AI Utility Asset Management

The Problem

“Unplanned outages from aging, data-siloed utility assets”

Organizations face these key challenges:

Siloed asset, outage, and maintenance data (SCADA/AMI/OMS/GIS/EAM) prevents a unified, accurate view of asset health and risk

Reactive maintenance and emergency repairs drive high costs, safety risk, and customer-impacting outages, especially for aging distribution infrastructure

Capital planning and maintenance prioritization are manual, slow, and inconsistent, making it difficult to justify spend and meet reliability targets

Impact When Solved

Predict failures earlier: 30–60 days advance warning for high-risk assets on critical feeders to schedule planned workLower cost-to-serve: 10–20% fewer emergency truck rolls and 5–15% reduction in maintenance labor hours through condition-based schedulingImprove reliability and compliance: 5–15% SAIDI/SAIFI improvement on optimized circuits and reduced risk of reliability penalties and reputational damage

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0