Climate Risk Assessment
The Problem
“You can’t manage climate risk when building data is siloed and failures are reactive”
Organizations face these key challenges:
Energy and BAS data, work orders, and vendor reports live in separate systems—no single view of risk by building
Engineers chase alarms and comfort complaints while root causes (drift, bad schedules, failing components) go unaddressed
Maintenance is preventive-by-calendar or break/fix—costly surprises during heat waves, cold snaps, and peak demand
Portfolio reporting for insurers, lenders, and executives is manual, slow, and inconsistent across properties
Impact When Solved
The Shift
Human Does
- •Manually review utility bills, BAS trends, and alarm histories to spot issues
- •Read vendor PDFs/audit reports and summarize recommendations
- •Perform periodic site walks and investigate complaints after they occur
- •Create risk and performance reports in spreadsheets/slide decks
Automation
- •Basic rules/threshold alarms in BMS (often noisy and non-contextual)
- •Scheduled preventive maintenance plans in CMMS
- •Static dashboards that require experts to interpret
Human Does
- •Set business priorities (risk tolerance, comfort targets, budget constraints)
- •Approve and schedule corrective actions (retro-commissioning, repairs, setpoint changes)
- •Handle exceptions, safety-critical decisions, and vendor management
AI Handles
- •Ingest and normalize data from BMS/BAS, meters, CMMS, IoT sensors, and documents
- •Detect energy waste patterns (schedule drift, simultaneous heat/cool, economizer faults) and explain causes in natural language
- •Predict equipment failures and recommend prioritized maintenance actions with expected impact
- •Continuously score buildings for operational climate risk and auto-generate audit-ready reports for stakeholders
Operating Intelligence
How it works
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 or schedule repairs, setpoint changes, or retro-commissioning work without review by a facility manager, property operations lead, or building engineer. [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
Technologies
Technologies commonly used in Climate Risk Assessment implementations:
Key Players
Companies actively working on Climate Risk Assessment solutions:
Real-World Use Cases
Predictive spare-parts and maintenance scheduling for critical building systems
AI predicts which parts a building will likely need soon, so managers can stock the right items and schedule repairs at the least disruptive time.
AI-assisted building operations monitoring and decision support for senior living facilities
AI watches building equipment in senior living communities, spots issues early, and helps staff decide what to fix before residents are affected.
GPT-4–Enabled Data Mining for Building Energy Management
This is like giving a large commercial building a very smart assistant that can read all its meters, logs, and reports, then explain where energy is being wasted and how to fix it—using natural language instead of dense engineering dashboards.