RICS AI Standard for Small Firms: Register, Assessment, Client Notice

The RICS AI standard has applied since 9 March 2026. For AI that materially affects surveying work, a regulated firm must record a pre-use assessment, keep written systems and risk registers, judge output reliability, and notify clients in writing beforehand. The notice must explain where AI is used, how clients can challenge it and whether opting out is possible.

The threshold is material impact on the delivered service, not simply whether a staff member opened an AI tool. RICS gives examples such as summarising evidence relied on in a report, composing significant parts of an opinion or recommending where to investigate a fault. Routine diary or email support may fall outside that threshold, although RICS's own guidance lists co-pilots that write emails to clients as a use that may be material. Record the classification and its reasoning before deciding which controls apply.

Tick the material-impact gate for each proposed use

Use the published RICS professional standard as the authority. Begin with a use case, not a vendor name: “AI summarises inspection notes that the surveyor will use to draft a condition report.” Ask whether the output can influence what is inspected, how evidence is understood or what opinion is delivered. If yes, consider the use material and apply the relevant requirements. If no, record why, because the same tool can be used differently on another instruction.

A copyable decision record has these fields: service; task; system and feature; inputs; proposed output; how the output could influence the service; material or non-material conclusion; reasons; decision maker; decision date; reassessment trigger. One illustrative entry might say: “Residential condition survey; AI reformats spelling in a finished internal note; no change to defect finding or recommendation; non-material, subject to human review.” A second use of the same assistant to infer the cause of a damp patch is likely to demand a different assessment. Do not treat the first entry as blanket approval of the product.

For a valuation firm, an AI-generated draft of the final value narrative is a material use even if a surveyor later edits it. The output can influence the reasoning the client sees. By contrast, an assistant putting inspection appointments into a diary may not affect the service. The edge case is a system that prioritises which buildings receive an inspection: its effect may be material although it does not write a report. Where uncertain, record the chain of influence and ask the firm's professional lead to decide.

RICS states that a member or regulated firm which determines that its AI use will have a material impact must record that determination and the reasoning. The broader responsible-use checklist covers practice governance; the narrow work here is getting the register, assessment and notice into a sequence a small firm can maintain.

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Write the pre-use assessment before the first client file

Section 3.2 requires a written assessment, before materially impactful use, of whether AI is the most appropriate tool. The considerations include the services provided, the task, alternatives, environmental and stakeholder impact information, data risks, likely output errors, bias and consequences. A periodically reviewed policy or standing statement can perform this role if it explicitly addresses those points for each service and purpose. A product brochure alone does not answer whether it is appropriate for this firm's task.

For an illustrative four-surveyor firm, consider a tool that summarises 40 pages of inspection notes for each of six monthly reports. The proposed benefit is quicker retrieval of observations, perhaps 20 minutes of manual search per report. The alternative is a standard indexed note template and text search. Test both on five representative, synthetic files. If AI misses one significant defect reference in five, the apparent two-hour monthly time saving may be irrelevant to the professional risk. Record the test design, the missed item, the human review plan and the decision. Those numbers are illustrations, not measured tool performance.

A filled assessment line could read: “Task: locate references to roof water ingress in site notes. Alternative: keyword search of indexed notes. Stakeholder effect: a missed reference could alter the client's repair priorities. Data: notes may contain occupant details; anonymise before testing. Known error risk: paraphrases can omit qualifiers. Decision: pilot only on synthetic notes, then require the surveyor to compare every cited observation with the original; no client data until permissions and supplier due diligence are complete.” This is a usable starting record, not a substitute for the firm's own assessment.

Ask the supplier written questions before procuring a materially used third-party system. The standard calls for information on environmental impact, development stakeholders, data and confidentiality compliance, permissions for data used in development, accuracy and diversity of training data including gaps and bias, and supplier liability. Keep the request, response, follow-up and test result. If the supplier cannot answer, log the missing information as a risk; silence is not assurance. A short procurement sheet can record the precise question, response date, evidence supplied, unresolved gap and decision maker.

Keep a systems register and a separate risk view

RICS section 3.2 requires a written AI systems register for systems whose use materially affects surveying services. It must include the system, purpose, date first used and date for the next review of use and appropriateness. Add operational fields that make it workable: service, internal owner, version or supplier, data types, approved users, assessment link, client-notice wording, output check and status. Those extra fields are practical suggestions; the four named items are the explicit register minimum in the standard.

The example dates and system are illustrative. A register row should link to the specific approved use, not imply that any feature of a supplier's suite is approved. If the tool later adds automatic defect recommendations, treat that as a new use and reassess. Close old versions with a date rather than erasing them. A reviewer should be able to answer which system produced a material output on a given instruction and which approval was current at the time.

