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AI Adoption & Innovation

As Director of both the UX and Content teams, I led a series of initiatives to integrate AI into our design, documentation, and development workflows. These efforts focused on increasing velocity, improving accuracy, reducing repetitive work, and expanding the strategic value of our teams across the organization.
 

AI is now embedded into multiple stages of our product lifecycle—from discovery and design to documentation creation, component development, and engineer handoff. The result is a more efficient, scalable, and future‑ready operation.

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To comply with my non-disclosure agreement, I have omitted any confidential information. The information in this case study is my own interpretation and does not necessarily reflect the views of my employer. 

My Role

I acted as both strategist and hands‑on practitioner, responsible for:

  • Identifying high‑impact AI use cases across UX and Content

  • Training teams on effective prompting and ethical usage

  • Partnering with Product and Engineering to integrate AI into cross‑team workflows

  • Developing new AI‑driven pipelines for design, documentation, and code creation

  • Establishing repeatable processes for quality, consistency, and governance

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AI in UX Design: Accelerating Exploration and Delivery

Rapid Wireframing & Concept Generation

UX designers use AI to produce:

  • Rough wireframes and layouts

  • Alternative interaction concepts

  • Exploration prompts for edge cases

  • Variations on complex workflows
     

This allows faster iteration and more alignment during early planning stages.
 

AI + Figma MCP to Maintain and Expand the CCL

We use AI and the Figma MCP to:

  • Create new CCL components

  • Enhance existing patterns

  • Align designs to token rules

  • Auto‑generate code snippets for integration
     

We collaborated with engineering to ensure AI‑generated components follow coding standards, accessibility rules, and token structure. Designers now create AI‑generated pull requests, which developers review and check in—dramatically speeding up the creation and maintenance of the CCL.
 

AI‑Generated Checklists & Workflows

We used AI to ensure every designer follows a consistent, high‑quality workflow when creating:

  • Repeatable task lists

  • Component QA steps

  • Documentation guidance

  • Handoff templates

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AI in UI/UX

A major innovation is the Harness—a small application built with real, coded CCL components and matching Figma prototypes. This repository is provided directly to developers.

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Benefits:

  • UX and Product can review actual working UI code, not just prototypes

  • Faster iteration with fewer back‑and‑forth cycles

  • Developers focus on logic and data, not front‑end construction

  • Higher accuracy between design intent and coded implementation
     

This has significantly increased delivery velocity and improved quality.

 
AI in Content Operations

AI has been especially impactful in modernizing our documentation workflows across the 5,000+ pages we migrated and continue to maintain.

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Automation for Migration & Formatting

For the migration of 240+ PDFs, I built a Python tool powered by AI logic that:

  • Mapped Word styles to CSS

  • Converted pages to HTML

  • Reduced formatting work from ~20 minutes per page to under 1 minute
     

This allowed writers to focus on accuracy, verification, and content refinement.

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Prompt Frameworks for Consistent Content

To ensure consistency across the team, we created a library of AI prompt templates for:

  • Merging multiple topics into one cohesive article

  • Maintaining consistent writing style and tone

  • Rewriting outdated content for clarity

  • Improving grammar, structure, and flow

  • Creating summaries, highlights, and callouts

  • Converting product training transcripts into polished product documentation

 

These prompt frameworks ensure that all writers—regardless of style or seniority—produce unified, high‑quality content.

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AI‑Assisted Structural Improvements

AI helps the team:

  • Identify and consolidate duplicate content

  • Break long PDFs into cleaner HTML topic structures

  • Suggest cross‑links between related topics

  • Flag outdated screenshots or UI references

  • Normalize inconsistent terminology across products

 

This dramatically improves both accuracy and usability of the content.

 
AI for Analysis & Planning

AI supports broader operational work by helping us:

  • Build documentation roadmaps

  • Evaluate user stories for completeness

  • Analyze customer feedback and usability metrics

  • Develop project schedules

  • Prioritize upcoming documentation updates

 

This has allowed us to make more informed decisions, faster.

 
Cross‑Team Impact

AI adoption has improved UX, Content, Product, and Engineering in the following ways:

  • Faster design cycles from AI‑generated wireframes and CCL components

  • Higher accuracy in documentation and fewer outdated artifacts

  • Reduced development effort through the Harness and code‑aligned Figma output

  • Better consistency through prompt libraries and standardized workflows

  • More data-driven decisions from AI‑assisted analysis

  • Greater scalability for future product expansion

The Final Word 

AI has become an integral part of both team operations—enabling faster delivery, better quality, and more consistent experiences across design, documentation, and engineering. These efforts elevated the impact of UX and Content, transforming both teams into strategic accelerators for the organization.
 

This initiative continues to evolve as we explore new AI capabilities, refine our workflows, and expand how we use AI to support our product ecosystem.

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