Design System Design Stories

Is Your Design System Machine-Readable?

Until recently, design systems were crafted for humans, but today, they need to be understood by machines too

By Roopa V Rao · Jul 1, 2026 1

Is Your Design System Machine-Readable?

You read it right! Until recently, design systems were crafted for humans, but today, they need to be understood by machines too. AI is emerging as a new consumer of the Design System, along with Developers.

The next evolution of the design system is no longer just “Design to Code” but moving towards “Design to Code to AI Readiness”. While AI introduces uncertainty, the design system will play a critical role in bringing consistent interaction patterns, transparency to system states, enhanced usability and accessibility.

Tools like Figma already contain rich design data like variables, components and variants, but are confined within the design ecosystem. The Model Context Protocol(MCP) is changing that. MCP acts as a bridge, enabling AI to directly understand the design system.

With MCP, design systems evolve beyond a static library of components into a living data set, continuously synced across design, code and AI. With well-defined context layers, design systems can turn into powerful AI-enabled systems.

While traditional design systems focus on tokens, component names and documentation, AI+MCP-driven design can enable context-rich and intent-driven design. Designers need to switch their deliverables from structured design outputs to training the machines to understand design intent.

Designers need to understand how to implement design systems with Intent. It starts with understanding MCP and aiming to create a consistent and structured system.

When MCP connects to Figma, it extracts variables, component structure, naming semantics, variants, and description. MCP does not add intelligence. It exposes structured design data, so AI can interpret and act on it.

Step 1: Define the naming conventions

Ensure consistency and clarity across variables, components and patterns.

Step 2: Define the primitive (foundational) variables

Define base tokens such as colours, spacing, and typography.

Step 3: Create semantic( functional) variables

Define context-aware variables derived from primitives.

Example: button-primary-bg

Step 4: Embed intent in description

Define when and why the variable is used.

Example:

Primary action hover state

Text Input box border (active state)

Step 5: Structure components with semantic meaning

User clear component names, define properties, variants, and clearly define the intent in the description of the component.

Example:

Primarily used for high-intent actions like checkout

The action is irreversible

Step 6: Name patterns using the Flow type/user/Intent/Outcome format

This is where the design system evolves. From components to behaviour flows. Provide clear purpose descriptions.

Example:

checkout/registered-user/ progressive / conversion

checkout/guest/ progressive / drop-off

The goal is to define the structure and also embed the intent for both humans and AI to understand the reason behind the design.

Advantages of implementing a design system for humans and AI

Focus shifts from UI to behaviours which drive higher customer experience.
Unified patterns, interactions, behaviours across multiple products.
Faster builds due to reusable components and code + AI-assisted code generation.
Faster time to market due to reduced Design, Dev and QA cycles.
Reusable logic, as we are moving from only components to intent.
Reduced testing time. Standardised patterns minimise edge cases and inconsistencies.
A well-defined system enables AI to generate reliable test cases.

ROI to Business

Speed: Faster release cycles and quicker iterations.
Efficiency: Lower design, dev and QA time over a period (though higher initially)
Quality: Fewer bugs, more consistent experience
Scalability: System grows across products without a proportional cost increase.

This is how I see designCreating products or solutions that are user-centred and solve real problems while being useful, usable, desirable, accessible, and valuable, both for the user and the business. systems evolving in the AI era. I am keen to hear how others are approaching this. What’s working, what’s not, and where do you see the gaps?

About the Author

Roopa V Rao

Design Strategist & Consultant

With nearly two decades of experience leading enterprise design, I help organisations transform UX from a tactical function into a strategic capability. I partner with leadership teams to shape digital products that align user needs with business goals, regulatory compliance, and operational realities.

I specialise in healthcare and other regulated industries, bridging strategy, research, and execution to design products that are intuitive, safe, compliant, and scalable. My work focuses on reducing risk, improving adoption, accelerating decision-making, and creating meaningful business impact.

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