Cutting Truck Rolls with AI: Faster Fixes, Real Savings
In this “How might we” north star, I prototyped how to bring transparent AI to field ops. An agentic “no-truck-roll coach” that unifies operational data and proposes next best actions across three moments with human-in-the-loop controls—so teams resolve remotely, schedule smarter, and improve FTF with confidence.
Why it’s needed
High truck-roll rates and inconsistent FTF drive costs, delays, and emissions. Agents lacked a single, explainable way to decide between remote resolution and dispatch, or to prioritize parts and scheduling with confidence.
The experience
An agent opens a ticket; the coach explains the most likely root cause, shows ranked factors and confidence, and offers a one-click remote fix. If a visit’s needed, it proposes an appointment window, technician, and parts—plus side-by-side alternatives with cost/FTF/SLA trade-offs. The agent can accept, modify, or override, and every decision feeds an audit log and learning loop.
How it’s AI-First
Agentic workflow: Chained tools (diagnose → propose → simulate → schedule) orchestrate tasks, not just predictions.
Unified signals: CPE telemetry, ticket history, inventory, and telematics fused into a single decision context.
Human-in-the-loop by design: Agents steer with constraint weights, approve actions, and teach the system via feedback.
Transparent intelligence: Every recommendation is explainable (“why-this”), confidence-scored, and auditable.
Continuous learning: Decisions and outcomes feed a feedback loop to improve models and recommendations.
Operationally realistic: Starts from today’s data and workflows; validates the riskiest assumptions first (trust, data quality, control) while keeping the vision achievable.
