AI for law firms: practical workflows for intake, review and billing

AI helps law firms most in the repetitive work around the legal advice: intake, conflict checks, first-pass document review, time entry and billing. It speeds up sorting, extracting and drafting, while a lawyer decides what the work means and signs off. The risks that matter are confidentiality, privilege and unchecked output, so each workflow below has a human gate built in.

#Where AI fits in a law firm, and where it doesn't

Law firm work splits into two kinds. One is judgment: advising a client, choosing a strategy, deciding whether a clause is acceptable. The other is processing: collecting facts from a client, checking names against a database, reading 4,000 documents for the ones that matter, writing a time narrative.

AI is useful for the second kind. It reads fast and doesn't get bored on document 3,000. It's unreliable as the final authority on law or fact. It can produce confident text that is wrong, and courts have sanctioned lawyers who filed briefs citing cases an AI tool had invented. Treat every output as a draft from a capable assistant you haven't yet verified.

We usually don't automate the final step of any legal task, such as filing, sending advice or approving a conflict. Those stay with a named lawyer.

#Four workflows worth building first

#Client intake

Most firms lose leads and waste lawyer time at the front door. A potential client emails, fills in a web form or calls, and the details arrive in pieces.

A good intake workflow runs like this:

1. The inquiry arrives by form, email or voice call.
2. The system extracts the facts: who the client is, who the other parties are, the type of matter, dates, jurisdiction and any deadlines mentioned.
3. It asks follow-up questions for whatever is missing, in plain language.
4. It classifies the matter and flags anything urgent, such as a limitation period or a hearing date.
5. It writes a one-page summary into your practice management or CRM system.
6. A lawyer or intake coordinator decides whether to accept, refer or decline.

Two cautions. An intake assistant must never give legal advice or imply that a lawyer-client relationship exists, so say plainly that it only collects information. And what a prospective client tells you can carry confidentiality duties even if you decline the matter. Treat intake data as confidential from the first message.

#Conflict checks

A conflict check searches the names of every party, related party, opposing counsel and affiliated entity against your past and current matters. The weak point is name variation. "Robert Smith," "Bob Smith" and "R. J. Smith & Sons Ltd" can all be the same person or family.

AI helps in two ways. It extracts the full list of names from an intake form or engagement letter, including those buried in the narrative. It also fuzzy-matches against your matter database and ranks likely hits by similarity and shared context such as address or company number.

It must not clear a conflict on its own. The system produces a ranked list of possible hits with the evidence for each, and a lawyer or conflicts manager records the decision and the reasoning. Keep that record in the matter file.

#Document review

This covers due diligence, discovery and contract review. In the usual setup a person reads everything, which is slow and uneven from one reviewer to the next.

A better process:

1. Ingest and classify. Documents are converted to text, deduplicated and sorted by type, using the same techniques as intelligent document processing.
2. Tag by issue. The system applies tags set by the supervising lawyer, such as change-of-control clauses, termination rights, indemnities or relevant dates.
3. Extract key terms. Parties, effective dates, renewal terms, governing law and notice periods go into a table.
4. Show the source. Every tag and extracted value links to the exact passage, so a reviewer can check it fast.
5. Sample and measure. A lawyer reviews a random sample, including documents the system tagged as irrelevant, and the error rate is recorded.
6. Review what matters. Lawyers read the tagged and uncertain documents and make the substantive calls.

Firms skip the sampling step, and it's the one that tells you whether to trust the rest. Technology-assisted review is an accepted practice in litigation, but the party using it stays responsible for the result. Keep records of how you validated it.

#Billing and time entry

Unbilled time is a quiet source of lost revenue. Lawyers forget to log calls, or write vague entries like "work on file."

AI can draft time narratives from signals your systems already hold: calendar events, emails sent, documents edited, calls logged. The lawyer sees a suggested entry for each block of activity, adjusts it and confirms. A second pass can check entries against client billing guidelines before an invoice goes out, flagging block billing, entries over a daily cap and banned task types.

