Product teams ask me this question constantly: What is the future of the product cycle with AI?

I’m Diane, founder of The Design Project. I’ve designed for Crocs and Mercedes-Benz, been the solo designer for five startups, and today my team and I work with over 50 B2B SaaS companies — including teams acquired by Slack, Nvidia, and even MrBeast.

My focus is accelerating design-to-development workflows using AI. Right now, I take my Figma designs, build front-end code with Claude Code and Cursor, and deliver directly into our customers’ repo.

In this post, I’ll break down how this workflow works, why it’s the only AI prototyping method I recommend, and the most common questions teams ask when implementing it.

Why Figma → Claude Code → Cursor Is the Future

I’ve tested everything — Figma Make, v0, Lovable, all of them.

My recommendation:

➡️ Figma → Claude Code inside Cursor → direct codebase integration

Why?

Because Claude Code is simply better. It eliminates the middle layer and generates clean front-end code directly into the repo your engineers already work in.

This is AI augmenting your product team, not reinventing the workflow.

Step 1: Give Your Product Team Repo Access

Designers, PMs, and anyone involved in product delivery should have direct access to the repo. This is often scary for devs, who say:

“I don’t want my designer messing up the code.”

Valid.

The solution:

✔️ Create a dedicated product-team branch

✔️ Designers and PMs work safely in that branch

✔️ Devs keep peace of mind knowing nothing breaks

This single change unlocks everything.

Step 2: Connect Cursor, Claude Code, and Your Figma MCP

Once access is set, here’s the basic setup:

• Cursor running your project

• Claude Code installed

• Figma MCP connected so Cursor can read your components and styles

From there, the workflow becomes incredibly powerful.

Example: Building a Sidebar with AI

In this example, I have a Figma file and a simple UI that needs an “Add Context” button and a right-side sliding sidebar.

I prompt Claude Code with:

“Create a button that matches this one and place it next to the existing button.”

It generates the component, styled correctly, placed correctly.

Next prompt:

“Based on my Figma selection, create a sidebar that slides out from the right when clicking the Add Context button. Match all fonts and colors. Ensure every component state is functional. Use the codebase’s existing language and patterns.”

Claude Code builds the entire sidebar — animations, styling, layout, functionality. It’s not perfect, but it’s close enough that light tweaks finish the job.

This is where your design system becomes critical. Once consistent patterns exist, AI can replicate components flawlessly.

Step 3: Commit the Code

When the feature looks good:

• I hit Commit

• Cursor pushes the branch to GitHub

• Devs review, optimize, and merge

Designers ship. Engineers refine. Everyone wins.

Common Questions from Founders, PMs, and Heads of Product

1. How “done” does the design need to be before giving it to AI?

80–90% is perfect.

It doesn’t need:

• Interactions

• Micro-animations

• Pixel-perfect polish

It does need:

• The right components

• A clear layout

• Alignment with your design system

AI can build interactions, structure, and states — but it can’t read your mind. Give it enough clarity to generate something meaningful.

2. What does iteration look like after the first AI build?

Two options:

• Continue iterating inside Claude Code + Cursor

• Or go back to Figma and redesign, then prompt again

Most teams stay in Cursor once a feature begins. It’s faster and keeps code + design aligned.

Just know: there is a learning curve. Stick with it.

3. Can I trust the AI-generated code?

For front-end? Yes — with review.

Claude Code produces high-quality, framework-consistent front-end code. But engineers should still review PRs for:

• Performance

• Accessibility

• Security

• Long-term maintainability

Back-end logic should still be handled by engineers.

4. Does this replace front-end developers? Are designers just pressing buttons?

No and no.

Designers still:

• Understand UX deeply

• Map flows

• Create the Figma designs

• Direct the AI with clear prompts

• Ensure consistency with the design system

AI is not the brain — it’s the execution engine.

Designers who learn this workflow simply gain superpowers.

Final Thoughts

This is the future of product workflow:

• Designers move from mockups → shipping real, review-ready code

• PMs test ideas earlier and iterate faster

• Engineers focus on architecture, logic, performance, and scaling

• AI accelerates the entire cycle by eliminating repetitive front-end build work

If you want more insights on how product teams can integrate AI into their workflows, subscribe or reach out — I love sharing the systems that are working right now across 50+ SaaS teams.