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Abstract
The use of artificial intelligence (AI), which is in constant development, has impacted various spheres of society, profoundly reshaping how organizations conduct their business and bringing significant challenges. Understanding the applications and maturity levels of AI technologies is crucial for organizations seeking to harness the full potential of these innovations. The systematic literature review (SLR) revealed that while there are some models for stages of AI application, none have been specifically adapted and evaluated for the facilities management (FM) environment. FM is an operations area responsible for integrating people, spaces, processes, and technologies in built environments, aiming for optimal functionality throughout the life cycle. Additionally, the SLR revealed that AI applications in FM often rely on legacy platforms such as supervisory control and data acquisition for building maintenance activities and building information modeling for construction, as a form of adaptive technologies. Given this gap and the importance of this sector to companies, it is essential to identify which AI technologies are used and at what stages they are. The results indicated various potential AI applications in FM, which can present different maturity stages, underscoring the need for adapted models so that managers can categorize and subsequently direct managerial efforts in pursuit of operational excellence. The goal is to provide organizations with practical and adaptable insights to assess, enhance, and optimize AI applications, thereby increasing efficiency, productivity, and innovation in their operations. From an academic perspective, the study aims to fill the research gap on AI typologies and maturity levels in the context of FM, contributing to the advancement of FM theory. Received: 25 June 2024 | Revised: 23 October 2024 | Accepted: 18 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Robson Quinello: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, supervision, Project administration. Paulo Tromboni de Souza Nascimento: Conceptualization.
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