Section 3.3 also requires a risk register for materially impactful use. It must address overarching risks including bias, wrong output, gaps in information on the system or training data, and retention or use of firm inputs. Each risk needs likelihood, impact, mitigation, the firm's appetite, progress and a red/amber/green or similar rating. Responsible staff must review and update it at least quarterly. One controlled workbook may hold the system and risk sheets, but a list of tools is not itself a risk assessment.

An illustrative risk row: “AI omits a safety-relevant crack from a note summary; likelihood: possible in tested scans; impact: high; appetite: very low; rating: red until each material statement is checked against the original; control owner: senior surveyor; status: five-file test found one omission; next review: after revised workflow and before live use.” The firm should define its own likelihood and impact scale. RICS publishes a risk management sample and sector case studies alongside the standard, which help calibrate a first register without copying someone else's ratings. A green label without evidence of the control working is not a defensible review.

Another risk row concerns retention. If a supplier's terms do not establish how uploaded floor plans are retained or used, the correct status is “supplier answer outstanding; do not upload client plans”. A firm may be tempted to use public sample drawings meanwhile; check that those are genuinely permitted for the pilot. Quarterly review should revisit supplier terms, tool changes, user access, observed errors and client complaints, not merely change the date on the sheet.

Put a qualified surveyor between output and opinion

Section 4.2 requires professional judgement and a written decision about the reliability of a material AI output. The decision covers assumptions, concerns about underlying data, why each concern matters, whether it can be reduced and whether the output can reasonably serve its intended purpose. A qualified, named surveyor prepares or supervises that decision and accepts responsibility for use. Where output is unfit, the firm must tell the client in writing with reasons or a summary. High-volume automated outputs call for regular randomised dip samples rather than an individual full written decision on each, but the firm remains accountable for each output.

Consider a sample valuation analysis of 12 comparable transactions. The AI selects eight and estimates a value of $420,000. The surveyor discovers two of the eight are outside the agreed date range and one is a different property type. The output may still be useful as a list of candidates, but not as an unqualified valuation basis. A written reliability note should identify the three exclusions, the amended comparison set, the effect on the range and the named surveyor's conclusion. The final opinion remains the surveyor's, supported by checked evidence.

A different example is a condition-report draft that changes “possible historic staining” to “active leak”. The change is a substantive assertion, not a stylistic improvement. The checker should compare the generated sentence with the site note and photo, amend the report and log the failure type. If the tool repeatedly upgrades cautious observations into firm conclusions, adjust the approved use or stop it. The site-notes-to-report tutorial explores drafting checks in more detail.

For an automated batch of 100 low-level classifications, a firm might choose an illustrative random sample of 10 each week, record every error and expand the sample after a failure. The standard requires regular randomised dip sampling for high-volume outputs but does not prescribe that particular ten-per-cent rate. State your own sampling rationale in the policy. Keep the original inputs, sampled outputs, reviewer decisions and corrective actions. If the system changes version, do another test before assuming the old sample remains representative.

Send the client notice before material AI use

RICS section 4.3 says the client must be told in writing and in advance when and for what purpose material AI will be used. Engagement or related documents must explain when AI is involved, which parts of the service, available professional indemnity cover for AI use, the internal contest route, redress if the client feels harmed, and whether and how the client can opt out. Section 4.4 requires further written information on request about system type, basic working and limitations, due diligence, risk management and output reliability decisions. The RICS client information note gives a concise official explanation.

A draft notice for professional review could say: “For this instruction, we may use the approved AI note-summary system to locate references in inspection notes before a qualified surveyor prepares the report. The system will not decide the valuation or final condition opinion. We will check material output against original evidence. You may raise concerns with [role/contact route], and our complaints process explains how to seek redress. [State whether and how an opt-out is available.] [State the extent of relevant professional indemnity cover if available.]” Fill the bracketed fields with real firm arrangements; do not send them as placeholders.

The notice is not a substitute for consent to upload confidential data. Section 3.1 says a regulated firm should refrain from uploading private and confidential data to an AI system unless it has advance express written consent from affected stakeholders and has taken reasonable steps to find the upload risk acceptable. The firm must also safeguard data, restrict access, prepare data to protect privacy and train relevant staff at least annually. Have a qualified adviser review the interaction between the standard, engagement wording, data rights and any applicable law. Do not assume a client's acknowledgement of an AI notice authorises every data upload.

One practical failure test is a repeat client whose engagement letter predates the AI use. If the firm adds a materially used tool in the middle of the instruction, send updated written information before using it on that client's material. Another is a tool embedded in existing software: the staff member may not realise an “auto-summary” switch is AI. Include embedded features in the systems review, and ask the vendor what the function does with data. A third is the client's opt-out request. The team needs an actual non-AI route or a clear statement if no opt-out is offered; an empty sentence in the contract does not create a working process.

