When clients need to be told that AI was used
Clients do not need a technical explanation every time a firm uses software. They do need a clear disclosure when AI changes the risk profile of the work, touches confidential material, shapes advice, affects professional judgement or changes what the client thought they were buying.
For professional services firms, the practical test is simple: would a reasonable client, regulator or file reviewer expect to know that an AI-enabled tool influenced the work? If the answer is yes, disclose it in plain English and keep the evidence of human review.
The safest answer is proportionate disclosure plus a file note
The safest approach is to tell clients when AI use is material, not to turn every internal tool into a long disclaimer. Material use usually includes uploading client information, using AI to draft or analyse advice, relying on AI to summarise evidence, or using an external tool where retention, training, security or access settings matter.
That disclosure should say what the tool was used for, what it was not used for, and who remained accountable for the final judgement. The firm should also keep a short audit trail showing the approved tool, the data entered, the reviewer, the checks completed and any changes made before the work reached the client.
Check where AI use needs disclosure and evidence
What counts as material AI use?
AI use is likely to be material when it touches client confidential information, personal data, privileged material, regulated advice, audit evidence, valuation work, claims handling, financial recommendations or any output that a client may rely on. The question is not whether the model is impressive. The question is whether the tool affected the work in a way that creates a confidentiality, quality, accountability or expectation risk.
Examples include using a public AI tool to summarise a client file, asking an assistant to draft advice from matter facts, processing client calls through an AI transcription service, or using an embedded AI feature inside a practice, CRM, document or workflow platform. Even when the tool is approved, the firm still needs a controlled process for disclosure and review.
How to decide what the client should be told
Use a short decision tree. First, did client data or client context enter the tool? Second, did AI influence advice, judgement, drafting, evidence analysis or a client-facing deliverable? Third, would the client reasonably expect a human professional to have done that part of the work unaided? Fourth, do the contract, engagement letter, professional rules or regulator guidance require a particular standard of transparency?
If the answer to any of those questions is yes, use a concise disclosure. Avoid vague claims that AI was used safely. Say what happened: for example, that an approved AI-enabled tool helped summarise source documents, that no client data was used for model training under the configured terms, and that a named professional reviewed the output before it informed the final advice.
What the internal record should contain
The file note matters because disclosure without evidence is weak. A good record states the tool or system used, the purpose, the category of information entered, the data protection or confidentiality control, the reviewer, the checks made, and the final professional decision. It should also record when AI was considered and rejected because the data or judgement risk was too high.
This record does not need to be bureaucratic. It can be a structured field in the matter file, workflow system or approval log. The important point is that the firm can prove human review, data discipline and accountability if a client, insurer, regulator or internal quality reviewer asks later.
Build an AI disclosure and review operating model
How this differs by professional context
Law firms should pay particular attention to confidentiality, privilege, client consent, SRA expectations and whether AI use affects advice or client communication. Accountancy, audit and advisory firms should focus on independence, evidence quality, review trails and whether AI influenced conclusions. Financial services and insurance teams should connect disclosure decisions to Consumer Duty, SM&CR accountability, complaints risk, claims handling, underwriting judgement and data protection.
The common thread is control. Clients do not need theatre around AI. They need confidence that sensitive information was handled properly, that the work was reviewed by a competent human, and that AI did not quietly replace professional responsibility.
A practical disclosure rule for firms
Create three categories. Category one is low-risk internal productivity, such as formatting non-confidential notes, where no client disclosure is usually needed. Category two is controlled assistance, such as summarising approved documents or drafting first-pass text, where disclosure may be needed depending on the matter and file note. Category three is high-risk or client-material use, where disclosure and senior review should be mandatory before the work proceeds.
Review the categories quarterly, because embedded AI features change quickly. Procurement, IT, risk and fee-earners should agree which tools are approved, which data is allowed, and which client communications require a disclosure line.
Conclusion
Tell clients about AI use when it affects their data, the substance of the work, the delivery model or the professional judgement they are relying on. Keep the wording short, but make the control real: approved tools, human review, a clear file note and an escalation route for higher-risk matters.
Implement AI review and disclosure controls
Need a safer AI route for your firm?
If this article reflects a live decision in your practice, the useful next step is to map the workflows, confidentiality risks, supplier controls and governance gaps before tools spread informally.
