From Algorithmic Accuracy to Organisational Value: A Systematic Overview of Artificial Intelligence-Enabled Defect Detection in Facilities Management

Artificial intelligence (AI) has become an important enabler of defect detection, fault diagnosis and predictive maintenance in Facilities Management (FM). Despite rapid technological advancements, existing studies predominantly emphasise algorithmic accuracy while giving comparatively less attention to the organisational, operational and managerial conditions required to translate AI predictions into sustainable FM value. This paper aims to provide a systematic overview of AI-enabled defect detection in FM and to develop an integrative Technology–Process–Management (TPM) perspective for understanding how AI capabilities are transformed into organisational value. The study adopts a systematic overview methodology based on a previously completed systematic literature review covering publications from January 2019 to March 2025. The source review identified 94 eligible studies through a transparent PRISMA-based review process. Evidence was synthesised using a structured narrative approach by categorising findings into three analytical dimensions: Technology, Process and Management. The review identifies five major AI technology families, namely conventional machine learning, deep learning, computer vision, semantic and language-enabled methods, and digital twins. The findings demonstrate that high predictive accuracy alone is insufficient to ensure successful implementation in FM. Instead, AI effectiveness depends on data quality, interoperability, workflow integration, governance, organisational readiness, operator participation and lifecycle economic considerations. Based on these findings, the paper proposes a Technology–Process–Management (TPM) framework that conceptualises the relationships between implementation readiness, operational mechanisms and multi-level organisational outcomes. The review also outlines future research priorities, including prospective multi-site validation, human–AI collaboration, interoperability, lifecycle economic evaluation and implementation-oriented reporting. The findings suggest that AI-enabled defect detection should be viewed as a socio-technical transformation rather than solely a technological innovation. For FM practitioners, successful AI adoption requires aligning technological capability with organisational processes, governance structures and maintenance decision-making to achieve sustainable operational value.

Artificial intelligence; Facilities management; Defect detection

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