Despite significant advances in digital surveying technologies, Heritage Building Information Modelling (HBIM) remains constrained by labour-intensive processing, fragmented classification systems, and limited standardised pathways for integrating Artificial Intelligence (AI). The absence of a systematic and standardised roadmap for AI adoption has limited both academic progress and industrial implementation. This paper proposes a comprehensive AI implementation roadmap for automated HBIM, developed through iterative research and empirical experimentation on UK heritage case studies. Building upon Design Science Research (DSR) principles, the roadmap delineates the critical dependencies among classification systems, data acquisition, algorithmic segmentation, and geometry generation, while embedding the Five HBIM Motivations, revival, restoration, restitution, retrofit, and resilience, as the primary structuring device for project intent. The study synthesises experimental findings into a practical, ISO 19650-aligned framework capable of guiding AI integration at both strategic and operational levels. An AI-enabled HBIM Execution Plan is presented as an implementation mechanism, enabling project teams to align digital workflows with heritage objectives, classification structures, and computational capacities. Evaluation through expert interviews confirms the roadmap’s feasibility, adaptability, and potential to enhance documentation efficiency, semantic richness, and interdisciplinary collaboration. The paper contributes a robust, scalable, and standards-compliant methodology for embedding AI in HBIM, offering a pivotal reference for the UK cultural heritage sector and a template for international replication.
Gil et al. (2026) studied this question.