Context: Every utility measures the difference between what is produced and what is ultimately accounted for, and that percentage reflects discipline at finding, prioritizing, and closing loss drivers before they become normal.

Challenges: Suspect feeders or zones are identified, but field verification slips, work orders are opened but corrective actions linger, and metering exceptions move across teams with diluted ownership, so the same areas reappear month after month.

Solution: The System Loss Guidance Agent, an AI agent built using Microsoft Copilot Studio, monitors zone-level loss trends as they worsen and intervenes during verification, repair, and adjustment steps, flagging stalled corrective tasks before the loss becomes embedded.

Impact: Utilities adopting this approach see earlier verification and faster escalation, with system losses improving as a consequence of tighter execution, not louder initiatives.