The 5 Phases - Adapted for AI-Human Systems

 

Empathize - Understanding the Human Reality Behind AI Requirements

Traditional Approach: User interviews and observation to understand needs and pain points.

My AI-Enhanced Approach:

  • Deep Domain Immersion with subject matter experts who understand both current workflows and automation potential

  • Collaborative intelligence sessions where stakeholders and I explore problems together using AI as a thinking partner to uncover insights that wouldn't emerge through traditional interviews alone

  • Systems-level empathy - understanding not just individual user needs, but how those needs fit within complex organizational and regulatory environments

  • Trust pattern identification - discovering what makes users confident in automated systems and what triggers skepticism

Key Innovation: I use AI collaboration during the empathy phase to accelerate domain learning and help stakeholders articulate complex challenges they might struggle to express in traditional interviews.

 

Define - Framing AI Problems as Human Problems

Traditional Approach: Problem statements and user personas based on research synthesis.

My AI-Enhanced Approach:

  • Human-AI partnership definition - clearly articulating the ideal collaboration between human expertise and AI capability

  • Trust requirement mapping - identifying what users need to see, understand, and control to have confidence in AI recommendations

  • Systems constraint integration - defining problems within the reality of existing workflows, legacy systems, and organizational structures

  • Macro-micro problem framing - ensuring solutions work at both the individual interaction level and the system-wide impact level

Key Innovation: Instead of defining problems as "what can AI do," I frame them as "how can AI amplify human capability while maintaining human agency and oversight."

 

Ideate - Generating Solutions That Enhance Human Capability

Traditional Approach: Brainstorming sessions and ideation workshops to generate multiple solution concepts.

My AI-Enhanced Approach:

  • AI-augmented ideation - using AI as a creative partner to explore solution spaces I might not consider alone, while maintaining human judgment about feasibility and desirability

  • Workflow revolution thinking - looking for opportunities to fundamentally transform processes rather than just automate existing ones

  • Pattern synthesis across domains - leveraging AI's ability to identify patterns across different industries and applications that might inform breakthrough solutions

  • Real-time stakeholder collaboration - conducting ideation sessions where ideas can be immediately explored and refined with AI assistance

Key Innovation: I've learned to use AI not just as a research tool, but as a thinking partner that helps me and stakeholders push beyond incremental improvements to revolutionary workflow transformations.

 

Prototype - Building AI Concepts You Can Test With Real Users

Traditional Approach: Wireframes and clickable prototypes to test user interactions.

My AI-Enhanced Approach:

  • Dynamic scenario prototyping - creating prototypes that demonstrate AI behavior across multiple use cases and edge conditions

  • Trust gate visualization - building prototypes that explicitly show how users can verify, modify, and override AI recommendations

  • AI-assisted rapid iteration - using AI tools to quickly generate and modify prototype variations based on stakeholder feedback

  • System integration mockups - prototyping how new AI capabilities integrate with existing workflows and legacy systems

Key Innovation: My prototypes go beyond interface design to demonstrate the AI's "personality" and decision-making process, helping users understand how to collaborate effectively with the system.

 

Test - Validating AI UX in High-Stakes Environments

Traditional Approach: Usability testing with task completion metrics and user feedback.

My AI-Enhanced Approach:

  • Expert validation sessions - testing with domain experts who can evaluate both usability and accuracy of AI-assisted workflows

  • Trust building measurement - tracking how user confidence evolves through repeated interactions with AI prototypes

  • Edge case exploration - deliberately testing scenarios where AI might fail to ensure graceful degradation and clear human override paths

  • Systems impact assessment - evaluating how individual user interactions affect broader organizational workflows and outcomes

Key Innovation: I test not just whether users can complete tasks, but whether they understand when to trust AI recommendations and when to rely on their own expertise.

 
Design Philosophy - 4 Core Principles
 
 

AI Partnership: This site demonstrates my approach to AI collaboration—human ideas enhanced by AI capability.
Content and insights: 100% my experience and thinking.
Organization and articulation: Claude AI assistance.
Design mockups: Figma Make.
Imagery: Midjourney.

Learn more about human-AI collaboration in my methodology.