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AI can automate letters before action and small claims for commercial debt, but every figure, step and claim remains the practitioner's responsibility. Because debt recovery is bulk work, one flawed template or data error can repeat across hundreds of claims.
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In short
Debt recovery can be highly automated. The SRA's 2025 authorisation of Garfield.Law, an AI-driven small-claims debt recovery firm, gives a useful benchmark: the system cannot propose case law, acts only once the client approves each step, and named lawyers stay accountable. For CILEX practitioners the Code requires accuracy (outcomes 1.2 and 3.2), client instructions (5.5), clear information (5.8) and supervision (8.2 to 8.4). If a bulk process issues wrong claims, debtor complaints, costs consequences and client claims can follow, and PI usually responds to client loss subject to the policy terms.
Commercial debt recovery runs on volume. AI and automation tools import a client's ledger, calculate the sum due with interest and charges, generate letters before action, chase responses and prepare small claims for issue. Generative AI chatbots draft responses to debtor disputes. The aim is to process many low-value claims cheaply, which makes accuracy controls and client approval steps especially important.
In May 2025 the SRA authorised Garfield.Law, an AI-driven firm running a small-claims debt recovery litigation assistant. Before authorising it, the SRA checked quality control, confidentiality and conflicts. The system cannot propose case law, to reduce the risk of hallucinations, and it acts only once the client approves. Named solicitors remain accountable for its outputs and the firm must carry the minimum insurance. CRL has no equivalent decision, but these features are a sensible yardstick for any CILEX practice automating debt work.
CRL's position on AI is that firms remain responsible and the lawyer is the decision-maker. Under the CILEx Code of Conduct:
Bulk processes fail in bulk. A ledger import that misses credit notes, an interest rate applied to the wrong contracts, or a template that states the wrong pre-action position can affect every claim in a batch. AI responses to debtor disputes may dismiss a genuine defence or make threats the firm cannot carry out.
Illustrative: a CILEX practitioner firm runs a monthly batch for a trade client. A change in the client's ledger export means partial payments are dropped, and the system issues small claims for the full original balances. Several debtors defend, some complain, and the court considers costs against the client. The client's relationship with its customers suffers and it looks to the firm for its losses and wasted costs.
Automated letters before action must still reflect the client's real position: the correct sum, the basis of the claim, and an accurate statement of what will happen if the debtor does not respond. AI tools that generate replies to debtors can be tuned to sound firm, but a letter that threatens steps the client has not authorised, or describes consequences inaccurately, risks breaching outcome 3.2 and attracting complaints. Where a debtor raises a dispute about quality, delivery or set-off, a person should review it before the claim proceeds, because pressing an unmeritorious claim can lead to costs consequences for the client.
A client's claim for losses caused by wrongly issued claims would usually be treated as a professional negligence claim, subject to the policy terms. For CRL-authorised entities the £2m minimum applies regardless of wording. In bulk work the key question is aggregation: whether many small claims arising from one error count as one claim or many, which affects the limit and excesses. Fines and regulatory sanctions are usually not covered. See does PI cover AI mistakes?.
Apex arranges PI for CILEX practitioners in debt recovery, and a broker can review how your policy handles bulk work, aggregation and AI-assisted processes. See the AI for CILEX lawyers hub, AI-drafted letters of claim, AI-assisted litigation after Mazur and AI client care and costs.
Yes, provided the lawyer remains the decision-maker and the Code outcomes are met. The SRA's Garfield.Law authorisation shows the kind of controls a regulator looks for: no AI-generated case law, client approval before each step and named lawyers accountable.
The practitioner and the firm. CRL's position is that firms and lawyers remain responsible for decisions and actions taken with AI, and outcomes 1.2 and 3.2 apply to the accuracy of claims and letters.
It depends on the aggregation wording in your policy. Some wordings treat claims arising from one act or related acts as a single claim, which affects the limit and the number of excesses. Ask your broker how yours works.
It is good practice and supports outcome 5.5 on acting on instructions. The SRA required Garfield.Law's system to act only once the client approves, which is a useful benchmark for any automated debt recovery process run by a CILEX practice.
Tell us about your firm and the work you do. A broker will review how your current PI treats AI-assisted work, or arrange new cover. No fee for a quote. Or call 0117 325 0027.
Start a PI quote Request a callbackApex Insurance Brokers Limited is authorised and regulated by the Financial Conduct Authority. Registered in England and Wales, company number 07014570. This page is general information about how AI is used in the profession and how insurance may respond. It is not legal, regulatory or insurance advice on your circumstances, and it does not guarantee that cover will be available or on what terms.
