AI Agentic System for Prioritizing Critical Care Customer Calls During Outages

Manual processes for identifying critical care customers during chaotic outages are error-prone and expose utilities to severe penalties. This workflow embeds regulatory rules directly into the call routing logic, ensuring every registered life-support customer is flagged, prioritized, and documented. The system creates an immutable audit trail of all actions—identification, queue jump, status update, and any escalation—providing defensible proof of compliance during PUC audits or legal discovery.

During a storm, call center queues can stretch to hours. For a customer on oxygen concentrators, every minute counts. This agentic system performs a real-time lookup against the Critical Care registry in the CIS the moment a call is received. If matched, the call is instantly routed to the front of the queue or to a dedicated high-priority agent pool. This slashes the time to first human contact from potentially 45+ minutes to under 10 seconds, directly impacting customer safety and outcomes.

When critical care customers are stuck in general queues, they call repeatedly, escalating to supervisors and clogging channels. By autonomously resolving their highest-priority need—accurate status and escalation—this workflow removes these high-friction, high-risk interactions from the bulk queue. This reduces average handle time for all other calls, decreases agent stress, and lowers the overall abandoned call rate by clearing the most urgent cases first.

Prioritization is only the first step. The workflow's orchestration layer triggers follow-on actions: an AI agent immediately provides a personalized restoration estimate by cross-referencing the customer's address with live OMS/SCADA data. It can also automatically notify field crews of a high-priority location or trigger a wellness check coordination with local emergency services if a prolonged outage is predicted, creating a closed-loop safety system.

The workflow transforms static critical care registries from a compliance checklist into a dynamic operational asset. By analyzing patterns—where these customers cluster, their typical outage durations, the outcomes of interventions—the system generates insights for grid hardening, targeted resilience investments, and refined emergency planning. This shifts the function from a cost center managing risk to a strategic unit improving overall system reliability.

Implementation is structured to mitigate risk. Phase 1 deploys a silent analyst agent that flags critical care calls and suggests prioritization to human agents, building trust in the logic. Phase 2 enables autonomous queue jumping with a mandatory, brief human agent confirmation. Phase 3 is full automation with continuous monitoring and a dedicated supervisor dashboard for real-time override. This ensures control is never ceded, and the system learns from human feedback.

This agent queries the Customer Information System (CIS) for the registered critical care flag and performs a real-time verification check. It cross-references with recent interaction history and, if required, initiates a secure document re-verification workflow. This ensures the priority list is accurate and audit-ready, a core regulatory requirement for protecting life-support customers.

The core logic layer that intercepts incoming voice and digital contacts. It ingests the verified critical care status from the CIS agent and applies business rules (e.g., outage duration, medical device type) to dynamically reprioritize the customer in the ACD/contact center queue. This component must integrate with platforms like Genesys, NICE, or Amazon Connect to enact the priority bump before the next agent assignment.

A low-latency API orchestration layer that unifies real-time data needed for informed handling: outage map and restoration estimate from the Outage Management System (OMS), smart meter last-gasp ping from the Meter Data Management System (MDMS), and the customer's service address from GIS. This hub provides a single, enriched context payload to both AI and human agents, eliminating system-switching delays during high-stress calls.

Upon connection, this agent provides the critical care customer with a validated status update and, if necessary, executes pre-approved escalation protocols. This may include creating a high-priority ticket in the field work management system, notifying a dedicated account manager via Teams/Slack, or triggering a proactive callback from a supervisor. All actions and rationale are logged to a immutable audit trail for regulatory review.

A critical governance component that wraps the entire workflow. It enforces strict access controls to medical data, applies decision boundaries to prevent over-escalation, and includes automated bias checks to ensure equitable treatment. This layer also manages failover procedures, such as defaulting to human triage if system confidence scores are low or if the CIS is unreachable, ensuring operational resilience.

Deployment typically follows a phased pilot: 1) Backend Integration: Building and testing the data hub against CIS/OMS staging environments. 2) Logic Pilot: Running the queue orchestrator in shadow mode to validate prioritization logic against historical call logs. 3) Limited Live Pilot: Activating the workflow for a single, trained agent group during non-storm conditions. 4) Scale & Storm Testing: Gradual expansion followed by controlled testing during minor outage events.