AI INNOVATION
My Role
Senior UX Designer — Led AI prototyping initiatives, evaluated and tested AI tooling, developed plugins and workflows, mentored teams, and established knowledge-sharing hubs.
Platforms
Figma · Figma Make · Builder io · Replit · Lovable · Confluence · Figma Code Connect · Figma MCP
Year
2025
The challenge
When I joined, AI initiatives around design-to-code and prototyping were not being taken seriously. Teams lacked guidance on how to adopt AI tools effectively, which led to missed opportunities in reducing prototyping cycles, aligning design-to-dev handoff, and accelerating validation.
The opportunity
I saw an opening to explore how AI could reshape our workflows - faster prototyping for Product Managers, seamless Figma-to-Code structures for Developers, and a reduced frustration curve for Designers. By leveraging tools like Figma Make and Figma MCP, I envisioned a workflow where prototypes weren’t just clickable visuals but production-aware design assets.
The results
AI UX innovation established: Built the first internal AI prototyping framework and tooling workflow. Executive visibility: Presented AI demo sessions directly to the CEO twice in 8months of my tenure and senior stakeholders. Reduced cycle time: Enabled PMs to validate with customers faster, shortening the sales cycle. Improved handoff: Designed Figma-to-Code workflows using Code Connect + MCP, ensuring dev-ready outputs aligned with our design system. Knowledge culture: Authored Confluence knowledge hubs and tutorials; mentored peers to make AI adoption smoother and less intimidating.
Work in Progress
Design Principles
Curiosity First - explore widely, test deeply, and learn fast.
Scalability - AI tools must fit into design systems, not break them.
Clarity & Enablement - documentation and tutorials empower others to succeed.
Faster Validation - prototype to customer testing in hours, not weeks.
NN/g Heuristics Applied
Match between system and real world (AI outputs aligned with design system standards).
Consistency and standards (forcing AI outputs to respect design tokens and system styles).
Error prevention (structured tutorials reduced adoption errors).
Recognition rather than recall (Figma Code Connect made component-code relationships explicit).
Flexibility and efficiency of use (PMs and devs got faster paths for prototyping & handoff).
Help and documentation (Confluence hubs ensured accessible guidance).
Deep Dive
Context: AI was Untapped
When I joined, AI initiatives in design were more “buzzword” than practice. No one had taken ownership, and teams often felt skeptical about its practical use. I saw this as both a challenge and a chance to drive innovation.
Exploring the Landscape
I began by exploring the landscape - testing Builder io, Replit, Lovable, and Figma Make. Instead of waiting for direction, I created live demos for stakeholders, showcasing not just how tools worked, but how they could tangibly shorten cycles between prototyping, validation, and production. These sessions built executive confidence, with the CEO himself I have done 2 demos to showcase him the power of AI if driven correctly.
Building the Workflow
Next, I developed structured workflows around Figma Code Connect and Figma MCP:
Designers could map Figma components directly to code.
Developers could rely on IDEs generating consistent, context-aware code.
AI copilots had documentation references to reduce “hallucination” and enforce standards.
Enabling the Teams
This wasn’t just about tools - it was about creating a culture of enablement. I built Confluence knowledge hubs, wrote tutorials, and mentored peers so they wouldn’t feel lost or frustrated in adopting AI. My principle was simple: don’t just show the tool, show the path.
Real Impact
The results have been transformative: Product Managers now prototype ideas rapidly with Figma Make and validate them with customers early, reducing risks and shortening sales cycles. Designers are empowered to create “ideal prototypes” that directly inform developers, while developers receive cleaner, standardized inputs that align with our design system.
Most importantly, I positioned our team as early adopters of practical AI, ensuring we don’t just follow the AI wave but shape it. This initiative continues to evolve, with ongoing experiments for PM workflows, dev handoffs, and scalable integration into our design system.
