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Energy Efficiency Manager

Conducting energy audits and technical assessments

Enhances✓ Available Now

What You Do Today

Perform or oversee energy audits of customer facilities — identifying efficiency opportunities, calculating savings, and making recommendations that customers can act on.

AI That Applies

AI pre-screens facilities using billing data to estimate likely opportunities before the audit, generates audit reports from field data, and calculates savings from measure libraries.

Technologies

How It Works

The system ingests billing data to estimate likely opportunities before the audit as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — audit reports from field data — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Audit preparation is data-driven. AI identifies the most likely opportunities before you walk through the door, so audits are more targeted and efficient.

What Stays

The on-site assessment — seeing the equipment, understanding the operations, and identifying opportunities that data alone can't reveal.

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 conducting energy audits and technical assessments, understand your current state.

Map your current process: Document how conducting energy audits and technical assessments 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 on-site assessment — seeing the equipment, understanding the operations, and identifying opportunities that data alone can't reveal. 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 audit software 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 conducting energy audits and technical assessments 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's our current capability gap in conducting energy audits and technical assessments — and is it a people problem, a tools problem, or a process problem?

They're prioritizing which operational processes to automate

your process improvement or lean lead

How would our regulator react to AI-assisted compliance monitoring — have we asked?

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.