Case Studies
Case Studies
Transition to Work
"Here's how this methodology works in practice."
Through real projects, you'll see how these principles and processes come together to create AI systems that government users actually trust and adopt. Each case study demonstrates different aspects of my approach—from complex domain learning to stakeholder collaboration to breakthrough workflow innovation.
The Result: AI solutions that don't just work technically, but succeed in the complex reality of government operations where trust, transparency, and mission success are paramount.
What Makes This Approach Different
Traditional AI UX: Focus on making AI interfaces easy to use My Approach: Focus on making AI-human collaboration effective and trustworthy
Traditional Government UX: Slow, consensus-driven, risk-averse My Approach: Rapid iteration with continuous validation and stakeholder collaboration
Traditional Design Thinking: Linear process from research to solution My Approach: Adaptive, AI-augmented process that accelerates learning while maintaining human insight
The result is AI systems that government users don't just tolerate—they embrace as essential tools that make their critical work more effective.
““While I can’t share specifics from current client work due to NDAs, I can demonstrate my approach through these conceptual frameworks. Each represents real challenges I encounter in government AI automation—and how human-centered design thinking solves them.””
Case Study # 1
Trust-Centered AI Decision Support
Challenge: How do you design AI that operators trust with mission-critical decisions?
Case study #2
Multi-Stakeholder Government Platform
Challenge: How do you design one AI system for completely different user types?
case study #3
Service Design for AI Integration
Challenge: How do you integrate AI without disrupting mission-critical workflows?