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Case Study · Indeed · Employer Product

Scaling design system adoption through embedded product infrastructure

Embedding within employer product teams to build tooling, automation, implementation workflows, and operational systems that drove adoption from 45% to 94% across 500+ teams.

Design system adoption Developer tooling Quality infrastructure AI implementation systems Product operations

Great systems still fail without operational infrastructure

The centralized system solved foundational architecture.

But employer product teams still faced:

The issue wasn’t component quality.

The issue was: implementation infrastructure.

Product teams needed systems that worked inside real development workflows.

Moving closer to where implementation breaks

Instead of operating purely from centralized systems, I embedded directly into employer product teams to understand where adoption was failing.

This meant working inside:

This helped identify friction such as:

This role became the bridge between centralized architecture and product execution.

Reducing repeated implementation work

Employer teams repeatedly rebuilt the same product-level patterns.

To solve this, I helped create employer-specific packages that extended the centralized system with reusable product infrastructure.

These included:

This reduced duplication while keeping teams aligned with central standards.

Reducing manual review bottlenecks

Manual design reviews couldn’t scale.

We introduced automated quality systems directly into engineering workflows.

Linting systems

Design system lint rules prevented misuse.

Visual regression testing

Chromatic and visual diff workflows helped catch regressions early.

Accessibility validation

Automated accessibility checks reduced QA burden.

Implementation auditing

Custom audits identified fragmentation and non-system usage patterns.

Release validation

Pipeline tooling ensured safer updates.

The goal: move quality enforcement earlier into development workflows.

Measuring adoption health

One of the biggest gaps was visibility.

Teams often assumed adoption was healthy when implementation quality was poor.

We built reporting systems to track:

This created much better operational visibility.

Building AI-assisted development systems

As AI coding tools became more common, I helped define how AI agents could operate safely within design system constraints.

This included:

The goal was allowing teams to move faster without introducing new inconsistency through AI-generated code.

Acting as the bridge between platform and product

I wasn’t just consuming the design system. I helped evolve it.

I acted as the liaison between product teams and the centralized design systems organization by:

Scaling adoption through people

Tooling alone doesn’t solve adoption.

I also helped build:

This helped scale behavioral adoption alongside technical adoption.

Operational impact at scale

94% Adoption across employer product teams
73% Reduction in fragmentation
$4.2M Annual productivity savings
60% Reduction in review cycles
2,000+ UI surfaces modernized
1,200+ Repositories supported

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