Hoppa: Tackling information overload in the built environment sector

This case study explores how information management technology company Hoppa is applying responsible AI to address one of the most persistent challenges in the built environment sector: the inability to find and act on important information relating to assets such as buildings or infrastructure. By automating the organisation, classification and governance of asset data, Hoppa helps infrastructure owners and their supply chains get the most out of stored information and reduce associated risks – producing a significant positive shift in how the industry applies digital insights in the real world. This case study is published under The Turing Way Practitioners Hub 2025-26 Cohort - case study series. The Practitioners Hub is a The Turing Way project that works with experts from partnering organisations to promote data science best practices. Key takeaways Poor information governance in the built environment sector creates safety, compliance, financial and resourcing risks that can be managed and reduced with the help of AI. AI enables information management at volumes and speeds that may be infeasible with human effort alone. The biggest opportunities are often in designing interoperable platforms that sit on top of existing systems, rather than introducing standalone new tools. Human-in-the-loop design is essential for the responsible implementation of AI in safety-critical settings. Lowering the cost of data classification through technological advances can drive improvements within information-heavy industries such as the construction sector and enable secure, more selective data sharing across supply chains. AI works best – and is easiest to pitch to end users – when applied successfully to real-world problems. Reliability, repeatability and utility always beat flashiness and “AI for the sake of AI”. AI should be seen as an opportunity and not just talked about in the context of its risks. While careful consideration of safety and ethics is essential, an overly negative framing can obscure AI’s potential to address longstanding challenges and improve outcomes in traditional sectors.