🎯
Business Objectives
- ›Maximize billable capture
- ›Detect leakage and fraud
- ›Forecast cost-to-serve
⚙️
Operational KPIs
- ›Avg expense / WO
- ›Billable %
- ›Fuel cost / mile
📈
Management KPIs
- ›Cost-to-serve
- ›Margin per WO
🤝
Customer KPIs
- ›Pass-through accuracy
🗄️
Available Data
- ›Expense type, amount, billable flag
- ›Vehicle, driver, WO linkage
- ›Receipt timestamp
✨
AI Use Cases
- ›Receipt OCR + auto-categorization
- ›Fraud / duplicate detection
- ›Billable classification model
🔮
Predictive Analytics
- ›Forecasted monthly cost per category
- ›Predicted billable conversion lift
🚨
Anomaly Detection
- ›Outlier fuel charges
- ›Driver-level expense outliers
🎁
Recommendation Engines
- ›Suggested missing billable line items
🤖
AI Copilot Features
- ›'Why did expenses spike last week?'
💬
Natural-Language Reporting
- ›'Show me non-billable expenses > $50 this month'
📊
Executive Dashboard Metrics
- ›Cost per WO trend, billable %, top cost drivers
⚡
Workflow Automation
- ›Auto-attach receipts to WO
- ›Auto-flag duplicates
🛰️
Autonomous Agents
- ›Expense Auditor Agent reviews weekly batch
🖥️
Recommended Dashboards
- ›Cost Control
- ›Billable Leakage
💡
Example Executive Insights
- ›$8.7k/mo non-billable expenses could be reclassified with rule update
📦
Required Datasets
- ›expenses
- ›work_orders
- ›drivers
- ›vehicles
📅
Historical Data Needed
12 months
🧠
ML Models / AI Approaches
- ›OCR (Document AI)
- ›Isolation Forest
- ›Rules + ML hybrid