AI for law firms: use cases, risks and how to start

How are law firms using AI in day-to-day work?

Before choosing any tool, a firm needs to know where its time actually goes. In most practices, a large share of it goes on tasks that are repetitive and document-heavy: reading, summarising, comparing, drafting, recording time and finding what the firm already knows.

What about client intake?

Intake comes first because it is where clients form their view of the firm. AI can read an enquiry, extract the key facts, check for conflicts against a list the firm provides, suggest the right practice area and draft an acknowledgement for a person to send. The client hears back sooner, and the fee earner receives a structured summary instead of a rambling email. The AI does not accept instructions or give advice. A person decides whether the firm can act.

Can AI help with document review?

Document review is the best-known use. AI can read large sets of contracts, correspondence or disclosure, group them, summarise them and pull out the clauses or facts a lawyer asked for. It can flag documents that look different from the rest. The benefit is speed and consistency, because the same question is asked of every page. The risk is that it misses or misreads something, so a lawyer must sample the results and review anything that matters. Treat the output as a first pass that directs attention, not as a finding.

What about legal research?

Legal research also benefits, with care. AI can summarise a body of material, propose search terms, compare positions and draft a research note with sources listed. Because language models can state things confidently that are not true, every authority and quotation must be checked against the original before it is relied on. A research assistant that shows its sources makes this check quick. One that does not should not be trusted for anything a client will see.

Drafting is the fourth area. AI can produce a first draft of a letter, a clause, a note of advice or a summary of a meeting, using the firm's own precedents and style. The lawyer then edits. The gain is a faster start and a more consistent house style, and the discipline is to treat every draft as unfinished.

Where else does AI help a law firm?

Time capture and billing narratives are less glamorous but valuable. AI can suggest time entries from emails, calendars and documents, and draft clear descriptions for a fee earner to confirm.

What about time capture?

Knowledge search is the sixth use. Most firms hold years of precedents, advice notes and know-how that nobody can find. AI can search that material by meaning rather than by exact keywords, answer a question and point to the source documents, while respecting who is allowed to see which matter. It stops people reinventing work that a colleague did last year, and it helps new joiners learn faster. The quality of the answer depends on the quality and organisation of the material, so some tidying is often part of the project. We discuss how to approach this in our guide to AI agents, and the same ideas apply here.

Can AI search a firm's own knowledge?

AI brings real risks to a law firm, and they are manageable. Accuracy is the first: models can produce plausible but wrong statements, invent sources or miss a key clause. Confidentiality is the second: client information must not be shared with a tool that retains it or uses it for other purposes without the firm's control. Privilege is the third: the firm must be sure how a tool stores and processes material before putting privileged documents into it. The fourth is over-reliance, when busy people stop checking. The answer to all four is process: choose tools deliberately, restrict access, review outputs, log what was done and train people on the limits.

What are the main risks of AI in a law firm?

Rules differ by jurisdiction, and regulators and professional bodies continue to issue guidance on AI. A firm should check what applies to it and to its clients before automating anything that touches regulated work. Many firms find it useful to begin with a short written statement of what is allowed, what is not and who decides. We help firms think this through when they draft their internal AI use policy, and that policy then guides every tool.

How do you manage the risks, and what do the rules say?

Client confidentiality is the duty that matters most when a firm adopts AI. Time returned to fee earners is the reward, but it only counts if client trust survives. Three areas deserve attention: where data goes, who can see it and who checks the output. Faster responses and consistent work are worth little if a client's information ends up somewhere it should not.

How should a firm protect client confidentiality?

Confidentiality starts with where data goes. Ask every supplier where documents are stored, who can access them, whether they are used to train models, how long they are kept and how they are deleted. Prefer set-ups where client data stays in an environment the firm controls, with access limited by matter. Never paste client material into a consumer tool whose terms you have not read. Put the rules in writing and make sure that every person, including trainees and support staff, has seen them.

Where does client data go?

Access control matters as much as the model. If the AI can see every matter, anyone using it can potentially retrieve information they should not. Mirror the firm's existing ethical walls and permissions inside any AI tool that searches firm documents. A qualified lawyer who reviews every outputis the single most important control. AI drafts read fluently even when they are wrong, so the reviewer needs the source in front of them and enough time to check it. Review should be proportionate: a quick read for an internal summary, a line-by-line check for anything that goes to a client, a court or a counterparty. Record who reviewed what. If the firm cannot show that a person checked the work, it cannot show that the work is reliable.

