Figma AI: how product design is changing

Actualizado: 2026-05-03

Figma’s AI features are nearly a year old since their Config 2024 presentation, and there is now enough experience to make an honest assessment. They aren’t the first AI features in a design tool (Galileo AI, Uizard and others pushed earlier), but Figma’s dominant market position means its decisions set the direction for the whole profession. After last July’s public stumble with “Make Design” (the feature generating screens too similar to Apple’s weather app, which Figma pulled to rework), the current approach is more sober and more useful.

This isn’t a feature review (official docs cover those better) but a reflection on how work changes and what habits are taking hold. Written from use, not from demo. For the broader collaborative design context, the analysis of Figma for collaborative prototyping remains a reference.

Key takeaways

• The features that have stuck are the quiet ones: mass renaming, visual search, placeholder replacement, prototype generation.
• First Draft is useful for breaking initial block; it rarely produces something usable without significant editing.
• AI lowers the cost of testing variants and makes the designer-product boundary more porous.
• Engineering handoff improves in mechanical ways; the conceptual work remains human.
• Designers who delegate everything to AI lose sensitivity; those who maintain manual work do not.

Features that have stuck

Of everything presented in 2024, some features have found a real place in the flow and others are used less than promised.

The ones that have stuck are the quiet kind:

• Mass layer renaming: from “Rectangle 42” to semantic names in seconds.
• Visual component search by similarity: sketch something freehand and Figma shows similar components in the design system.
• Smart replacement of placeholder text with realistic content.
• Automatic prototype generation linking screens.

These are features making invisible work (the kind designers do many times a day without anyone noticing) faster. Renaming a hundred layers in seconds changes deliverable quality without changing design creativity.

Visual search attacks a classic problem: component libraries grow, nobody remembers what the secondary button with icon was called, and the designer ends up making a slightly different duplicate. Reducing silent duplication in real terms is a benefit that shows up in design system maintenance time.

Placeholder text replacement has a side effect I didn’t expect: designs reviewed with clients are understood better. A wireframe with “Lorem ipsum” forces the client to imagine; one with AI-generated realistic-but-generic text conveys the product’s shape without confusing the discussion with final content. This accelerates conversation because design is discussed, not copy.

Features used less than expected

Some features got a lot of communication push and in practice are used cautiously.

Screen generation from text description (First Draft, the reincarnation of Make Design, now more nuanced) is useful for breaking initial block, but rarely produces something usable without significant editing. Value is in starting the process, not finishing it. Generated screen quality is lower than what a junior designer produces on the same basis, but time to first approximation is much shorter.

The deep problem is that designing a screen is not just producing an image: it’s reflecting user understanding, business constraints, and information architecture. AI sees styles, but not why a form is structured that way, why a certain button is primary, or why this flow has three steps. It generates screens that look right but aren’t always right.

Other assistance features (alternative copy suggestions, icon generation, palette proposals) sit at an intermediate level: occasionally useful, but haven’t become part of the daily flow.

How it affects teamwork

The most interesting effect is what you notice in how product design teams work among themselves and with other functions (product, engineering, research).

First, the cost of trying variants has dropped. Presenting three alternatives instead of one used to be expensive in time; now it’s cheap, which has shifted review culture in some teams toward comparison-based discussion rather than defense of a single proposal. Decisions improve when alternatives are visible.

Second, the designer-product boundary has become more porous. A product manager with basic Figma knowledge can now produce a plausible initial draft without needing a designer for starting. This frees designer hours for higher-value work, but requires new collaboration practices. Teams that have managed it worst are those interpreting AI as substitution; the best-managed, those who redefined when and how the designer contributes.

Third, handoff to engineering has improved in mechanical aspects. Semantic layer names, orderly structure, tokens applied correctly: these things no longer depend on the designer’s discipline under pressure. What remains human work is communicating the why, the decisions document, notes on edge cases.

What remains human work

Areas of product design where AI contributes little:

• User research, interview interpretation, insights synthesis. Material interpretation still requires a researcher who understands the product and user context. AI can summarize, but it can’t detect what remains to be asked.
• Design systems: decisions about which patterns to canonize, which components to maintain, and which tokens to define. Requires medium-term vision and deep knowledge of team needs.
• Stakeholder negotiation, translation of ambiguous requirements into concrete designs, defending decisions to those who don’t share them.
• Aesthetic and brand criterion. Generated proposals are technically correct but lack their own voice. Brands wanting to differentiate still need human creative direction.

Habits taking hold

In teams I work with, some concrete habits have begun normalizing:

• Using First Draft for initial exploration and then redesigning by hand from zero taking only worthy ideas. It’s brief inspiration, not editing the generated.
• Leveraging invisible features (renaming, organization, text replacement) in every review before sharing. A cheap way to raise perceived quality.
• Keeping a design done entirely by hand every so often to keep criteria sharp. Designers delegating everything to AI lose sensitivity, and it shows at three months.
• Documenting decisions in parallel notes to Figma (in Notion or similar) so intent isn’t lost in a file AI can regenerate.

My read

AI integration in Figma is maturing well. The features that stuck are those solving repetitive work without pretending to substitute judgment. Those promising to substitute judgment (generating full screens from description) remain useful only as a starting point, and being that is already enough.

Figma has found a voice in this different from competitors trying to sell “AI that designs for you.” Figma’s voice is closer to “AI saving you boring work so you design better,” and that difference matters. It translates into teams adopting it not losing quality, and that isn’t small in a context where poorly-applied AI produces regressions more often than people count.