Despite the hype, LinkedIn found that third-party AI tools for coding and design don't work out-of-the-box on their complex, legacy stack. Success requires deep customization, re-architecting internal platforms for AI reasoning, and working in "alpha mode" with vendors to adapt their tools.

AI prototyping shifts the purpose of a design system from a human-centric resource, reinforced through culture and reviews, to a machine-readable memory bank. The primary function becomes documenting rules and components in a way that provides a persistent, queryable knowledge base for an AI agent to access at all times.

Atlassian improved AI accuracy by instructing it to first think in a familiar framework like Tailwind CSS, then providing a translation map to their proprietary design system components. This bridges the gap between the AI's training data and the company's unique UI language, reducing component hallucinations.

AI-generated design falls short because it cannot integrate the myriad of constraints top designers handle: business goals, cultural context, brand emotion, and system-wide consistency. AI will eliminate drudgery, freeing designers to focus on this higher-level, holistic, and creative work.

Moving PRDs and other product artifacts from Confluence or Notion directly into the codebase's repository gives AI coding assistants persistent, local context. This adjacency means the AI doesn't need external tool access (like an MCP) to understand the 'why' behind the code, leading to better suggestions and iterations.

To avoid inconsistent or 'vibe coded' documentation, Atlassian's design system team built scripts that crawl their front-end monorepo. These scripts automatically generate structured guideline files for AI consumption by extracting component definitions, types, and usage examples directly from the production source code.

AI tooling is creating a 'fluid model' where any employee, regardless of role, can potentially ship code. This dramatically expands the design system team's responsibility, which must now create tooling and guardrails to support a much broader and less technical user base across the entire organization.

Move beyond basic AI prototyping by exporting your design system into a machine-readable format like JSON. By feeding this into an AI agent, you can generate high-fidelity, on-brand components and code that engineers can use directly, dramatically accelerating the path from idea to implementation.

AI tools like LLMs thrive on large, structured datasets. In manufacturing, critical information is often unstructured 'tribal knowledge' in workers' heads. Dirac’s strategy is to first build a software layer that captures and organizes this human expertise, creating the necessary context for AI to then analyze and add value.