Who checks the work before it reaches a client?

Do not judge a tool by a demonstration. Demos are built on tidy examples, while a firm's documents are messy, scanned, inconsistent and long. Test on your own material before deciding anything, and write down the result. We set out how in our guide to testing AI agents before launch . For a law firm, a good test uses real but anonymised matters, includes awkward cases, and compares the AI's output with the work of an experienced lawyer. Score it on accuracy, completeness and tone, and decide in advance what level counts as acceptable. If a tool cannot meet that level, it is not ready, however much it costs or how many other firms use it.

How should a firm test and govern AI?

A policy should cover the same few things. Which tools are approved. What data may go into them. Which tasks are allowed and which are not. Who reviews outputs, and how. What must be recorded. And how to report a problem. Keep it short enough that people read it, and review it regularly as tools and rules change. A policy nobody follows protects nobody. A firm that starts small gets more value than one that begins with a firm-wide programme. A pilot in one practice group, on one task, with a small group of willing lawyers, teaches more in weeks than a plan made in the abstract. It reveals how the tool behaves on your documents, where review takes time and which fears need addressing. Then you can widen it with evidence. Starting small also keeps the cost of a wrong choice low, and makes it easier to change direction.

What does AI give a law firm?

The first benefit is time returned to the people whose time is most valuable. Hours spent reading, summarising, retyping and searching can go back to advising clients. The second is speed: enquiries are acknowledged sooner, documents are triaged sooner and first drafts appear sooner, which clients notice. The third is consistency: the same checklist, style and standard applied to every matter, regardless of who is busy or on leave. The fourth is better use of what the firm already knows, as knowledge search turns years of precedents into something people can actually find. The fifth is capacity: a firm can handle more matters without a matching rise in administrative work, which matters most to smaller practices. There are limits to these gains. AI does not exercise judgement, owe duties to a client or carry professional responsibility. It makes mistakes, and its mistakes are often fluent and convincing. A firm that treats it as a capable assistant under supervision will do well. A firm that treats it as a substitute for a lawyer will eventually be embarrassed, or worse. For that reason, the benefits above are the reward for good process, not a feature of the technology alone.

How should a law firm get started with AI?

Start with a conversation about the firm's real work, not about tools. List the tasks that fee earners and support staff repeat every week, and note where they wait, retype or search. Then choose a shortlist and rank each by how much time it would return and how easy it is to check.

What should a law firm automate first, and why?

Good first choices are intake triage, summarising documents, drafting routine letters and suggesting time entries, because the work is frequent, the output is easy to check and a wrong answer is cheap to correct. Poorer choices are anything that gives advice, files with a court or moves client money. Begin where a lawyer can review the result in a minute or two, then move up to more demanding work as confidence grows. Resist the temptation to start with the most impressive demonstration.

Who should own the project?

Every AI workflow needs an owner. In a law firm this is usually a partner or practice manager with enough standing to decide, supported by someone who knows the technology. The owner keeps the policy up to date, reviews errors, decides when to widen a tool's role and has authority to switch it off. Partners should be visible users, because staff will follow what leaders do. Include the people who will work with the tool in its design from the beginning, since they know exactly where the work gets stuck. Without ownership, tools drift, and trust declines.

How does the Embedded Week help a firm?

This is where our Embedded Week fits. We sit with your team for a week and watch how matters really move through the firm: how enquiries arrive, where documents are copied between systems, how time is recorded, what people wait for. We map where the time goes and then build a custom AI solution for your firm specifically, not a generic product, using your tools and your rules. We then train your people to run it. Because we observe first, the solution targets the tasks that actually cost your firm time.

Can a firm start small and build up?

The Embedded Week also gives you a plan you can keep, whether or not you continue with us. You come away with a map of the work, a ranked list of opportunities and a view of the risks, including which tasks should stay with people. We recommend building in stages, one workflow at a time, with review at each step. Many firms begin with intake or document summaries, learn how the tool behaves, and then add research support, drafting or knowledge search. This approach keeps costs predictable, spreads the learning across the firm and avoids a large project that tries to change everything at once. At every stage, AI supports professional judgement and does not replace it, and rules differ by jurisdiction.

Who for?

If you would like to explore what this could look like for your firm, book a meeting with us. We will talk through how your practice works, which tasks are the strongest candidates and whether an Embedded Week would be a sensible next step. There is no obligation, and we will tell you plainly if AI is not yet the right answer for a particular process. The decision stays with you, and so does the judgement on every piece of work that leaves your firm.