As variable renewable energy (VRE) rises, utilities must operate closer to limits without compromising reliability. This article presents a production-oriented framework for applying AI across the grid lifecycle. We detail (i) grid digital twins that fuse SCADA/PMU telemetry with physics-based models for contingency and “what-if” analysis; (ii) probabilistic load/VRE forecasting with calibrated uncertainty; (iii) orchestration of distributed energy resources (DER) and microgrids through multi-objective optimization and volt/VAR control; (iv) predictive maintenance that estimates remaining useful life (RUL) and reduces technical/non-technical losses; and (v) cyber-resilience for operational technology (OT) using anomaly detection and cyber-physical consequence modeling. A reference architecture couples time-series data platforms, feature stores, and MLOps with secure control loops to EMS/DMS.