DS-Bridge: AI-Enabled Workflow

  • AI Systems Design
  • Workflow Automation & Enablement
  • Human AI Collaboration
  • Design Engineering Operating Models

60-Second Summary (TL;DR)

The outcome: Reduced the path from product idea to production-ready UI from months to days by replacing static design documentation with a shared, executable system.

Why it mattered: The organization needed proof that design systems and AI-assisted workflows could scale securely and deliver real value beyond Figma artifacts.

What I did: Designed and validated an AI-enabled operating model that lets non-technical designers plan, build, validate, and document real components directly in code.

Proof: A junior designer with no coding background shipped a production component in under a week; the system produced 5 components, 95 tests, and a live documentation site adopted as the source of truth.

Timeframe: December 2025, 2 weeks from idea to full site

External POC Link: DS-Bridge

Guarantee Sale Component with interactive demo properties

Overview

Primary Impact:
Turned months of design–development handoff into days by letting designers validate production-ready UI directly in code.

Secondary Impacts:

  • Replaced static Figma documentation with a living, executable source of truth usable by designers, engineers, and product partners.
  • Enabled non-technical contributors to safely build and validate real components directly in the codebase.
  • Shifted engineering effort from manual translation to high-leverage refinement.

Role: AI / Systems-Focused Designer (Principal-level scope)

Scope: Company-wide whitelabel design system; design, documentation, workflow automation, and engineering integration.

Why This Is Hard: Required aligning human workflows, AI behavior, and enterprise security constraints while proving real production value, not a demo.

Why Leadership Needed Proof

This work originated from a VP of Product mandate to evaluate whether the company should continue investing in a whitelabel design system at scale. Leadership needed more than static design artifacts to justify further funding and adoption

Prior efforts relied on Figma-based components and written guidelines, which:

  • Failed to drive consistent usage outside the design team
  • Did not give engineering or product leaders confidence in real-world feasibility
  • Required ongoing manual maintenance with unclear ownership

The core leadership question was not “can we design these components,” but:

  • Can this system reduce time-to-market?
  • Can it scale across teams without increasing headcount?
  • Can it operate securely within enterprise AI constraints?

A traditional proof of concept would have been insufficient

  • Static mocks would not surface technical constraints
  • Documentation alone would not demonstrate adoption or workflow change

What leadership needed was proof of an operating model, not a visual demo:

  • Evidence that designers, engineers, and product partners could work closer together
  • Validation that AI-assisted workflows could produce production-ready output
  • Confidence that this approach could become a long-term system of record

The Real Problem (Not the Obvious One)

What leadership thought the problem was:

  • Create a small set of static whitelabel components in Figma
  • Show visual examples of how other brands might be supported
  • Prove concept viability at a surface level

What was missing from that framing:

  • No consideration for how the system would scale
  • No plan for long-term maintenance or contribution
  • No clear ownership model
  • No assessment of time, cost, or operational overhead
  • No exploration of how AI workflows could reduce effort or improve quality

The real problem I identified:

  • Design systems fail not at creation, but at contribution and maintenance
  • Small updates accumulate hidden cost when every change requires manual Figma work and parallel documentation
  • A decentralized design system spread across multiple Figma files had no single, reliable source of truth

What would have broken if nothing changed:

  • Whitelabel components would quickly drift out of sync
  • Files would continue to grow bloated and underused
  • Designers would absorb ongoing manual maintenance work
  • Engineers and product partners would still lack a clear, shared place to reference the system

The actual need:

  • A way to rapidly scale a design system that is easy to maintain
  • One URL, one system, one source of truth
  • A workflow that makes small changes cheap at scale

Operating Model We Proved

Strategy: Treat design and development as a continuous, shared feedback loop where real code is the validation layer, not the final step

Mental model:

  • Figma is a collaborative exploration tool, not the source of truth
  • Code is where interaction quality, edge cases, and constraints are validated
  • AI acts as an accelerator and guardrail, not a replacement for judgment

What this replaced:

  • Treating Figma as the final artifact handed off to engineering
  • Writing extensive documentation before behavior was validated
  • Relying on engineers to surface interaction issues late in the process

What changes immediately when adopted:

  • Designers approach Figma with implementation efficiency in mind
  • Components are validated through interaction, not assumption
  • Design, product, and engineering align earlier around what will actually ship

The 3 Decisions That Drove the Outcome

Decision 1: Component Demo-First, Two-Phase Workflow

Problem it solved: Designers and AI agents were attempting to refine component behavior and documentation simultaneously, which led to redundant work, bloated AI context, and lower-quality results

