AI readiness for buildings depends on data quality and control access
Which Buildings Are Artificial Intelligence-Ready? A Measurement-Based Assessment Framework for AI Question Answering and Actuation
Artificial Intelligence
Summary
Buildings with AI systems can answer questions and control operations, but many are not fully ready for this. The authors created a way to measure how ready a building is for AI by checking how much of its data can answer questions and control functions. They tested this on a fake office and some real buildings, finding that missing or unlinked data often limit readiness. This measurement helps prioritize building upgrades for better AI use.
What this means in practice
- •For building managers: Rank buildings by AI readiness to prioritize data and control system upgrades for smarter operations.
- •For building automation engineers: Assess which sensors and control points need integration to improve AI answer and actuation capabilities.
Authors
Wooyoung Jung
Abstract
Agentic artificial intelligence (AI) systems are becoming the interface to buildings, answering questions and controlling operations, but a building's readiness for them has not been systematically assessed. This study proposes a framework to quantify it. First, a building's knowledge graph sets two ceilings. The answerable-readiness ceiling is the share of operational questions its data could answer, and the actuation-readiness ceiling is the share of control actions it exposes. Second, a reference AI agent's accuracy on a fixed set of these questions shows how much of the ceilings is realized. On a simulated office, the agent realizes 0.62 of a 0.64 answerable ceiling, so missing data, not the AI, limit readiness, except in naming a fault's cause. Across 37 public real-building graphs, the median answerable ceiling is 0.16, and in 15 of 45, unlinked sensors lower it. The framework turns "is this building AI-ready?" into an auditable, ranked retrofit question.