AI Utility Customer & Asset Analytics
Advanced analytics for utility customer insights, asset management optimization, and schedule optimization.
The Problem
“Unifying customer and asset insights for reliability”
Organizations face these key challenges:
Siloed operational and customer systems prevent a single, trusted view of grid conditions and customer experience
Reactive maintenance and outage response lead to avoidable failures, longer restoration times, and inefficient crew utilization
Limited ability to detect and prioritize losses, meter issues, and high-bill drivers at scale, increasing revenue leakage and call center load
Impact When Solved
The Shift
Human Does
- •Review separate outage, meter, asset, and customer reports to identify service and maintenance issues
- •Prioritize maintenance, vegetation, and field work using time-based schedules, alarms, and crew judgment
- •Investigate outages and high-bill complaints after customer calls or exception reports
- •Target customer outreach using broad segments such as rate class, geography, or recent events
Automation
- •Apply fixed dashboard rules and threshold alerts within existing operational systems
- •Produce periodic exception lists for outages, losses, billing anomalies, and asset conditions
- •Summarize historical performance trends from manual extracts and engineering studies
Human Does
- •Approve maintenance, switching, dispatch, and replacement priorities based on risk and operational constraints
- •Review high-risk outage, loss, and customer cases and decide actions for exceptions or sensitive accounts
- •Authorize proactive customer communications, field interventions, and revenue recovery actions
AI Handles
- •Fuse customer, outage, meter, asset, weather, and work history data into a unified risk and experience view
- •Predict asset failure, outage likelihood, likely fault location, and non-technical loss or billing exception risk
- •Recommend optimized crew schedules, maintenance timing, field visit prioritization, and targeted switching actions
- •Trigger proactive customer insights and case triage for high-bill drivers, outage impacts, and remote resolution opportunities
Operating Intelligence
How AI Utility Customer & Asset Analytics runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve switching, dispatch, maintenance, replacement, or customer intervention actions without a designated utility operator or planner making the final decision. [S1] [S2]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AI Utility Customer & Asset Analytics implementations:
Key Players
Companies actively working on AI Utility Customer & Asset Analytics solutions:
Real-World Use Cases
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