Case Study
AI Support Operations Lab
Designing systems to improve support performance using data and AI
Problem
Support leaders often have visibility into their metrics but lack a clear way to translate those metrics into actionable improvements and predictable outcomes.
As a result, teams struggle to prioritize the right changes, understand trade-offs, and quantify impact.
Solution
I designed an interactive system that connects support performance data to operational decision-making.
The system allows leaders to:
- analyze performance across key metrics
- identify operational bottlenecks
- simulate improvements
- quantify expected business impact
System Overview
The system is structured around five core components:
- Dashboard — provides a real-time view of operational performance
- Tickets — surfaces root causes through AI-assisted categorization
- Agents — models workload, utilization, and burnout risk
- Simulator — allows leaders to test operational changes
- AI Advisor — translates data into strategic recommendations
Simulation Layer
Rather than simply reporting metrics, the system enables leaders to simulate changes and evaluate outcomes before implementation.
Examples:
- increasing routing accuracy
- expanding self-service
- reducing handoffs
- optimizing staffing
Each change is tied to measurable outcomes such as SLA performance, backlog size, resolution time, and customer satisfaction.
Impact
This system demonstrates how support organizations can:
- move from reactive reporting to proactive decision-making
- prioritize the highest-impact operational improvements
- reduce inefficiencies and cost-to-serve
- improve both customer experience and team performance
Tech
Built with React, TypeScript, Tailwind, and Recharts using synthetic support data.