Building Research & Information

AI in the Built Environment: Opportunities, Risks, and Future Directions

Artificial intelligence (AI) is rapidly reshaping the built environment sector, transforming how we plan, design, construct, manage, and operate buildings and infrastructure. From construction to property and real estate, facilities management, and urban planning, AI technologies, including generative AI, computer vision, and natural language processing, alongside predictive modelling, simulations and optimisation tools are driving new efficiencies and capabilities.

Despite these advances, the increasing adoption of AI brings significant challenges. Concerns regarding data integrity, algorithmic bias, privacy, governance and the transparency of AI-assisted decision-making remain central to debate among professionals and sectors. Importantly, AI should be recognised to augment, not replace professional expertise, judgement, and ethical responsibility in the built environment. The sector’s global drive toward digitalisation has produced unprecedented volumes of data, and AI now plays a pivotal role in extracting meaningful, actionable insights. Applications span energy optimisation, smart building control, predictive maintenance, supply chain management, automated design generation, construction safety monitoring, spatial analytics, and ESG reporting. As these technologies mature, they present both opportunities for innovation and pressing questions about their implications for industry practice, governance, and the future of work.

As AI becomes increasingly embedded throughout the building lifecycle, understanding its opportunities, risks, and impacts is essential. This Special Issue seeks to advance critical insights into how AI is transforming the built environment and to explore pathways for responsible, ethical, and high-impact adoption.

This Special Issue invites high-quality, original contributions that examine the use, impact, risks, and future potential of AI, including machine learning and deep learning, across the built environment. Topics of interest include, but are not limited to:

• AI in construction: automation, robotics, quality control, progress monitoring, risk and safety analytics
• AI in property and real estate: valuation models, market intelligence, customer engagement, asset management, predictive analytics
• Smart buildings and facilities management: IoT-enabled optimisation, energy management, predictive maintenance, AI-driven knowledge systems
• AI in urban planning and infrastructure: spatial modelling, forecasting, environmental assessment, transport and mobility analytics
• Design and engineering applications: generative design, simulation, optimisation, digital twins, parametric modelling
• Explainable AI (XAI): AI governance, ethics, and regulation: transparency, bias, data integrity, privacy, and responsible GenAI in the built environment
• AI for sustainability and ESG: carbon modelling, resource optimisation, lifecycle assessment
• Security and risk management: computer vision for surveillance, access control, anomaly detection
• Multimodal and agentic AI: advanced AI systems applied to complex built environment challenges.
• Human-AI collaboration: professional adoption, skills, workflows, and organisational change.
• The challenge of educating and training built environment professionals in AI.