Run a small-firm evidence check every quarter

Put the assessment, systems register, risk register, supplier correspondence, test pack, output-reliability notes and client notice versions in a restricted folder with named ownership. At the quarterly risk review, pick three material uses and trace each from approval through one client instruction to a checked output and the notice that preceded it. Verify the next review dates, staff access and current supplier terms. If one link is missing, record a corrective action with owner and date. That exercise makes the register a working control rather than a compliance ornament.

Another illustrative trace: the register says a note-summary system first ran on 1 April and was due for review on 1 July. A sample instruction on 15 June has a signed engagement notice dated 10 June, a summary produced on 16 June and a named surveyor's reliability note on 17 June. That sequence is coherent. If the notice is dated 20 June, it arrived after the material use and needs investigation. The dates are illustrative, but the order is essential.

Include a negative test in that review. Take a sample output the firm rejected and ask whether the record explains why it could not reasonably be used, who decided that and whether the client was told in writing where the standard requires it. A folder full of successful summaries gives no evidence that the team knows how to stop. If the rejected output is missing from the register trail, improve the file naming and link it to the instruction. If the same error appears again, update the risk rating and the output check rather than treating each incident as isolated.

Review the firm's written procurement and use policy periodically (a yearly review is a sensible rhythm, and the risk register itself needs a quarterly one) and train relevant people at least annually, as the standard requires. Name who may approve a new AI system, who may add a use to the register, who checks outputs and who handles a client challenge. For smaller practices, one person may hold several roles, but the decisions should remain explicit. A senior surveyor should be able to pause a material use when evidence is weak. Retain a manual method for urgent work when the system is unavailable or its output is unreliable.

When an AI tool influences site evidence, compare the tested workflow with the field-check boundary for land surveyors: an automated suggestion can help direct attention, but the surveyor still records what was observed and what remains uncertain. The same principle applies to a defect image classifier. Keep the original image, the system suggestion, the inspected finding and the signed professional conclusion as distinct records. This makes a later client challenge answerable without relying on a generated summary alone.

The standard sets professional obligations; the examples here are implementation aids, not legal advice or a certification. Ask a qualified professional to check the final assessment and client terms against the firm's instructions and applicable requirements. Use the exact RICS text where a requirement is in doubt. The strongest small-firm proof is a short, dated trail showing why a tool was chosen, which risks were accepted, how output was checked and what the client was told before it was used.

Questions about material use and records

Does every spellcheck require a client notice?

No. The standard applies to AI outputs with a material impact on the surveying service. RICS's FAQ says help drafting emails or managing a diary is unlikely to be material, though not impossible, while its guidance lists co-pilots writing emails to clients and customer-service chatbots as uses that may be. Record the reason for classifying a borderline use before relying on it.

Are the AI systems register and risk register the same document?

They serve different purposes. The written systems register records each materially used AI system, its purpose, first-use date and next review date. The risk register records risks such as bias, erroneous outputs and data retention, with likelihood, impact, mitigation, appetite, status and a rating. A small firm can keep them in one controlled workbook if the required information and review trail remain clear.

Can a firm rely on a supplier's accuracy claim?

A supplier claim is evidence to examine, not a professional decision about a particular output. The standard requires written supplier questions and an assessment of the information received, followed by a qualified surveyor's documented reliability judgement for material outputs. Test the tool on representative work and record gaps. If information is missing, capture that as a risk rather than silently assuming it is favourable.

Further reads

• Best AI for Quantity Surveyors: Five Tool Types Compared by Job — Choose tools by the surveying job.
• AI Tools for Quantity Surveying: Take-Offs, Cost Plans and Checks — Apply tool checks to measured quantities.
• Using AI in Surveying: Quotes, Reports and Client Updates — Classify quote and client-update uses against the register.
• Use of AI in Building Surveying: Client Data and UK GDPR Basics — Review client-data handling before an upload.
• How Can AI Help Quantity Surveyors? BoQ Checks, Takeoffs and Cost Reports — Review quantity outputs in context.
• Damp Survey Report: Write Findings and Quote Lines With AI — Turn readings and site notes into a clear damp report and quote lines in ChatGPT or Claude, while the surveyor keeps the diagnosis and checks every line.
• Dilapidations Schedule: Draft Items From Lease Clauses With AI — A worked example of mapping lease clauses to dilapidations schedule items with AI, with the RICS format explained and cost and legal checks left to people.
• Can AI Check an Interim Valuation? A Quantity Surveyor's Test — A quantity surveyor's clause-by-clause test of AI on a contractor's interim application: what it catches, what it misses, and who values.
• Schedule of Condition Template: Draft From Photos With AI, Then Verify — A fill-in schedule of condition template, plus the steps to draft entries from site photos with AI and verify every line on site.
• AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.

Sources: RICS, Responsible use of artificial intelligence in surveying practice, first edition PDF, sections 1 to 4; RICS client information note; RICS responsible use of AI page and FAQ (checked 4 November 2026).