| Description | Artificial Intelligence (AI) is rapidly reshaping professional practice across the construction industry, with Quantity Surveying (QS) frequently identified as one of the occupations most exposed to AI because many of its core activities involve structured, data-intensive and rule-based processes. However, there remains limited empirical evidence to support these claims. This study therefore sets out to investigate AI Exposure in QS practice, focusing on identifying key application areas, opportunities, challenges and implementation strategies, and exploring the future evolution of the profession. The study adopts an exploratory interpretivist approach through qualitative research methods. Semi-structured interviews with experienced QS professionals and academics were conducted and thematically analysed. Findings were interpreted through the RICS competency framework by grouping professional competencies into five knowledge domains: Economic, Technical, Legal, Managerial and Technological Knowledge. The findings reveal that AI adoption within QS remains uneven and is concentrated primarily within routine, structured and data-intensive activities associated with Economic and Technical Knowledge. In contrast, competencies founded on Legal and Managerial Knowledge remain comparatively less susceptible to automation due to their reliance on professional judgement, contractual accountability and client engagement, while Technological Knowledge, particularly data management, emerges as the critical enabler of AI integration. The study further identifies that the principal barriers to adoption are organisational readiness, digital maturity and governance rather than limitations of AI itself. The study makes three principal contributions. First, it demonstrates that AI exposure should be understood at the competency level rather than the occupational level. Second, it develops an AI Transformation Framework that maps AI exposure against emerging professional value across QS knowledge domains. Finally, it provides an evidence-based roadmap for professional bodies, educators and industry to redefine competency requirements and strategically position the QS profession for an increasingly AI-enabled construction sector. |
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