Introduction
AI Showcase - compilation of experiemnts and presentations outside of my work. My investment in my own education and contribution to community.
Introduction
I focus on designing AI experiences that are transparent, trustworthy, and transformative for both users and businesses.
Value 1
Value 2
Value 3
Designing for appropriate trust
Making AI decisions explainable and auditable
What happens when AI fails
Revealing AI capabilities without overwhelming
AI augments, doesn't replace human judgment
Bias mitigation, privacy, and responsible AI practices
AI-Augmented Design Process
practical AI integration and innovation - copy
Image: a visual workflow showing traditional
vs. AI-enhanced process
Animated diagram showing your AI design framework Or Split-screen: Traditional workflow → AI-enhanced workflow
AI Tools I Use & Why
AI tools are amplifiers, not replacements. I use them to enhance human creativity, speed iteration, and scale impact—but the strategic thinking, empathy, and craft always come from me and my team.
Generative Design
Why: Accelerates visual exploration 10x, but I always refine with human craft
- Midjourney (concept exploration, mood boards)
- Runway ML (video prototyping, motion concepts)
Research & Analysis
Why: Surfaces insights I might miss, but I validate with domain expertise
- ChatGPT/Claude (interview analysis, synthesis)
- Dovetail AI (user research patterns)
Prototyping & Production
Why: Removes tedious work, frees me for strategic thinking
- Figma AI features (auto-layout, content generation)
- GitHub Copilot (rapid prototyping for interactive concepts)
Decision Support
Why: Data-informed decisions, but human judgment on trade-offs
- Analytics AI (A/B test insights, predictive modeling)
Ethical & Responsible AI
Why: Non-negotiable—AI products must be equitable
- Fairness testing tools (bias detection)
Leading Teams Through AI Innovation
How I Lead: Building AI-Ready Design Teams
Goal: Demonstrate leadership capability and organizational impact
Content Blocks:
Block 1: My AI Leadership Framework
Create a leadership model visual showing your approach:
Image: Visual framework with icons and illustration
Before My Framework
- Teams treated AI as a "black box"
- Designers felt threatened by AI tools
- AI features shipped without ethical review
- No shared language for AI UX decisions
After Implementation
- 85% of team confident discussing AI capabilities with engineers
- AI ethics checkpoints integrated into design sprints
- 40% faster concept-to-prototype with AI-assisted workflows
- Shared AI design pattern library used across 4 product teams
Cross-Functional Collaboration
Copy:
Image: Collaboration Map
"The AI Product Triad"
Design (Me) ↔ Engineering ↔ Data Science
Whiteboard sketch of your collaboration framework
Collaboration with Engineers
- Translate AI capabilities into user value
- Design feasible AI interactions within technical constraints
- Create AI design specs engineers can implement
Collaboration with Data Scientists
- Define UX requirements for ML models (latency, accuracy thresholds)
- Ensure model outputs are human-interpretable
- Design feedback loops that improve models
Collaboration with Product/Business
- Articulate business value of AI design investments
- Balance innovation with user trust and adoption
- Translate AI features into customer-facing value props
