Aipedia: Architects
AI tools can shortlist products and materials quickly, but the architect remains responsible for checking that what is selected meets the required performance. Wrong or substituted products, especially on fire performance, cause some of the costliest claims.
Part of: AI in Architects
In short
RIBA's 2026 AI Report found 18% of AI-using practices use AI for product and material selection. Tools can suggest products whose certification is out of date, whose fire classification does not suit the use, or which are not actually equivalent when a contractor proposes a substitute. Part 2A reg 11K requires designers to ensure the design would comply if built. Design and build contracts add pressure to approve fast. PI usually responds to negligent selection, but fire safety restrictions may narrow cover.
The RIBA AI Report 2026 found 18% of AI-using practices use AI for product and material selection. Uses include asking a chatbot to suggest products that meet a performance requirement, comparing datasheets, checking whether a contractor's proposed alternative is equivalent, and generating material palettes from images.
Tools may rely on outdated product data, mix up similar product ranges, confuse a manufacturer's marketing claim with a tested result, or treat products as equivalent because they share a description. They rarely know the precise application, substrate or building height that determines what is acceptable. Product ranges also change frequently, so an answer that was right when the tool was trained may be wrong today. The result is often a confident recommendation with no traceable evidence behind it, which is exactly what a later claim will probe.
See the ARB and RIBA guide for how these standards apply generally.
Illustrative scenario. On a design and build project, the contractor proposes a cheaper external cladding board. Under time pressure, the architect, now novated to the contractor, asks an AI tool to compare it with the specified product. The tool reports both as non-combustible, relying on a summary that applied to a different product in the same range. The substitute is installed. A later fire safety review finds it unsuitable for the building height, and replacement is needed.
The architect faces claims from the employer and possibly the contractor, and has no record of what the AI comparison relied on. See design and build risk for architects and novation and PI.
A negligent product selection or substitution approval is usually within the scope of architects' PI, subject to the wording. Fire performance is the main complication:
See cladding and fire safety exclusions.
Back to the AI in architecture overview.
Apex arranges PI for architects working on traditional and design and build projects. A broker can explain how your fire safety terms would apply to a product selection claim.
Not safely. An AI comparison can help frame questions, but equivalence depends on test evidence for the specific use, substrate and building height. Check the manufacturer's current data and, for fire performance, take specialist advice where needed.
Novated architects often face programme and cost pressure to approve substitutes quickly, and may owe duties to both employer and contractor. That combination makes rushed reliance on AI comparisons more likely and claims more complex.
Usually, as a negligence claim, subject to the wording. If the issue concerns fire safety or cladding, restrictions or aggregate limits on the policy may apply, so check how your policy treats those risks.
Keep the product data and test evidence relied on, the date checked, who reviewed it, and the reasons for accepting or rejecting substitutes. Note where AI tools were used and what was verified independently. This supports Standard 4.5.
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