Purpose This study aims to investigate how artificial intelligence (AI) can enhance Lean Construction Management to achieve efficient and sustainable project delivery in Pakistan. It examines current practices, perceived benefits and the organizational, technical and cultural barriers that influence AI adoption within the construction sector. Design/methodology/approach A mixed-methods design was used, combining a systematic literature review, a Delphi-based expert consultation with 12 regional specialists, semi-structured interviews with 50 professionals and a quantitative survey of 125 valid responses. Statistical analyses, including correlation tests, were conducted to assess relationships between AI use, Lean outcomes and adoption factors. Findings Results reveal a strong positive correlation between AI tool usage and improved Lean outcomes, particularly in project planning, real-time monitoring and waste reduction. Predictive analytics and image-recognition technologies produce the greatest operational benefits. Despite growing interest, adoption remains limited due to high upfront costs, inadequate digital skills, data and integration challenges and organizational resistance to change. Capacity building and supportive policies are viewed as essential enablers. Originality/value This study provides the first empirical validation of AI-Lean integration in Pakistan’s construction sector. It demonstrates a strong positive correlation (r = 0.865) between AI tool usage and Lean outcomes, introduces a novel “fragmentation-of-adoption” concept specific to resource-constrained settings, and integrates the technology acceptance model, unified theory of acceptance and use of technology and diffusion of innovations theory into a unified adoption framework. These contributions offer a replicable evidence base for emerging economies, moving construction innovation literature beyond conceptual propositions to context-specific, empirically grounded practice.
Ali et al. (Sun,) studied this question.