Is your design system AI-ready?

Tokens are the vocabulary AI uses to understand your system. Well-named, tiered tokens (primitive → semantic → component) give AI agents the foundation they need to generate on-brand results.

• Your tokens are named semantically (e.g., color.action.primary, not blue-500)
• Token tiers are defined: primitives, semantic aliases, and component-level tokens are distinct
• Tokens are available in a format tools can consume (JSON, CSS variables, Figma Variables)
• Token documentation is current and accessible to the full team

AI-assisted code generation is only as good as the component API it has to work with. A strong API means less prompt engineering and more reliable output.

• Components follow consistent prop naming conventions across the library
• All variants and states are documented and reflected in the codebase
• Component behavior (default, disabled, loading, error) follows predictable patterns
• Props and their expected values are documented in a way an AI tool could reference

When Figma components don’t match their code counterparts, AI tools working across both surfaces make mistakes. Parity makes the design layer a reliable source of truth.

• Figma component names match their code counterparts exactly
• Design tokens in Figma are mapped and synced to code tokens
• Figma component variants reflect what actually exists in production code
• There is a defined process for keeping Figma and code in sync as components evolve

AI tools that read Figma files rely on layer names and structure. A file full of “Frame 47” is unreadable to an agent; semantic names let it navigate, extract, and generate accurately.

• Layers in your Figma files are named descriptively and consistently
• Components use Auto Layout rather than fixed frames wherever applicable
• Design files are organized with a clear hierarchy (library vs. product vs. drafts)
• Naming conventions are documented and enforced (through review or tooling)

AI tools can propose changes and generate new components — without a governance model, those contributions create drift instead of progress.

• There is a defined process for proposing and contributing new components
• Component ownership is clear — someone is accountable for quality and updates
• Breaking changes follow a documented versioning and communication process
• At least one person (or team) is responsible for system health and evolution

The teams getting ahead right now have at least one repeatable AI-assisted workflow — not just conversations about it.

• Your team has experimented with at least one AI tool in a design system context
• At least one repeatable AI-assisted workflow is in use today (token generation, docs, code review…)
• System documentation is clear and machine-readable, not just human-friendly
• The team shares an understanding of where AI can and cannot be trusted in your workflow