How a UX researcher uses Figma Make at GM

Quick Overview

UX Researcher Laura Dunn from General Motors demonstrates how the Figma Make AI tool significantly streamlines the creation of complex design artifacts like detailed user flows and design system documentation by generating high-fidelity outputs directly from natural language prompts, saving substantial time compared to manual iteration.

Key Points: Laura Dunn, UX Researcher at GM, showcases using Figma Make AI to generate a comprehensive vehicle service scheduling flow based on screenshots and specific requirements. The AI successfully generated a multi-step, single-page flow incorporating progressive disclosure, which was a major goal of the initial prompt. The initial prompt was detailed, outlining 5 steps for the service flow, including selecting services, location, dealer/address, date/time, and contact confirmation. The AI-generated output maintained the Buick brand aesthetic, including color palettes and typography, demonstrating adherence to brand guidelines. Laura noted that while the AI output was impressive, it sometimes required manual iteration, such as refining the navigation pattern documentation that was generated separately. The AI helped rapidly prototype complex interaction patterns like conditional visibility based on selected services, which would have taken significantly longer manually. The session concluded with Laura demonstrating how the AI helped refine the navigation pattern guidelines documentation by producing a highly structured, multi-column table based on complex input.

Context: The video features a discussion between UX Researcher Laura Dunn from General Motors (GM) and host Jay Dalal on the "Deep Dive" channel, focusing on practical applications of the Figma Make AI tool. Laura uses screenshots of an existing Buick service scheduling website flow to prompt Figma Make to create an improved, interactive, single-page scheduling experience that adheres to GM/Buick branding and incorporates progressive disclosure principles.

Detailed Analysis