Critical Success Factors for the Adoption of Artificial Intelligence in Facilities Management: Second-Order Systematic Review

The application of Artificial Intelligence (AI) in Facilities Management (FM) has grown significantly, driven by the pursuit of resource optimization, automation, and operational efficiency.However, specialized literature remains in an early stage, hindering a comprehensive understanding of the Critical Success Factors (CSFs) that influence the adoption of this technology in the sector.To address this gap, this study conducts a Second-Order Systematic Review (SOSR) to identify and consolidate the main CSFs associated with the adoption of AI in FM.The analysis is grounded in a conceptual model based on the TOEH theoretical framework (Technology-Organization-Environment-Human), which enables a multidimensional reading of both facilitators and barriers.Key challenges include system interoperability, data quality, the reliability of AI models, and building typology diversity, issues exacerbated by technological fragmentation and a lack of standardization, which hinder integrated solutions.Regulatory concerns regarding data privacy and governance, combined with limited workforce training, further hinder large-scale adoption.Conversely, innovations such as digital twins, explainable AI, robotics, and cybersecurity for smart buildings emerge as drivers of transformation.The findings provide valuable insights for FM managers, technology providers, and policymakers, contributing to the development of effective strategies for integrating AI in the Facilities Management sector.