The lawyer stays responsible for every entry. Auto-submitting AI-written time is a bad idea. Clients and courts expect a time record to be an accurate statement by the person who did the work.

#Confidentiality and privilege cautions

These issues decide whether a tool is usable at all.

• Where client data goes. Free consumer chat tools may keep prompts or use them to improve models. Use tools under written terms that cover retention, training use and subprocessors, or host models in your own environment. Our guide to self-hosting a private LLM covers when that is warranted.
• Ethics guidance. In the US, the American Bar Association issued Formal Opinion 512 on generative AI tools in July 2024. It applies existing Model Rules duties, including competence, confidentiality, communication, supervision and reasonable fees, to lawyers' use of these tools. It also says client informed consent can be required before you put client information into a self-learning tool, and that boilerplate in an engagement letter is not enough. State bars add their own guidance, and it keeps changing, so check with your ethics counsel.
• Privilege. Sharing privileged material with a third party can raise waiver questions. Contract terms that limit the vendor's access and use are your first defense.
• Matter segregation. Ethical walls and client-specific access controls have to carry over to any AI tool. An assistant that can search every matter breaks your wall.
• Permissions in retrieval. A firm knowledge assistant over precedents and matter files must respect who is allowed to see what. Our explainer on how RAG works for company documents covers permission-aware retrieval.
• Citations. Check every case, statute and quotation an AI cites against the primary source.
• Telling clients. Some engagement terms and jurisdictions call for informing clients about AI use. Put it in your engagement letter.

Key point: Automate the processing and keep the judgment. Every AI output that reaches a client, a court or an invoice should have a named lawyer's review behind it, and the log should show it.

#Human sign-off by workflow

#How to start without a big project

1. Choose one workflow with high volume and low risk, such as conflict-check name extraction or time-entry drafting.
2. Write the rules down with the lawyers and staff who do it today, including what "good" looks like and what must always escalate.
3. Run it alongside the current process for a few weeks and compare results.
4. Review the failures. Each missed conflict or wrong tag points at a missing rule or instruction.
5. Go live with a review step and an audit trail. Widen scope only once the partner responsible accepts the error rate.

For a firm with, say, 50 lawyers, that is a few weeks of work on one workflow. It isn't a firm-wide transformation. You can read more about how we work with professional services firms, or see our general workflow automation approach.

If you want to talk through which workflow suits your firm first, book a strategy call. We'll look at how work moves through your systems and tell you which steps are worth automating and which to leave alone.

#Frequently asked questions

#How can law firms use AI?

The strongest uses are client intake, conflict-check name extraction and matching, first-pass document review, summarizing, drafting time narratives and checking invoices against billing guidelines. Each should keep a lawyer's review before anything reaches a client, a court or a bill.

#Is it safe to put client information into AI tools?

It depends on the tool and its terms. Consumer tools may retain or train on what you enter, which puts confidentiality at risk. Use enterprise terms with clear limits on retention and use, or host models inside your own environment, and ask your ethics counsel about your jurisdiction's rules.

#Can AI do legal research and draft briefs?

It can speed up first drafts and surface leads, but it can state invented cases and quotations with confidence. Every citation needs checking against the primary source, and the lawyer owns the final work product.

#Will AI replace paralegals or associates?

For the processing tasks described here, no. The volume of work and the need for review keep people involved. The work changes, with less manual sorting and typing and more checking and judgment. Each firm decides how to redeploy that time.

#What should we automate first?

Pick a high-volume, low-risk workflow with a clear review step, such as extracting names for conflict checks or drafting time entries. Run it in parallel with the current process, measure errors, and expand only when the results are consistent.

#Legal workflow software vs AI agents: which do we need?

Legal workflow management software tracks matters, deadlines, tasks and approvals, and it is the right base for most firms. An AI agent does the reading and drafting around it: summarizing a bundle of documents, preparing a conflict search, drafting an engagement letter from your template. They work together. The software keeps the record, and the agent proposes entries that a person approves. If your main problem is that nobody knows who owns which task, fix that with workflow software first.