Integrating Artificial Intelligence into Quantity Surveying Cost Management: Framework Development and Nigerian Context Analysis

Artificial intelligence (AI) presents transformative opportunities for quantity surveying cost management, but the development of AI applications in this field must be contextualised to the institutional, data, and practice environments of specific national contexts. This study investigates the integration of AI into quantity surveying cost management in Nigeria, developing an original implementation framework grounded in empirical analysis of Nigerian practice conditions. A pragmatist paradigm underpins a multi-method design combining a systematic review of 85 AI-in-construction studies, a national survey of 260 quantity surveying practitioners and technology stakeholders, prototype development of three AI cost management tools tested in collaboration with 15 Nigerian firms, and expert validation through a Delphi panel of 40 specialists. Findings demonstrate that AI applications in automated measurement, cost prediction, and procurement analytics offer significant potential value for Nigerian practice, but implementation is constrained by data quality deficits, absence of Nigerian training datasets, limited software infrastructure, and inadequate AI literacy. The prototype AI cost prediction tool, trained on 250 Nigerian project records, achieves a mean absolute percentage error of 9.2%, demonstrating feasibility even with limited Nigerian data. The Nigeria AI Cost Management Integration Framework (NAIF) is developed, addressing data infrastructure, professional governance, curriculum reform, and ethical use dimensions. This study makes an original applied and theoretical contribution to AI adoption in construction cost management in the Global South. Keywords: artificial intelligence, cost management, quantity surveying, Nigeria, framework