Skip to content

Field Technician

Performing preventive maintenance on equipment

Enhances✓ Available Now

What You Do Today

Inspect and maintain transformers, switches, regulators, capacitor banks, and other field equipment. Replace worn components before they fail and cause outages.

AI That Applies

AI prioritizes maintenance based on equipment health scores, predicts failure probability, and optimizes maintenance routes to minimize drive time.

Technologies

How It Works

The system ingests equipment health scores as its primary data source. Predictive models fit to historical outcome data identify which variables are the strongest leading indicators, then apply those weights to current inputs to generate forward-looking scores. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The physical inspection, testing, and repair work.

What Changes

Maintenance is predictive instead of calendar-based. AI sends you to the equipment most likely to fail, not just the equipment that's due for inspection.

What Stays

The physical inspection, testing, and repair work. You see, hear, and smell things that sensors can't detect.

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 performing preventive maintenance on equipment, understand your current state.

Map your current process: Document how performing preventive maintenance on equipment works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The physical inspection, testing, and repair work. 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 CMMS 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 performing preventive maintenance on equipment 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 data do we already have that could improve how we handle performing preventive maintenance on equipment?

They're prioritizing which operational processes to automate

your process improvement or lean lead

Who on our team has the deepest experience with performing preventive maintenance on equipment, and what tools are they already using?

They understand the workflow dependencies that AI tools need to respect

a frontline supervisor

If we brought in AI tools for performing preventive maintenance on equipment, what would we measure before and after to know it actually helped?

They see the daily reality that AI tools need to fit into

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.