Exploring data readiness for AI adoption in the construction industry

A primary driver for AI adoption in construction is enhanced productivity and efficiency. Building Information Modelling (BIM) may be observed as a convenient foundation for AI adoption offering a common source of readily available and structured data. The concept of BIM data readiness as a prerequisite for AI adoption in construction is explored through a Systematic Literature Review. The initial search revealed 33 potential sources of relevant literature. After careful filtering, screening and review, 9 articles were identified and studied in detail. The findings reveal several fundamental data readiness components for AI adoption. First, BIM-AI integration requires robust data acquisition, interoperability and development practices to ensure data quality; second, awareness of distinct project life-cycle requirements and third, the necessity of customised industry standards that would steer and inform AI adoption, addressing data security, ethics and sustainability issues. Though most papers recognised and emphasize the importance of data management strategies, key areas of ethics and level of development received scant attention. Understanding BIM as a source of structured data offers a pragmatic milestone in the journey towards AI adoption in construction.