AI Workflow for Dynamic Crew Prioritization Based on Real-Time Risk Scores

By dynamically routing crews to the highest-consequence risks first—such as vegetation within critical clearance zones or corrosion near ignition sources—the workflow directly targets the precursors to wildfires, explosions, and major outages. This shifts capital from reactive, high-cost emergency response to proactive, lower-cost prevention, quantifiably reducing environmental liability, regulatory fines, and insurance premiums.

The orchestrator eliminates daily manual schedule juggling by dispatchers. It continuously ingests live risk scores from satellite, drone, and IoT systems (e.g., SAP, Maximo) and re-sequences mobile workforce task queues. This reduces windshield time, minimizes crew idle periods between jobs, and ensures the highest-skilled crews are matched to the most complex faults, improving effective labor capacity by 20-30% without adding headcount.

Static annual plans often address low-risk items while high-risk conditions develop. This workflow ensures capital and labor are applied where they mitigate the most financial and operational exposure. By shortening the time between risk detection and crew dispatch from weeks to hours, it extends asset life, defers major replacement capex, and improves the ROI of the entire maintenance budget.

Automated prioritization creates a defensible, data-driven audit trail for why specific assets were addressed before others. The system logs all risk inputs, the orchestration logic (e.g., LangGraph), and human-in-the-loop approvals for major schedule changes. This demonstrably satisfies regulators (e.g., FERC, NTSB) that the utility is using best-available technology for risk management, reducing compliance overhead and inspection friction.

The central orchestrator acts as a unified command center, fusing geospatial models, weather feeds, and crew GPS into a single operational dashboard. This gives managers real-time visibility into risk versus mitigation capacity, enabling faster, more confident decisions during storms or public safety power shutoff (PSPS) events. It turns fragmented data into a coherent operating picture.

The build is not a one-time fix but a scalable orchestration layer. New risk data sources (e.g., InSAR for subsidence, acoustic sensors for leaks) can be integrated as modular agents. The business impact compounds over time as the system's predictive accuracy improves, allowing for finer prioritization and earlier intervention, continuously driving down operational risk and cost per mitigated unit.

This core component ingests and normalizes live signals from satellite (e.g., Planet, Sentinel), drone inspection platforms, LiDAR scans, IoT corrosion sensors, and weather APIs. A fusion agent correlates these disparate data streams by asset ID and geolocation, applying weighted scoring logic to generate a unified, time-stamped risk score for every asset in the network. It replaces manual data aggregation from 5+ systems, ensuring the prioritization engine operates on a single source of truth.

Built on an orchestration framework like LangGraph or Temporal, this agent continuously evaluates the prioritized risk queue against crew capacity, location, skills, and parts inventory. It automatically generates optimized daily work schedules and pushes them to mobile workforce management systems (e.g., SAP Fieldglass, ClickSoftware). The orchestrator recalculates schedules every 15 minutes based on new risk alerts or field delays, eliminating static, day-old planning.

For high-cost or high-consequence schedule changes (e.g., diverting a crew from planned maintenance to an emergency repair), the workflow routes a change proposal with cost/risk trade-off analysis to a human dispatcher via Slack, Teams, or a dedicated dashboard. The dispatcher can approve, modify, or reject the change within a configurable SLA (e.g., 5 minutes). All decisions are logged with rationale for auditability, balancing autonomy with operational control.

Optimized schedules and work orders are pushed directly to crew tablets via integration with SAP Mobile, ServiceNow Field Service, or custom mobile apps. Upon task completion, crews submit digital close-out reports with photos and notes. This feedback (e.g., 'corrosion worse than expected') is ingested back into the risk engine to recalibrate future scores for similar assets, creating a closed-loop learning system.

A real-time executive dashboard built on Grafana or similar displays key metrics: risk backlog by severity, crew utilization, predicted vs. actual risk reduction, and cost of delay. It provides traceability from a satellite-detected vegetation encroachment alert through to the crew dispatched and the work completed. This is critical for demonstrating ROI to leadership and for post-event analysis of system performance during storms or other crises.

The workflow's actions must be reflected in systems of record. This integration component uses API agents to automatically generate and update work orders in IBM Maximo, SAP PM, or Oracle EAM. It also interfaces with ERP systems to check parts availability and trigger purchase requests for materials needed for newly prioritized high-risk work, ensuring the field plan is executable and financially tracked.