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Operationalizing AI for Faster, Smarter Outage Restoration
Utilities are under growing pressure from extreme weather, workforce constraints, and rising customer expectations — making AI a critical enabler of operational performance during grid emergencies. The highest-value AI use cases are those that move beyond pilots to deliver measurable improvements in reliability, response speed, and customer outcomes.
Among these, AI-driven Outage Restoration Optimization stands out as a priority. Traditional restoration planning relies heavily on manual forecasting and static resource allocation, limiting a utility’s ability to anticipate storm impacts and deploy crews effectively.
This session focuses on integrating AI-driven predictive modeling into the restoration workflow by combining weather and incident forecasts with historical outage and workforce data. The result is clearer foresight into storm severity and location, improved manpower planning, and faster, more coordinated restoration. By enabling proactive, data-driven decisions before and during storms, utilities can reduce outage duration, improve operational efficiency, and strengthen customer trust when it matters most.
Session Sponsored by Guidehouse
