• Audit the current product UI and existing design artifacts; identify the highest-leverage opportunities to standardize, consolidate, and reduce inconsistency.
• Define the information architecture of the system itself — naming, taxonomy, component hierarchy, and documentation structure — so that both humans and AI agents can find, reason about, and correctly apply the right primitives.
• Create a concrete step-by-step plan by partnering with design, engineering, and leadership, to correctly prioritize pieces of this effort, balancing long-term scalability with near-term product needs.
• Define and ship a scalable token strategy (color, typography, spacing, elevation, motion) with a pipeline that keeps Figma variables, code tokens, and Storybook in sync.
• Partner with Front-End engineers to translate the system into reusable, well-architected components with clear APIs, states, and accessibility built in.
• Own the component lifecycle end-to-end: versioning, release notes, deprecations, and handoffs.
• Build a lightweight, builder-friendly contribution process — designers, engineers, and PMs should be able to propose, review, and add to the system without a heavy governance tax.
• Create documentation that people (and agents) actually use: guidelines, do/don't examples, usage rationale, and decision records, structured so they're machine-readable as well as human-readable.
• Drive pragmatic adoption: start where it matters most, ship iteratively, and build credibility through visible product impact.
• Define how AI agents help audit, tag, and maintain the system's structure — and where human review gates belong (naming decisions, deprecations, new pattern introductions).
• Shape the pipeline that takes a Figma frame or a PM's prompt through Cursor / Claude Code into Storybook-validated components, including the review checkpoints that keep quality, accessibility, and brand integrity intact.
• Make the system legible to agents. Ensure components, tokens, and patterns are documented, named, and structured so that when a designer or PM prompts an agent to "build the post-intent screen," the agent reaches for the right primitives and produces something shippable — not something that needs to be rebuilt.
• Define what success looks like and track meaningful signals: adoption, UI consistency, design/dev cycle time, accessibility improvements, and the quality of AI-generated output against the system.
• Partner with Research and Data where useful to connect system changes to product outcomes (quality, user confidence, conversion efficiency).
