SCALING SYSTEMS (WIP)
My Role
Senior UX Designer & Developer — led the identification of workflow gaps in design systems, designed solutions, and developed scripts/plugins to automate repetitive tasks, improve token documentation, and streamline adoption for both internal and external teams.
Platforms
Figma · Figma Make · Tokens Studio · GitHub · Airtable · Custom Scripts/Plugins
Year
2025
The challenge
As our design system scaled past 1800 tokens and multiple releases, our workflows began to break. Designers resisted upgrading to new components because replacing every instance in Figma was a tedious, manual process. Documentation lagged - with only one person managing tokens, keeping Confluence tables up to date was impossible, and tokens stored in tokens studios were nearly unreadable not of use for developers/customers. Customers who customized their tokens struggled with upgrades, since new releases either overwrote their changes or forced them to abandon updates altogether. These gaps created frustration, inefficiency, and resistance to adoption across internal teams and external customers.
The opportunity
I saw a chance to radically improve design system adoption and scalability by automating the most painful workflows. If we could remove the manual burden of component replacement, automate token documentation, and give customers a safe way to upgrade selectively, we would: - Save thousands of hours across design, development, and customer-facing teams. - Build confidence in our system by making tokens and components more accessible and maintainable. - Accelerate adoption of new releases, ensuring our design system stayed relevant and up-to-date. By combining my UX design expertise with scripting and plugin development, I prototyped and built solutions that turned these pain points into opportunities for speed, clarity, and consistency.
The results
The impact has been transformative across three fronts. First, the Component Mapper & Replacer plugin automated the process of scanning Figma files, detecting outdated layers with AI, and replacing them with the latest components meaning updates like typography changes that once took days now happen in seconds. Second, the Token Documentation Automation script pulled tokens directly from GitHub and pushed them into Airtable via API, creating an automated, filterable documentation hub in under five minutes per release - saving over 1000 hours of manual effort and giving both internal teams and customers clear visibility into tokens across releases. Finally, the Token Upgrader & Deprecation Manager plugin allowed customers to selectively upgrade tokens without losing their customizations, while tagging deprecated tokens and offering safe replacements. This not only reduced manual reconciliation but also built trust with customers, who now upgrade more confidently and more often. Together, these solutions have created a smarter, scalable workflow that strengthens adoption, reduces friction, and future-proofs our design system.
Design Principles
Automation First — reduce repetitive manual work.
Scalability — workflows that grow with tokens & components.
Adoption Through Ease — make upgrades effortless, not a chore.
Transparency — visibility across releases for both internal & external teams.
NN/g Heuristics Applied
Visibility of system status (Airtable shows live tokens per release; plugin highlights new/old/deprecated).
Consistency and standards (tokens aligned across GitHub, Airtable, Figma).
Error prevention (automation reduces manual replacement/documentation errors).
Recognition rather than recall (designers browse tokens/components instead of decoding JSON).
Flexibility and efficiency of use (customers selectively upgrade only what they need).
Help and documentation (automated hubs replace outdated Confluence tables).
Deep Dive
1. Component Mapper & Replacer
Problem: Designers resisted adopting new components (like Typography) because replacing every instance manually was too painful.
Solution: Built a plugin that:
Scans the library for component references.
Uses AI + object detection to map layers to new components.
Replaces all instances in seconds, while preserving text/data.
Value: Teams instantly adopt the latest components, keeping designs consistent and connected to tokens.
2. Design Token Documentation Automation
Problem: 1800 tokens with no proper documentation; manual Confluence tables impossible to maintain.
Solution: Script that:
Pulls JSON tokens from GitHub.
Parses names, values, release numbers, updates.
Pushes to Airtable with API keys → instant searchable documentation.
Value:10 releases documented in 10 minutes.
Instant changelogs → customers always know what’s new/old/updated.
~1000 hours saved for the org.
3. Tokens Upgrader & Deprecation Manager
Problem: Customers modified tokens → upgrading broke their customizations.
Solution: Plugin that:
Compares customer tokens with new release.
Lets them selectively accept new tokens.
Preserves customizations, updates only chosen tokens.
Tags deprecated tokens + offers safe removal/replacement.
Value:Customer teams save days of manual token reconciliation.
Smoother upgrades → higher adoption rates of new releases.
Reduced frustration → customers keep control without chaos.
Cumulative Impact
Together, these plugins/scripts created:
Faster adoption of system updates.
Stronger alignment between design & dev.
Greater customer trust in tokens upgrades.
Massive time savings (1000+ hours org-wide).
