7월 17일 금요일
7월 17일 금요일
Friday, July 17
FigmaYuhki Yamashita
AI summary focused on the core points of the original article.
In an era where anyone can make things with AI, direction and craft create a bigger difference than speed.
Good situations to read this
When there are more AI prototypes than before, but it feels unclear which direction to choose
When you feel the team is refining the first idea too quickly
When you want to find your product’s differentiation not in features, but in the criteria for judgment and the level of finish
AI quickly turns ideas into reality, but speed alone does not make a good product.
Starting point for product judgment
Before going deep into the first idea, widen the set of options
Do not reach a conclusion based only on abstract 2x2s or wireframes
Turn several directions into something like a real experience and compare them
What to look for in team reviews
Which direction is more convincing
Whether users will actually feel a meaningful difference
Both looking at multiple directions only superficially and going deep on only one direction have limits.
How AI can help
Build different solution directions in parallel
Flesh out each direction all the way to the end-stage experience
Let team members and agents look at the same screen and build reactions together
Points designers should take away
Compare actual user flows instead of abstract discussions
Choose not the “good-looking” proposal, but the “more right” one
AI-generated outputs are plausible from the start, but plausibility is not the same as point of view.
Common failure modes
Accepting the first proposal as is
Not pushing further just because it looks nice
A pattern that is average in a good way becomes the product’s identity
The mindset needed
Choose, question, remove, and refine again
Create an intention that people will remember, rather than just functional completeness
Good teams move quickly, but they carefully decide what to choose and refine it all the way through.
Standards for product teams
Build quickly
Choose intentionally
Obsessively raise the level of finish
Practical application
During reviews, ask “Is this the right answer?” before “How can we make this better?”
Do not settle for the average level of polish in AI outputs
This article is good because it gives a fairly crisp framework for product sensibility in the AI era. Especially for teams using Figma Make or AI prototyping, it works well as a reminder that more output is less important than choosing strong options and polishing them all the way through.