Automation Workflow for Self-Service Outage Reporting and Estimated Restoration Time (ERT) Provisioning

This workflow automates the first, most critical customer interaction during a power outage. Instead of waiting on hold, customers report an outage via a web or mobile portal. An AI agent instantly validates the address against the Customer Information System (CIS), pings the smart meter via the Meter Data Management System (MDMS), and checks the Outage Management System (OMS) for existing tickets. This eliminates repetitive call-center intake and reduces false reports, directly lowering operational cost per incident. The system's core value is immediate, accurate status communication, which cuts inbound call surge by over 30% during major events.

Implementation integrates the orchestrator with CIS, OMS, MDMS, and workforce management APIs. The ERT engine analyzes crew assignments, fault location from GIS, and historical repair times. A confidence score gates automatic ERT publication; low-confidence predictions route to a human dispatcher. The workflow includes observability for data quality (e.g., meter connectivity) and exception handling for address mismatches. Rollout requires phased deployment, starting with non-critical circuits, and includes audit trails for regulatory compliance on communication accuracy.

This workflow automates the initial customer contact and status provisioning that traditionally floods call centers during outages. By connecting a customer-facing web/mobile interface to an orchestration layer, the system instantly validates addresses against the Outage Management System (OMS) and smart meter pings, then synthesizes crew location, fault isolation data, and historical repair times to generate a credible ERT. The operational upside comes from deflecting 40-60% of routine outage calls, reducing average handle time, and setting accurate customer expectations that improve satisfaction scores (CSAT) and reduce inbound inquiry volume.

Implementation requires integrating the customer portal with the OMS, Geographic Information System (GIS), Workforce Management System (WMS), and Meter Data Management System (MDMS) via a central orchestrator, built with frameworks like LangGraph for state management. Key controls include fallback logic for ERT confidence scoring, human-in-the-loop review for ambiguous cases, and real-time observability to monitor agent performance and data latency. The rollout must sequence from a single feeder pilot to full deployment, ensuring the data unification layer can handle peak volumes without degrading core system performance.

The initial phase focuses on core deflection and data integration. We implement a web/mobile portal where customers report outages via form or voice. An AI agent instantly validates the address against the CIS and queries the OMS for any active outage or crew assignment on that circuit. If a known outage exists, the system provides an immediate, data-driven Estimated Restoration Time (ERT) and creates a ticket, deflecting a Tier-1 support call. This requires building a real-time API orchestration layer between the portal, CIS, and OMS, with fallback logic for address matching exceptions.

Phase two introduces intelligence and proactive communication. The orchestrator agent now analyzes smart meter 'last-gasp' signals from the MDMS to confirm meter-level loss, differentiating between customer-side and grid failures to prevent unnecessary truck rolls. It also monitors crew progress, triggering automated SMS/IVR updates if the ERT changes by more than 30 minutes. This phase requires building stateful conversation memory, integrating with telecom APIs, and adding monitoring dashboards for exception routing and ERT accuracy tracking to ensure operational control.

This workflow automates the high-volume, repetitive task of initial outage validation and customer expectation setting, directly reducing call-center surge and average handle time. The operational upside comes from deflecting Tier-1 calls, providing accurate ERTs based on live crew and fault data, and creating a structured ticket in the OMS without manual dispatch coordination. Savings are realized through reduced labor costs, optimized field crew utilization, and improved customer satisfaction (CSAT) scores during service incidents.

Implementation requires a phased rollout, starting with a pilot on non-critical distribution circuits to validate data integration and ERT accuracy. Key controls include a human-in-the-loop approval gate for low-confidence ERTs, comprehensive audit logging of all data queries and decisions, and real-time monitoring of system performance against SLAs. Governance must define escalation paths for exceptions, regular model retraining on historical restoration data, and integration with existing change management and IT service management (ITSM) frameworks like ServiceNow for ticket lifecycle oversight.