Options considered: Build component and documentation together in a single pass. Fully automate component and documentation generation. Separate component validation from documentation

Tradeoff accepted: Slower initial documentation in exchange for higher-quality components and dramatically less rework

Decision: Enforced a component demo-first workflow where designers:

  • Push a real component into the codebase
  • Interact with it directly to validate all states, properties, and edge cases
  • Iterate between Figma and code until behavior is correct
  • Enter documentation only after the component is validated

Why this worked:

  • Reduced AI context window size, improving output quality
  • Prevented designers from trying to solve too much at once
  • Lowered cognitive load for non-technical designers
  • Ensured documentation reflected real, validated behavior

Proof:

  • Fewer rework cycles per component
  • Faster convergence on correct behavior
  • Cleaner, more accurate documentation

Guarantee Sale Overview with when to use and when not to use

Decision 2: Structured Session Planning and Command-Based Guardrails

Problem it solved: Non-technical designers could easily become disoriented in the terminal, jump between tasks, or give the AI unscoped instructions that led to inefficiency, confusion, and low-quality output

Options considered: Allow free-form prompting and trust designers to self-organize. Create heavy documentation or training upfront. Encode structure directly into the workflow.

Tradeoff accepted: Reduced flexibility in exchange for clarity, focus, and repeatability

Decision: Introduced structured session planning and designer-friendly commands that:

  • Forced agreement on scope before execution
  • Guided designers through a predictable sequence of actions
  • Automatically captured progress, decisions, and outcomes

Why this worked:

  • Prevented designers from jumping around or over-scoping work
  • Taught systems thinking implicitly through repeated use
  • Rewarded focus with visible progress and higher-quality results
  • Made contribution feel safe and approachable for non-technical users

Proof:

  • Designers stayed focused within sessions
  • Reduced need for corrective prompting
  • Increased confidence and willingness to contribute over time

Designer Developer Workflow 2 phase plan

Decision 3: Let a Junior, Non-Technical Designer Own a Real Component

Problem it solved: A senior-led proof would not demonstrate scalability or cultural change; leadership needed evidence that the system worked for people without systems thinking or coding experience

Options considered: Use myself as the primary operator of the system. Pair closely with a designer and guide each step. Give a junior designer real ownership and step back.

Tradeoff accepted: Higher risk of failure in exchange for stronger proof of scalability and enablement

Decision: Chose a junior designer with no coding background and limited systems exposure and:

  • Taught only the minimal set of commands needed to operate the workflow
  • Explained where to view live output
  • Intentionally avoided intervening in debugging, refinement, or problem-solving

Why this worked:

  • Forced the system to be self-explanatory and resilient
  • Created true ownership instead of dependency
  • Enabled the designer to build confidence through independent problem-solving
  • Revealed whether the workflow could teach systems thinking implicitly

Proof:

  • The designer independently shipped and documented a real component
  • Continued working on the system without supervision
  • Demonstrated leadership and initiative beyond their role

Evidence and Validation

Cycle-time compression:

  • Reduced idea-to-interactive validation from weeks or months to days
  • Enabled same-week validation of component behavior in real code

Production proof:

  • 5 whitelabel-ready components shipped
  • 95 automated tests with full TypeScript coverage
  • Live documentation site used as the system of record

Behavioral signals:

  • A junior designer independently planned, built, debugged, and documented a component
  • Continued contribution without supervision or intervention
  • Increased designer confidence and ownership over the system

What would not have happened otherwise:

  • Designers would not have validated interaction quality in real code
  • Engineers would still translate intent instead of refining solutions
  • Leadership would lack confidence in scalability beyond Figma artifacts

Constraints That Shaped the Work

Security and compliance:

  • All AI workflows, tooling, and integrations required approval from internal AI engineering
  • No external data leakage or unapproved tooling allowed

Organizational timing:

  • Leadership availability was limited during execution
  • Required proactive alignment with product owners and engineering partners

Human constraints:

  • Designers had no prior terminal or coding experience
  • The system needed to feel safe, guided, and reversible

Reflection (Forward-Looking)

Capability unlocked:

  • Designing shared operating models where design and development converge in real code

How this changed my approach:

  • I now prioritize workflow and system design over isolated deliverables
  • I design for independence, ownership, and scale rather than supervision

What I look for next:

  • Problems where AI can remove friction between disciplines
  • Opportunities to make validation faster and decision-making cheaper

Appendix (Optional)

  • Supporting artifacts
  • Deep technical details
  • Additional metrics or timelines
  • Link to work / URL