A Multi-Criteria-Driven Roadmap for Artificial Intelligence Adoption in Construction: A UK Study

The construction sector is vital to the national economy but continues to suffer from low productivity, cost overruns, and project delays. Although Artificial Intelligence (AI) has significant potential to address these inefficiencies and automate workflows, its adoption remains slow. This study evaluates the UK construction industry's readiness for AI by identifying the factors driving adoption resistance. A mixed-methods approach was employed, combining a cross-sectional survey with Spearman’s rank-order correlation, Analytic Hierarchy Process (AHP), and Fuzzy DEMATEL analysis. Results reveal a substantial gap between workforce willingness and organisational capability. While employees show a strong desire to upskill, 76.47% require support to use AI effectively, and 41.80% lack the skills to structure existing data. Fuzzy DEMATEL findings indicate that organisational readiness and funding constraints are the primary causes of resistance, rather than the technology itself. To address these barriers, the study proposes a dual-structure AI adoption roadmap. Although the industry generates sufficient data to train AI models, much of it remains unstructured, fragmented, and historically inconsistent. The roadmap recommends embedding internal data scientists to organise legacy data, support workforce development, and enable sustainable AI integration.