Artificial intelligence adoption across construction and commercial property has accelerated in the past year, but firms are struggling to turn pilot studies into routine practice, according to research from the Royal Institution of Chartered Surveyors (RICS).
RICS called on the industry to "lead not follow" as its report found that while experimentation with AI tools has become common, few organisations have embedded them into everyday workflows. The findings suggest the gap between testing and implementation remains the main barrier to real productivity gains.
Pilot projects outpace integration
The RICS research indicates that property professionals are using AI for tasks such as document analysis, valuation modelling, and asset management. Yet the translation from successful pilot to standard operating procedure is slow, with many firms unable to scale beyond initial test cases.
That mismatch matters for a sector under pressure to improve efficiency. Construction and real estate operations involve large volumes of data across design, planning, leasing, and facilities management - exactly the kind of work where AI tools can reduce time spent on manual tasks.
Industry asked to take the lead
The report positions RICS members as the group best placed to drive change. It calls on surveyors and property professionals to shape how AI is used rather than wait for external technology vendors to dictate the terms.
That means setting standards for data quality and verification. AI output in property work is only as reliable as the information it is based on, and the institution is pushing its members to take responsibility for that chain.
Why this matters for real estate and construction professionals
For professionals in the field, the takeaway is direct: those who move from testing AI tools to building them into daily processes will have an advantage over organisations stuck in pilot mode. The RICS report frames AI adoption as a leadership issue, not a technical one.
Practical steps include selecting specific, repeatable tasks where AI output can be verified against existing workflows. Starting with lower-risk functions such as lease abstraction or standard document reviewing makes it possible to build confidence, and that is the groundwork needed to make AI routine.
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