immediacy helps stakeholders feel like co-creators, not just reviewers. It also shifts the conversation: weβre not debating whether my prototype is βfinishedβ enough, weβre discussing the idea itself.
Where I Draw the Line
Still, Iβm careful about how far I let AI into the process. When it comes to fine-tuning motion, writing microcopy, or designing error states, I want human intentionality. Those moments are where users feel whether a product respects them or not.
I donβt want to outsource that responsibility.
Looking Ahead
I donβt think AI is the future of prototyping. I think itβs the future of starting. Itβs the fast-forward button that gets me to the interesting parts sooner.
But the craft β the empathy, the judgment, the care β thatβs still on us. And honestly, I wouldnβt want it any other way.
Continue reading...
