Papers for

building managers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

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

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.

Tue 6 OctArtificial Intelligence

The gist

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.

Open → 2610.09119v1