Cutting Truck Rolls with AI: Faster Fixes, Real Savings
This rapid prototype brings transparent AI to field ops. In this example, an agentic coach unifies CPE telemetry, ticket history, inventory, and telematics to deliver “why-this” recommendations with human-in-the-loop controls—so teams resolve remotely, schedule smarter, and improve FTF with confidence.
Challenge
High truck-roll rates and inconsistent first-time-fix (FTF) rates can drive up costs, lead to delays, and increase CO₂ emissions. Agents lacked a unified, explainable way to decide between remote resolution and dispatch.
Solution
In a rapid prototype, we built an agentic “no-truck-roll coach” that fuses CPE telemetry, ticket history, inventory, and telematics to recommend remote fixes, optimal scheduling, and parts—always with agent controls. The UX makes AI decision-making transparent via ranked factors, confidence, “why this,” weight sliders for constraints (cost/FTF/SLAs), side-by-side alternatives, and an audit log + feedback loop so agents can accept, modify, or override.
Expected Impact
Modeled 15% dispatch reduction—avoiding ~1,800 truck rolls and ≈$720K/month—while higher FTF lifts savings and CSAT and reduces miles/CO₂; explainability and feedback loops accelerate agent trust, training, and continuous model improvement.
