[
    {
        "id": "osp-15436",
        "type": "article-journal",
        "title": "Which Buildings Are Artificial Intelligence-Ready? A Measurement-Based Assessment Framework for AI Question Answering and Actuation",
        "author": [
            {
                "family": "Jung",
                "given": "Wooyoung"
            }
        ],
        "URL": "https://omanscience.com/en/articles/which-buildings-are-artificial-intelligence-ready-a-measurement-based-assessment-framework-for-ai-question-answering-and-actuation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]