AI Operations Command Center

A high-fidelity AI Product Management portfolio project showing how an enterprise operations team could use AI to predict fulfillment risk, explain root causes, simulate business tradeoffs, and coordinate human-in-the-loop decisions.

This project is designed to show senior product judgment: applied AI, operational complexity, measurable business impact, human-in-the-loop design, and enterprise adoption thinking.

Product thesis

Operations teams do not need another dashboard. They need an AI decision layer that connects operational signals across OMS, WMS, ERP, demand planning, carrier data, and finance systems, then turns those signals into prioritized actions.

Core user problem

Fulfillment leaders are drowning in lagging indicators. By the time OTIF, cost/order, or stockout metrics show a problem, the team has already lost margin, customer trust, or working capital.

Demo features

• Executive operations dashboard
• AI-prioritized incident queue
• Natural-language Ops Copilot
• Root-cause explanation with confidence scoring
• Fulfillment network risk map
• Forecast intelligence comparison
• Scenario simulator for cost, service, and inventory tradeoffs
• Human-in-the-loop incident timeline

Business outcomes modeled

Outcome	Metric
Improve delivery reliability	OTIF +1.8 pts
Reduce revenue leakage	$184K protected in simulated incident
Improve working capital	$1.2M released from safety stock optimization
Reduce fulfillment cost	6.1% lower cost/order
Speed up decision-making	Root-cause analysis in minutes instead of days

AI capabilities represented

• Retrieval-augmented generation across operational systems
• Time-series anomaly detection
• Root-cause analysis
• Scenario simulation
• Confidence scoring
• Human approval workflows
• Audit trail for enterprise governance

Repository structure

/product-strategy
/personas
/architecture
/figma-export
/demo-video
/prd
/kpi-framework
/roadmap
/sample-data
/src

Run locally

npm install
npm run dev

Portfolio positioning

This is not intended to be a production application. It is a high-fidelity product strategy artifact designed to demonstrate:

1. AI-native product thinking
2. Enterprise workflow design
3. Metrics ownership
4. Technical fluency
5. Strategic prioritization
6. Executive storytelling