Maria Wiedner MRICS is the Founder and CEO of Cambridge Finance and a member of RICS’ Commercial Property Professional Group Panel (PGP).
Is the commercial property sector embracing the potential uses of AI?
The sector is aware of AI’s potential – larger firms and forward-looking investors are experimenting with AI for valuations, asset management and market analysis.
However, compared with sectors such as finance, commercial property has been slower to embrace AI due to fragmented data, confidentiality concerns and cultural resistance to change. The sector risks falling behind unless adoption accelerates.
What opportunities and challenges has AI brought with it?
The opportunities include faster analysis of large datasets; automated valuations and cash flow modelling; improved forecasting of rents, yields and market cycles; and enhanced decision support through scenario analysis and sensitivity testing.
However, there are challenges too, such as data quality and consistency, as well as concerns about transparency and “black box” AI models (where the user isn’t able to see the internal workings of a system). Surveyors and analysts often lack AI and data science training, which creates a skills gap. There are also ethical and regulatory questions, especially around aligning outputs with RICS Red Book standards.
What is the general sentiment in commercial property towards AI?
Many surveying professionals recognise AI’s efficiency benefits but worry about accuracy, accountability and the risk of being replaced. The sentiment is mixed: younger professionals are more open to AI, while senior surveyors are cautious, focusing on professional judgement and client trust.
Are companies finding the integration of AI challenging?
Yes. Integration is proving difficult because property data is siloed across different platforms, formats and owners. AI requires clean, structured datasets to deliver value, but the industry still struggles with fragmented leases, valuation assumptions and transaction data. Many companies also lack in-house technical expertise and must rely on external providers, adding to costs and integration challenges.
Is AI streamlining certain processes in the industry? And is it improving the quality of data received?
AI can automate repetitive and data-heavy tasks such as lease abstraction, rent roll analysis, comparable evidence collation and initial valuation modelling. This can save significant time and reduce human error. However, interpretation, professional judgement and compliance with RICS standards cannot be fully automated. AI should be viewed as an assistant that supports surveyors in making informed, compliant decisions, rather than as a replacement for professional expertise.
Valuations can be streamlined, particularly for standardised asset classes like residential, logistics and retail portfolios. Automated valuation models (AVMs) speed up comparisons and produce indicative values. However, data quality is not necessarily improved by AI; rather, AI helps clean and structure existing datasets.
AI Training Is Critical
In RICS’ Surveying Skills Report 2025, members identified AI as the most critical emerging skill for the next five to ten years. Encouragingly, it also revealed the belief that the advance of technology (in the form of digital tools or data) will help surveyors provide greater value for clients.
59% of respondents say the use of advanced digital tools is the emerging skill that will be most important for surveyors in the next five to ten years.
RICS is committed to tackling the skills gap – and fast. It is taking on a driving role to galvanise impactful and accelerated collaboration between our membership, our industry, employers and professional bodies, academia and government. Our next step is to collectively plot a data-led and unified roadmap.
To read the results of the survey and to keep up-to-date on RICS’ progress in upskilling our membership, visit rics.org/surveyingskillsreport.