AI Agentic Workflow for Predictive Maintenance Alerts and Customer Notification

This workflow automates the operational bottleneck of manually identifying and communicating with customers affected by upcoming maintenance or predicted asset failures. By ingesting risk scores from grid health models (e.g., transformer failure predictions) and cross-referencing them with GIS and CIS data, it identifies downstream customers and triggers personalized notifications. The business value is clear: it reduces inbound call volume during maintenance events, improves customer satisfaction through proactive communication, and prevents reputational damage by framing outages as planned, communicated service.

Implementation requires integrating the orchestrator with SCADA for real-time alerts, a GIS for network topology, and the CIS for customer data. Notifications are sent via Twilio or similar APIs, with logic to respect customer channel preferences and regulatory notice periods. Critical controls include approval gates for high-risk predictions, exception routing for customer replies (e.g., medical equipment concerns), and full audit trails in the OMS. This creates a closed-loop system where maintenance scheduling and customer communications are dynamically aligned, reducing operational friction and building trust.

This workflow automates the costly operational bottleneck of unplanned outages and reactive customer complaints by acting on predictive grid models. When a transformer failure risk score exceeds a threshold, an orchestrator agent ingests the prediction, identifies all downstream customers via the Geographic Information System (GIS), and triggers a pre-maintenance notification sequence. The operational upside comes from reducing outage duration, improving customer satisfaction scores (CSAT), and preventing costly emergency truck rolls through planned, communicated interventions.

Implementation requires integrating the orchestrator with SCADA for real-time telemetry, a transformer health model, and the Customer Information System (CIS). The architecture must include approval gates for notification templates, exception routing for critical-care customers, and observability into delivery status and customer sentiment. Rollout is sequenced by feeder, with controls to pause notifications during storm events or if field crew capacity is exceeded, ensuring the system augments rather than disrupts operations.

The workflow automates the critical gap between predictive grid analytics and customer experience. When a transformer failure risk score exceeds a threshold, the system must identify all downstream customers, determine the appropriate notification (e.g., planned outage, inspection advisory), and execute multi-channel delivery. This eliminates the manual, error-prone process of cross-referencing asset-customer maps and drafting communications, directly reducing customer confusion and complaint volume during service events. The operational upside comes from improved public perception, regulatory compliance for advance notice, and more efficient crew scheduling.

Implementation follows a de-risked, three-phase approach. Phase 1 builds the core orchestrator (LangGraph) integrating with the Asset Management System (e.g., SAP EAM) and GIS to generate internal alerts and customer lists. Phase 2 adds the CIS integration and a basic communications layer for email, with all notifications routed through a human review queue. Phase 3 introduces multi-channel delivery (SMS, IVR), dynamic message personalization, and automated approval gates for non-critical alerts. Each phase includes robust observability for message delivery rates and customer sentiment tracking, ensuring the system improves over time without operational surprises.

This workflow automates the bottleneck of manually correlating predictive maintenance schedules from asset health models with downstream customer impact. It ingests failure-risk scores for transformers or feeders from systems like GE Digital's APM or OSIsoft PI, identifies affected customers via GIS and CIS integrations, and autonomously generates and routes personalized notifications. The operational upside comes from reducing surprise outages, improving customer satisfaction scores (CSAT), and lowering call-center volume during planned work by setting accurate expectations through SMS, email, or IVR.

Implementation requires a LangGraph orchestrator to manage the stateful workflow, integrating with SCADA for real-time confirmation and a human-in-the-loop queue for critical care or complex cases. Controls include approval gates for message templates, audit trails for compliance (e.g., PUC notice requirements), and rollback logic if the maintenance event is canceled. A phased rollout starts with non-critical, scheduled vegetation management alerts before graduating to high-voltage equipment predictions, allowing for tuning of prediction accuracy and customer response handling.