Purpose: This systematic review aims to critically assess the current state of Building Information Modeling (BIM) and Industry Foundation Classes (IFC) data interoperability and semantic readiness for scalable integration with Artificial Intelligence (AI) applications across the Architecture, Engineering, and Construction (AEC) industry.

Design/Methodology/Approach: A Systematic Literature Review (SLR) was conducted, adhering to PRISMA guidelines, analyzing key research focused on the intersection of BIM, IFC, and AI. A conceptual framework categorizing AI-ready data into five pillars—Structural Consistency, Semantic Completeness, Geometric Fidelity, Temporal Coherence, and Contextual Richness—was developed to synthesize findings.

Findings: While AI applications, notably in predictive maintenance, risk assessment, and generative design, exhibit clear reliance on BIM/IFC data, the implementation is often impeded by significant data quality challenges. The core issue lies in the semantic gap: IFC, designed primarily for data exchange, frequently lacks the explicit, complete, and consistently structured information required for machine learning algorithms. Current approaches heavily rely on labor-intensive pre-processing, graph-based data transformations, or domain-specific custom property sets, compromising true interoperability. Furthermore, the handling of geometric and topological data within IFC frequently suffers from inaccuracies that render it unsuitable for highly sensitive computational tasks like automated quantity take-off and robot navigation.

Originality/Value: This review introduces a novel framework for assessing AI-ready BIM data and systematically maps the specific data requirements of various AI applications to the current limitations of the IFC schema. It provides a foundational critique, guiding future research toward developing the necessary semantic middleware, robust geometric validation tools, and standardization efforts for achieving seamless BIM-AI integration.