With ever-growing volumes of data becoming available, the built environment now has a powerful new resource to leverage. This year, the Built Environment Technology Expert Panel is delving into how this data can be best utilised, examining everything from foundational collection and ethical governance to its capacity for reshaping professional practices and generating new value. This summary distills key insights from those discussions, highlighting opportunities and challenges as we move towards a more data-informed future.

We began by examining the fundamental importance of continual data gathering in the built environment. Discussions highlighted the urgent need for systems that can grade data quality and extract valuable insights from historical records. We identified numerous missed opportunities—economic, social, and operational—that stem from inadequate data practices, impacting everything from taxation to property values.

A significant concern revolved around trust and regulation. For data to be truly effective, there must be transparency, ethical governance, and clear guidelines, regardless of whether data management models are centralised or decentralised. Regulatory action, particularly in areas like planning and construction, will be crucial to enforce data standards.

The conversation also turned to the monetisation of data. We explored mechanisms such as data marketplaces and incentives to encourage sharing, alongside the necessity for a structured business model to support these initiatives. Issues of data ownership and liability were central, acknowledging legal constraints, the risks associated with incomplete data, and the need for regular updates, such as annual fire assessments. Ultimately, we explored the broader societal impacts, stressing the goals of healthier living and increased property value while also warning against the risk of losing control to private businesses. As a tangible first step, a London-wide pilot initiative was proposed.

Building on these foundational discussions, our workgroup is actively developing practical solutions to bridge the persistent data gap in built environment practice. Our core focus is identifying essential building data sets that should be consistently captured throughout each stage of the RIBA Plan of Work.

Our goal is to propose a structured overlay that defines a standardised schema and scope for data collection across all project phases. By embedding this framework into practice, we aim to foster more consistent and interoperable records across the industry. This consistency won't just improve project delivery and collaboration; it will also lay the groundwork for anonymised datasets to be shared publicly. Such datasets would be invaluable for academic research, industry benchmarking, and evidence-based policymaking, especially within a complex urban environment like London.

We envision a future where data is treated as a critical asset from project inception to post-occupancy, enabling smarter, more sustainable decisions at every level. As part of our proposal, we are developing minimal data schemas for architecture, structures, and planning, supported by a policy-driven pipeline for data collection and sharing. This forms a robust foundation that we hope will evolve into a broader, cross-disciplinary schema for the wider built environment industry.

The discussions highlighted that the built environment profession is undergoing a profound shift. By 2040, we envision a future shaped by multidisciplinary collaboration and AI-integrated services. Traditional architectural roles are evolving into more data-driven, creative positions, with firms increasingly incorporating non-classically trained architects and data scientists. Junior roles involving repetitive tasks are most susceptible to AI replacement—a shift comparable to how GIS transformed cartography or how the internet disrupted journalism.

A major opportunity lies in monetising intellectual property (IP). Designs, processes, and methods can be transformed into encodable, AI-readable assets, creating new revenue models beyond traditional fee-for-service structures. The profession must move towards generating "AI-friendly IP" such as parametric design systems, toolkits, and datasets that can be reused and scaled.

To realise this, two critical next steps were identified: First, clearly define what makes IP usable by AI—its required form, structure, and logic. Second, explore what a sustainable, future-ready commercial model looks like when IP, not just service, drives value. A diagram mapping the intersection of commercial models and IP utility was proposed to clarify these ideas and guide long-term strategic planning.

This is an exciting time for the built environment. By collaboratively addressing data gaps, embracing new technologies, and rethinking traditional models, we can lay the groundwork for a more efficient, sustainable, and prosperous future.

What are your thoughts on how data might transform the built environment in the coming years?