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Field Technician

Responding to outages and restoring service

Enhances✓ Available Now

What You Do Today

Get dispatched to outages — locate the fault, assess the damage, make repairs or reroute power, and restore service. Could be a blown fuse, a downed wire, or a transformer failure.

AI That Applies

AI analyzes smart meter data to precisely locate fault areas, predicts root cause based on weather conditions and equipment age, and recommends restoration sequences.

Technologies

How It Works

The system ingests smart meter data to precisely locate fault areas as its primary data source. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The output — restoration sequences — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

You arrive knowing more about what you'll find. AI narrows the fault location from 'somewhere on this feeder' to 'between pole 47 and pole 52.'

What Stays

Finding the actual problem, working safely around energized equipment, and making the repair in rain, ice, or darkness — that's all you.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for responding to outages and restoring service, understand your current state.

Map your current process: Document how responding to outages and restoring service works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Finding the actual problem, working safely around energized equipment, and making the repair in rain, ice, or darkness — that's all you. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support outage management systems tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long responding to outages and restoring service takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

What are the top 5 reasons customers contact us, and which of those could be resolved without a human?

They're prioritizing which operational processes to automate

your process improvement or lean lead

How do we currently measure service quality, and would AI-assisted responses change that measurement?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.