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Insights / Governance
Explore law-firm AI use cases across intake, research, contracts, litigation support and operations, with integration, cost and rollout guidance.
A client asks for an update. The latest draft is in the document system, a relevant conversation is in a partner's inbox, and the matter record still shows last week's position. Before anyone can write a useful answer, someone has to reconstruct what happened.
That situation captures one opportunity for AI in a law firm: connecting the information and handoffs around legal work. The same problem appears when opening a matter, assembling evidence, reviewing a contract, preparing a bill or handing a file to another lawyer.
An AI agent for a law firm is software that uses a model and connected tools to carry out steps in a defined task. It can gather permitted information, interpret documents, prepare work and request an action. The surrounding application determines which actions are allowed and which require a person's decision.
The opportunity extends across the firm. Research and drafting matter, but so do incomplete intake, difficult handovers, stale records and repeated checking. This guide examines those uses, the systems behind them, and how to decide whether a workflow is ready for everyday use. The examples are proposed designs, not claims about client deployments.
A single request to summarize a document may need only a drafting tool. A process that checks for missing documents, compares them with a matter checklist, prepares a request and tracks the response needs coordination across several steps.
Some of those steps should be ordinary software. An assigned matter owner can come from a database field. A required attachment can come from a checklist. A model can help interpret a loosely written email or explain why two documents appear inconsistent. Using AI selectively makes the process easier to inspect.
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| Approach | Example in a firm | Question to ask |
|---|---|---|
| Fixed automation | Route an uploaded file to the assigned matter team | Are the routing rules and exceptions clear? |
| AI assistance | Produce a first summary of a permitted document | Can a reviewer verify the summary efficiently? |
| Agent workflow | Gather matter records, prepare an update and manage its approval | What can it read, change and complete without further instruction? |
A chat interface can support any of these. Evaluate the actual permissions and behavior rather than relying on the word "agent." A narrowly scoped workflow with useful source links may be more valuable than a system that offers many tools but cannot reliably finish one task.
The following areas follow the life of a matter, from a prospective client's first message through delivery, billing and closure. They are a planning menu. Each firm should select the relevant pieces according to its practice, clients, information and professional obligations.
An intake agent could turn an enquiry and its attachments into a structured brief: the prospective client, other named parties, the issue described, requested assistance and any dates mentioned. It could distinguish a date supplied by the enquirer from a deadline confirmed by a lawyer, then identify the information still needed.
Conflict-check preparation is a separate task. Software could extract entities, preserve alternative spellings and search the firm's approved records. A useful output would show candidate matches and the reason each was returned. An empty search result should not be presented as a clearance decision; incomplete names and incomplete records still need attention from the responsible team.
After acceptance, a workflow could prepare the opening checklist, populate draft matter fields and assemble an engagement document from an approved template. Scope, terms, acceptance and any required due-diligence decisions remain with authorized people. A potential client should not receive a message implying representation simply because an intake form was processed.
Measure how many intake packets are ready for review, how often staff must request the same information twice, and how much correction the structured record needs. Fast acknowledgement alone says little about whether the matter can move forward.
Files arrive as scans, email attachments, duplicate downloads and revised drafts. An agent could classify incoming material, suggest the correct matter, extract key fields and identify likely duplicates. Keep the original file and its relationship to the extracted text so a reviewer can inspect both.
Version handling deserves particular attention. A recently uploaded document might be an older signed copy, while a document with "final" in its name might still be a negotiation draft. Compare document contents and recorded status; route uncertainty to the team instead of silently replacing an existing record.
Document-request follow-up can then use a matter-specific checklist. If a page is illegible, the workflow should request a readable copy. If an item is genuinely absent, it can prepare a request explaining what is missing. Those are different situations, and collapsing them into one "incomplete" label creates avoidable correspondence.
There are two distinct information problems here. Legal research asks what the relevant authorities say. Internal knowledge retrieval asks what the firm already knows or has approved for reuse.
For research preparation, an agent could break a question into issues, search authorized research sources and organize authorities with their jurisdiction, date and supporting passages. The reviewer needs to check that the authority exists, supports the proposition and remains applicable. A general document search does not establish the current legal status of a case.
For internal knowledge, useful material might include maintained practice notes, approved drafting guidance and reusable precedents. A response should identify the source version and its limitations. A clause negotiated for one client's unusual commercial position should not quietly become the default answer for every new matter.
If licensing terms or an integration do not permit access to a research collection, that is a scope constraint. An agent cannot solve it by inventing a citation or presenting a public-web result as equivalent to the missing source.
A contract-review workflow could compare an agreement with the firm's approved review instructions, find relevant clauses, list departures and prepare questions for the responsible lawyer. Each issue should point to the actual wording, explain the comparison made and distinguish a missing clause from an extraction failure.
For example, a hypothetical supplier agreement might contain a liability provision in the main document and a conflicting provision in a schedule. A useful review brings both passages together. A reassuring summary of the first passage would leave the central problem unresolved.
Drafting support could suggest revisions from approved precedents, prepare a comparison between versions and explain what changed. The lawyer determines the advice and negotiating position. The interface should preserve tracked changes and unresolved comments so accepting a proposed clause does not hide another person's work.
