Designing at the Speed of Intelligence: How I Scaled a Team to Build AI-Native Products and 10x Our Impact
Driving measurable impact
Rethinking the Design-to-Code Workflow
When I first started exploring generative AI in design, it wasn’t just about the tools—it was about the opportunity.
What if we could go from design to production code in minutes, not weeks? What would that mean for speed, quality, and product-market fit?
Today, I lead a design team that does exactly that—turning Figma files into front-end code in minutes, accelerating prototyping, reducing development friction, and building smarter experiences powered by AI. We no longer rely solely on static mockups or spec-heavy handoffs. Instead, designers prototype interactions and directly export working code for review, drastically shortening the feedback loop.
From Weeks to Minutes: Our Velocity Leap
Designers used to mock up interfaces and toss them over the wall to engineering. It created lag, miscommunication, and inefficiency. But with AI tools like Locofy and Builder, our team began generating production-ready code directly from Figma. Suddenly, ideas went from prototype to live UI in a matter of minutes.
We increased our design-to-code velocity by up to 90%, enabling us to ship faster and test sooner. For example, a checkout redesign that previously took 3 weeks from design finalization to engineering delivery was completed in just 3 days—including development-ready assets and interactions.
Raising the Bar on Quality
Speed was only half the story. Quality improved just as dramatically. We went from averaging over 40 bugs per release in QA to almost zero.
Before integrating AI and training our team on technical fluency, our QA process would routinely uncover inconsistencies in padding, accessibility violations, or performance slowdowns due to inefficient components. Now, designers preemptively address those issues during design and code export. We’ve also standardized accessibility testing into our design system, making inclusive design second nature.
Teaching Designers to Speak Code
That’s why I made a strategic decision: every designer on my team would learn the fundamentals of HTML and CSS.
Not to become engineers, but to develop technical fluency. That meant they could:
Spot problems in AI-generated code, such as improper use of containers or excessive inline styles
Refactor layout structures for responsiveness
Partner more effectively with developers by speaking their language
One designer recently identified an issue with excessive DOM nesting in a complex product tile component. By flattening the structure in Figma and generating cleaner code, they helped reduce page load time and improve lighthouse scores.
Real-Time Debugging, Real Trust
As our team gained fluency, our delivery quality rose alongside our speed. Designers were debugging issues before handoff. Engineering began trusting design more deeply. We closed the loop and created a new kind of collaboration—one where design moves at the speed of thought, but with the integrity of solid code behind it.
We introduced async review sessions where designers walk through their exported code with engineers, flagging areas that might need refinement. This collaborative inspection has saved us hours of downstream fixes and fostered deep cross-functional trust.
Designing For AI, Not Just With It
We didn’t stop at code generation. We began exploring how AI would shape the end user experience—not just the design workflow.
We built interfaces that anticipate user needs, like dynamic forms that pre-fill based on user history or search components that recommend results before typing. We also embedded guardrails for AI outputs—for instance, displaying reasoning behind automated suggestions or offering manual overrides when confidence scores dip.
These experiences required us to think beyond traditional UI. We introduced behavioral states in our design system, supporting patterns like confirmation loops, confidence thresholds, and explainability tooltips.
A 95% Reduction in Our Core Task
One of our biggest breakthroughs came when we redesigned our product around AI-powered automation. By integrating predictive logic and workflow intelligence, we enabled users to create automated purchase orders based on inventory signals and business rules.
Previously, users would manually generate dozens of POs per day—a time-consuming task prone to human error. With our new AI-driven flow, users simply confirm or adjust suggested orders, cutting effort by 95%.
The impact went beyond UX. Sales teams reported higher order accuracy. Operations saw faster cycle times. And user satisfaction scores for that feature rose by 40%
Leading with Clarity and Culture
Behind it all is a leadership model rooted in clarity, accountability, and people-first culture. I see managers as builders of high-performance teams.
We:
- Set clear goals and outcomes tied to business results
- Provide 1:1 coaching that focuses on both hard and soft skills
- Create feedback-rich environments with quarterly retros and peer reviews
- Maintain hiring standards that prioritize craft, curiosity, and culture fit
One example: We revamped our onboarding program to include code walkthroughs, shadowing sessions with engineers, and peer mentorship—resulting in 30% faster ramp-up time for new hires.
Final Thought: Talent Still Wins
AI has changed everything. But the real differentiator isn’t the toolset—it’s the mindset.
Designers who understand AI will be more valuable than ever. Design leaders who know how to build, stretch, and align those designers? Unstoppable.
By blending cutting-edge tech with structured talent development, I’ve helped create a design team that moves faster than ever—not just building interfaces, but shaping the future of intelligent digital products.
If you’re navigating the AI transition, or building a team that can thrive in it, this is the blueprint. Let’s build what’s next.