Blockchain Adoption in AI-Enabled Quantity Surveying: Transforming Construction Cost Management

Résumé

The construction industry continues to experience persistent challenges in cost management due to fragmented information systems, manual documentation processes, and limited transparency in financial transactions. Within this context, Quantity Surveying (QS) practice remains heavily dependent on document-driven workflows such as spreadsheets, emails, and isolated cost management systems. These practices often lead to data inconsistencies, delayed valuations, disputes over measurements and variations, and reduced trust among project stakeholders. Despite the growing adoption of digital technologies such as Building Information Modelling (BIM) and cloud collaboration platforms, issues of data integrity, traceability, and secure automation remain inadequately addressed. This study aims to investigate how blockchain technology can be adopted within AI-enabled Quantity Surveying services to enhance transparency, improve cost-management efficiency, and support secure automation of financial processes in construction projects. The research adopts a systematic literature review methodology, analysing recent studies on blockchain, artificial intelligence, and digital transformation in construction cost management. The collected literature is examined through thematic analysis to identify key technological capabilities, adoption challenges, and integration mechanisms. The study proposes a conceptual adoption framework that integrates blockchain-based distributed ledgers, smart contracts, and AI-driven analytics to support transparent cost verification, automated valuation processes, and tamper-proof financial records. The findings highlight the potential of blockchain-enabled QS services to enhance trust, accountability, and efficiency across construction cost management workflows. However, challenges relating to data governance, interoperability, regulatory acceptance, and organisational readiness remain key limitations for large-scale implementation.

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