AI in Utilities: From Forecasts to Autonomous Operations

Artificial Intelligence is becoming the control layer of modern utilities. Load and price forecasting, non‑technical loss detection, predictive maintenance, and vegetation management via computer vision are improving reliability and reducing cost. AMI and IoT streams feed models that predict faults before they cascade into outages.

In grid operations, AI supports optimal dispatch, DER orchestration, voltage/VAR control, and dynamic line rating. Customer-facing AI enhances collections, churn prediction, and next‑best‑action offers such as efficiency tips or tariff migration. For contact centers, virtual agents cut wait times while preserving CSAT.

ROI depends on data governance and integration. Many utilities sit on siloed SCADA, EAM, CIS, and outage systems that require data engineering, feature stores, and cybersecurity to become AI‑ready. Explainability and model risk management are crucial for regulators and boards.

Scaling AI is as much an operating‑model question as a technical one: product squads, MLOps, and continuous monitoring keep models relevant as the grid and weather patterns change. Training field crews to trust and use AI recommendations closes the last‑mile gap.