Case Study # 3 - Service Design for AI Integration

KEY POINTS

Challenge: How do you integrate AI without disrupting mission-critical workflows?
Design Thinking Focus: Empathize + Prototype phases
Key Insight: "AI integration fails when it ignores existing organizational workflows"

End-to-End Service Journey

User touchpoints, AI processing stages, and feedback loops across the complete workflow

service design approach:

• Holistic View—Map entire user experience including invisible processes
• Multi-Channel—Consider all touchpoints and platforms
• Continuous Improvement—Built in feedback and learning mechanisms

government context:

Government services must work for diverse user populations with varying technical literacy and access to technology.

AI Workflow Process Diagram

Data flow, processing stages, decision points, and feedback loops

process design:

• Multi-Source Input—Integrate diverse data efficiently
• Parallel Processing—AI and rule-based logic work together
• Continuous Learning —Feedback improves future performance

quality gates:

Multiple checkpoints ensure quality and allow for human intervention when needed.

scalability:

Process designed to handle both individual cases and bulk processing scenarios.

Complex System Integration

Multiple platforms, offline capabilities, and synchronization across government infrastructure

integration strategy:

• Hybrid Cloud—Government cloud for AI, on-premise for legacy
• Edge Computing —Offline capability for field operations
• Smart Sync —Priority-based synchronization strategy

technical challenges:

Government systems span decades of technology. Modern AI must integrate with legacy infrastructure while maintaining security and reliability.

design impact:

• Progressive enhancement approach

• Graceful degradation for offline use

• Clear sync status indicators

• Conflict resolution interfaces
  

future considerations:

Design for inevitable system migrations and evolving security requirements while maintaining user experience consistency.