AI Workflow for Automated Storm Response and Escalated Incident Management

Manual monitoring of NWS feeds and internal telemetry to declare a storm event is a high-latency, error-prone process that delays coordinated response. A custom workflow uses an orchestrator agent to ingest multiple weather data streams, cross-reference them with grid sensor thresholds, and automatically declare a storm event in the OMS. This triggers pre-configured response protocols—like adjusting triage rules and activating mass communication templates—within seconds, not hours. The savings come from eliminating the managerial scramble and ensuring a consistent, auditable start to every major event.

During a storm, determining when and how to escalate to an Emergency Operations Center (EOC) is often ad-hoc, based on individual judgment. A custom workflow embeds escalation logic directly into the orchestrator. Based on predefined thresholds for outage counts, critical infrastructure impact, or public safety risk, the system automatically notifies EOC personnel, populates situation reports, and establishes dedicated communication channels. This removes guesswork, ensures regulatory and internal policy compliance, and allows EOC staff to focus on high-level coordination instead of situational awareness gathering.

Manually crafting and sending updates to hundreds of thousands of affected customers is impossible during a storm surge. This workflow deploys specialized communication agents that are triggered by the storm declaration. These agents pull dynamic restoration estimates from the OMS, segment audiences (e.g., critical care, medical facilities, general population), and execute multi-channel communication plans via SMS, IVR, and email using pre-approved templates. This reduces inbound call volume by 30-50%, manages public perception proactively, and frees corporate communications teams for strategic media interaction.

Storm conditions rapidly change crew availability, travel times, and repair priorities. A static dispatch plan becomes obsolete quickly. This workflow integrates a resource orchestration agent that continuously ingests crew GPS, asset damage assessments from field apps, and revised outage predictions. It dynamically re-optimizes work orders and crew assignments, factoring in safety zones and parts availability. This improves crew utilization by 15-20%, reduces non-productive travel time, and accelerates overall restoration by ensuring the right resources are sent to the highest-priority faults first.

Post-storm regulatory reporting (e.g., for PUCs on SAIDI/CAIDI) is a manual, labor-intensive process prone to inconsistencies. This workflow is built with an audit and reporting agent that time-stamps and logs every automated action—from storm declaration and communication sent to crew dispatch and EOC handoff. It continuously compiles key reliability metrics from the raw event data. At the close of the event, it generates draft regulatory reports and restoration summaries, creating a complete, defensible record that reduces compliance labor and audit risk.

A production-grade build follows a phased approach. Phase 1 automates storm detection and internal alerting, with a human-in-the-loop approval gate for declaration. Phase 2 adds automated, read-only customer notifications. Phase 3 introduces dynamic crew dispatch recommendations. Phase 4 enables full, closed-loop autonomous operation with supervisory dashboards for override. Each phase integrates with core systems (OMS, GIS, CIS, WMS) via a unified API layer or middleware. Critical to success are exception-handling queues for edge cases and real-time observability tools to monitor agent decisions and system health during live events.

An autonomous agent monitors National Weather Service (NWS) feeds, Doppler radar, and lightning strike networks. Using predefined severity thresholds (e.g., wind speed, hail size, tornado warnings), it automatically declares a storm event in the Outage Management System (OMS). This triggers a shift from standard to storm-mode triage protocols, pre-emptively scaling communication templates and adjusting crew dispatch logic before the first outage is reported.

Upon event declaration, a dedicated communication agent pulls pre-approved message templates tailored by customer segment (e.g., critical care, general residential, business). It orchestrates high-volume, personalized outbound notifications via SMS, IVR, and email, using the Customer Information System (CIS) for contact data. The agent dynamically updates messages with restoration estimates sourced from the OMS and field crew GPS, reducing inbound call surge by 30-50% during major incidents.

A coordination agent acts as a digital liaison between the automated system and the human-led EOC. It synthesizes real-time data from SCADA, damage prediction models, and crew status into a unified situational dashboard. The agent automatically generates briefings, highlights areas requiring manual override (e.g., complex cascading failures), and routes high-priority decisions to designated EOC managers via Slack, Teams, or dedicated consoles, standardizing response and reducing managerial chaos.

This component upgrades standard outage triage logic for storm conditions. Ingesting a flood of smart meter 'last-gasp' signals, customer calls, and social media mentions, it uses GIS feeder models to cluster and validate outages. It then interfaces with Workforce Management (WFM) and transportation systems to optimize crew dispatch, prioritizing critical infrastructure and largest customer blocks, while dynamically rerouting crews based on road closures and evolving fault locations.

After storm mode is deactivated, an analytics agent automatically compiles the incident timeline, customer notifications sent, restoration durations, and crew metrics. It formats this data into mandatory regulatory reports (e.g., for PUC filings on SAIDI/SAIFI) and internal after-action reviews. This eliminates weeks of manual data aggregation, ensures reporting accuracy, and creates a closed feedback loop to refine thresholds and protocols for future events.

The foundational architecture layer. It uses a unified API gateway and stream-processing platform (e.g., Apache Kafka) to connect the OMS, CIS, GIS, MDMS, WFM, and NWS feeds. This hub provides a single source of truth for all agents, handles data schema translation, and includes comprehensive logging, tracing, and alerting. This ensures the workflow is auditable, can gracefully degrade if a backend system fails, and allows for secure, scalable data flow across legacy and modern systems.