AI Energy Audit Automation
The Problem
“Automate energy audits to cut waste faster”
Organizations face these key challenges:
High labor cost and long lead times for qualified auditors, limiting audit frequency and portfolio coverage
Data fragmentation across utility portals, BAS/BMS, submetering, CMMS, and spreadsheets causing delays and inconsistent baselines
Inconsistent recommendations and savings estimates across auditors, making capital prioritization and M&V difficult
Impact When Solved
The Shift
Human Does
- •Request utility, BAS/BMS, submeter, and equipment data from each facility
- •Conduct site visits, operator interviews, and manual equipment inspections
- •Build baselines and estimate savings in spreadsheets or engineering models
- •Prioritize measures and write audit reports for capital and operations teams
Automation
- •No significant AI support in the legacy audit workflow
Human Does
- •Approve audit scope, data access, and portfolio priorities
- •Review flagged anomalies, site-specific constraints, and low-confidence findings
- •Decide which recommendations move forward based on budget, risk, and operational impact
AI Handles
- •Ingest and normalize facility, utility, BAS/BMS, weather, occupancy, and production data
- •Detect waste patterns, benchmark sites, and estimate savings with uncertainty bounds
- •Generate prioritized recommendations, standardized audit reports, and work order drafts
- •Monitor post-audit performance and track measurement and verification against expected savings
Operating Intelligence
How AI Energy Audit Automation 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 audit scope, data access, or portfolio priorities without a human owner’s decision.[S2][S3]
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
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