AI prototyping in 2025

Something profound is happening in UX. The time it takes from idea to design to code is getting shorter, massively. With AI prototyping, you can spin up several variations of an idea—no heavy coding or designing work required—and put these versions in front of users and stakeholders in hours or minutes. The potential? A huge change in how we design, test, and revise products.

Lowering the barrier to prototyping

• AI code generators reduce the need for developer resources early on. Now, non-technical team members—product managers, marketers, you name it—can piece together an interactive demo with minimum effort. This sounds like a dream come true for experimentation, and it indeed offers loads of potential. But it also raises the stakes: if you (or any colleague!) can make multiple prototypes a week, how do you ensure user centricity? By generating rather than coding or designing a prototype, not every element, image or copy will be placed there because someone thought deeply about why a user might need it in the first place.

When lines between ‘prototype’ and ‘live product’ start to blur

Because AI-driven tools can generate surprising amounts of detail, it’s easy to jump from prototype to production mode. Yet code produced by AI is often riddled with corner-case bugs or follow standard patterns that don’t match your brand. Relying on AI generation alone leads to half-baked solutions going live. Today these tools allow us to explore possibilities, but involve developers and thorough testing if you plan to roll out a real product.

The tools enabling AI prototyping

The landscape is expanding fast, but most tools fall into three main categories, each serving different needs. Collin Matthews has put together a great article and course on AI prototyping tools, breaking down their strengths and best use cases.

• Chat-Based Code Generators (ChatGPT, Claude) – These are great for generating small UI elements, standalone pages, and quick feature mockups. Designers and product managers can describe what they need, and the AI produces working code almost instantly. However, they struggle with multi-page flows and maintaining context, meaning they work best for isolated experiments rather than complete applications.
  
  

• Cloud AI Development Platforms (Replit, Bolt, v0.dev) – These platforms don’t just generate code; they build, host, and deploy full prototypes on demand. They’re better suited for functional MVPs, multi-screen apps, or internal tools that need a backend. However, AI-generated code often requires refinement, and the output can be inconsistent—so you should still review and adjust before pushing anything live.

• Coding Assistants (GitHub Copilot, Windsurf, Cursor) – These tools integrate directly into code editors and help modify existing applications, generate feature implementations, or refactor messy code. Developers can speed up iteration cycles, but these tools assume some coding knowledge. They’re not built for standalone prototyping but are valuable when tweaking AI-generated designs or refining an existing product.
  
  

• Design AI prototyping (Miro Prototyping, Figma AI) - Though still in testing or Beta, established names like Figma and Miro are rushing to chime in on the AI prototyping wave. Those who still remember Invision will know that having a large user base alone no longer equals staying on top. Although we still have to experience these tools, they are testimony that AI prototyping is popping up in many places.

While they take different forms, the common thread is clear: these tools are making prototyping more accessible across disciplines. Whether in the hands of a UX designer, a product manager, or a developer, they shorten the path from idea to working prototype. The implication? The barrier to testing real interactions is lower than ever, shifting the focus from mainly building prototypes to making sure they are tested, refined, and aligned with real user needs before they evolve into final products.

Changing roles in the product development lifecycle

As AI reshapes prototyping, the roles of UX professionals, product teams, and developers are evolving. The ability to generate and test multiple prototypes in parallel doesn’t just change how fast we work—it changes what we focus on. Here’s what that means for key roles:

• Designers: now have the power to create complete design ideas without manually arranging layouts or relying on developers for early prototypes. But with great speed comes a new challenge: more iterations demand faster user testing cycles. Because there's a risk: relying too much on AI-generated patterns without verifying whether they meet real user needs. Designers must integrate user testing even more, ensuring that rapid builds don’t lead to rushed decisions.
  
  

• Product managers: can now collect evidence across multiple directions before committing to a final product vision. But this creates a new problem: too much feedback, too little time to process it effectively. Without a clear system for filtering results from user testing, decision-making can become reactive instead of strategic. AI may help generate prototypes faster, but it’s up to PMs to ensure the best one gets built, not just the fastest one.

• Developers: AI-assisted tools remove much of the repetitive setup work, shifting focus toward refining and improving AI-generated code. But faster prototyping doesn’t mean skipping key steps. AI-generated components often require debugging, optimization, and security checks before they’re production-ready. The new challenge for developers? Ensuring AI-built prototypes don’t make it to production with overlooked flaws.

• Researchers: perhaps the biggest impact is on UX research. More prototypes mean more user testing, more feedback, and more data to analyze. Research teams must move from occasional testing to continuous testing cycles, where multiple variations are evaluated in parallel. The challenge? Avoiding “research debt”—when too much unstructured feedback slows down decision-making instead of clarifying it. Strong synthesis and prioritization skills will be essential, perhaps offloading some of early research to colleagues.
  

  
  

AI prototyping lets anyone generate ideas in seconds, transforming collaboration in product development.

User testing is bound to become an even bigger bottleneck

When you can generate concepts so easily, you’ll likely need more user testing. If you previously tested a couple of concepts a month, you might be looking at five or six in the same timeframe now. A platform like Userlabs or an in-house research solution can help gather quick insights and guide faster iteration cycles, but you still need to keep track of it all.

• More prototypes, more data
  Each new idea demands testing. The challenge is ensuring you have the time and structure to verify what’s working and what isn’t.
  
  

• Staying grounded
  AI might handle the grunt work of building layouts, but it can’t read your users’ minds. The designer’s responsibility—knowing the audience and shaping an experience that fits—is still front and center. That’s why platforms like Userlabs help testing scale by accurately simulating users based on user data like interviews, predicting feedback and behavior.

Speed vs. strategy

A quicker design cycle doesn’t replace the fundamentals of empathy, clarity, and problem-solving. In fact, it makes them more important. The real potential of AI prototyping is found when you focus on people’s needs: generating multiple versions based on hypotheses, testing them thoroughly, and refining based on actual insights and critical thinking. If you skip that part, you’re just churning out prototypes without a purpose.

Looking ahead

Some foresee a day when anyone can propose an idea on Monday, confirm it with users by Wednesday, and push a working feature by Friday. It might sound like a stretch, but we’re already seeing glimpses of that speed in teams that embrace AI-assisted workflows. For better or worse, it raises the bar for everyone: design, development, and research all need to keep pace, ensuring that fast prototypes don’t lead to sloppy products.