Building Information Modelling (BIM) is changing quantity surveying from document-based measurement toward model-based, data-rich commercial management. This review examines how BIM-enabled quantity surveying can automate quantity take-off, strengthen cost estimation, improve change control and support faster commercial decision-making, with particular relevance to Saudi Arabia’s rapidly expanding construction and infrastructure market. An integrative review approach was adopted, drawing on recent peer-reviewed research on 5D BIM, automated quantity take-off, model-based cost control, machine-learning-supported estimating, BIM adoption and Saudi construction management. The evidence indicates that BIM can improve measurement speed, traceability, visualization and consistency, but automation is not inherently reliable. Quantity outputs depend on model completeness, classification, level of information need, measurement rules, object parameters and disciplined model governance. The review therefore distinguishes between automated extraction and professionally validated measurement. It also shows that 5D BIM creates greater value when quantities are connected to cost libraries, work breakdown structures, procurement packages, change records, progress data and forecast-at-completion processes rather than being used only to produce an initial bill of quantities. A BIM-enabled Commercial Intelligence Framework is proposed that integrates model assurance, automated measurement, cost intelligence, change management, risk and dashboard-based decision support across project stages. In the Saudi context, this approach can support Vision 2030 objectives by improving transparency and commercial control on complex programs while developing digitally capable quantity-surveying teams. The paper concludes that the future quantity surveyor is not displaced by automation; instead, professional value shifts toward data assurance, commercial interpretation, scenario analysis, contractual judgment and governance of model-based decisions.