Measure missed issues as well as incorrect flags. A tool that produces fewer comments may be easier to read while overlooking the exceptions the firm most needs to catch.
Litigation support can begin with a sourced chronology. The system extracts a proposed event, identifies the document and page supporting it, and distinguishes the date of an event from the date someone later described it. Contradictory accounts should remain visible.
For document review, an agent could suggest issue tags, identify potentially relevant passages and prepare items requiring privilege review. Those suggestions need evaluation against lawyer-reviewed examples. Finding useful documents is only part of the task; the team also needs to understand what the system missed. No automated flagging process should be described as guaranteeing that every privileged document will be found.
Hearing or deposition preparation could bring together approved exhibits, selected transcript passages and open factual questions. Preserve exhibit identifiers and page references when producing a pack. A polished summary is difficult to use if the lawyer cannot get back to the underlying evidence quickly.
Deadline assistance needs a tighter boundary. Software can extract dates from notices and prepare calendar proposals. The responsible professional must confirm the applicable rule, triggering event and final date before the workflow treats an entry as an authoritative deadline. Attorneys retain strategy, legal judgment, approval, signature and filing decisions.
A matter-status agent could assemble an update from current records, identify the last confirmed milestone and list questions for the responsible lawyer. It should distinguish "no new event recorded" from "nothing has happened." Silence in the system is not evidence about the matter itself.
Meeting assistance could turn an authorized recording or notes into a draft attendance note and action list. Names, amounts, dates and commitments need checking against the source. The firm must first decide when recording or transcription is permitted and how participants are informed.
During a handover, an agent could prepare the current position, outstanding requests, approved next steps and source links for the incoming team member. Client dissatisfaction, an unexplained delay or a request for advice should reach the relevant person rather than receiving a generic automated reassurance.
For these workflows, assess whether the client receives an accurate, useful response and whether the next owner can act on the record. Counting messages generated does not measure service quality.
Time-entry assistance could propose narratives from recorded work for the timekeeper to verify. An opened document or a calendar appointment is evidence to inspect, not proof of the duration of billable work. The system must not invent time or add activity that did not occur.
Pre-bill review could compare entries with the client's approved billing instructions, identifying missing descriptions, inconsistent rates or entries needing review. Distinguish a mechanical discrepancy from a judgment about whether work should be charged. The lawyer and billing team retain the final decision. The ABA's Opinion 512 discusses reasonable fees and time spent using and reviewing AI output in its US model-rule context. ABA guidance on generative AI
Financial administration could also include preparing invoice queries, assembling disputed items or highlighting unmatched ledger records. Keep client-account postings, money movement and write-offs outside the authority of these proposed preparation workflows.
For workload planning, summarize open tasks and review queues using agreed definitions. A lawyer with few recorded hours may still carry urgent, complex work. Capacity reports need context and restricted access; they should not become unexplained performance judgments about staff.
Closure offers another useful scope: identify unresolved tasks, assemble a completion checklist and propose material suitable for the firm's knowledge collection. A lawyer should approve reuse and any necessary redaction before confidential matter content becomes shared guidance.
Retention work needs connections to both policy and exceptions. A workflow can locate candidate records and prepare a proposed action, but it must account for holds and authorized preservation requirements. Deletion should follow the firm's approved process, with recorded decisions and an explanation of what happens to copies, search indexes and backups.
Governance evidence can be gathered along the way: access reviews completed, outstanding exceptions and workflow versions in use. Those records help the firm inspect its operations. Their existence does not itself establish legal compliance.
A transactional team may find version comparison and review against approved instructions useful. A disputes team may need better chronologies and evidence navigation. A high-volume practice may benefit from complete intake and document-request tracking. A smaller firm may place greater value on continuity when one person is unavailable.
These are starting hypotheses. Review a sample of actual work and locate the delay: gathering information, interpreting it, waiting for a decision or entering the result elsewhere. An agent that drafts faster will not remove a queue caused by an unavailable approver.
Choose a process with approved source material, a clear completion state and a person who can judge the result. Complexity and consequences matter alongside frequency. An infrequent task with a difficult-to-detect error can be a poor pilot even when its manual cost looks attractive.
For more examples of where work happens, see the site's legal workflows. If your team serves departments inside a company, the companion in-house legal article examines the additional handoffs among legal, procurement, security and business owners.
The workflow needs a reliable record of what has happened and what is waiting. For the matter-update example, I would separate six responsibilities:
1. Receive the work. Create a request tied to a matter and an initiating user or authorized process. Give it a stable identifier so repeated events can be recognized.
2. Retrieve the permitted material. Enforce matter access in the retrieval service. Return source references and dates with the relevant passages.
3. Prepare the draft. Give the model the allowed context and require it to identify missing or conflicting information. Preserve links between factual statements and their sources for review.
4. Validate the proposed action. Application code checks required fields, destination, permitted operation and current workflow state. The model's request to send is not itself authorization.
5. Obtain the required decision. Show the reviewer the exact draft, recipient and attachments. Record the version approved and reject execution if a relevant change invalidates that approval.
6. Execute and reconcile. Attempt the permitted action, record the result and resolve uncertain outcomes before retrying. An unfinished item remains visible to its owner.
Retrieval-augmented generation, often called RAG, supplies retrieved material to the model when it prepares an answer. It can help ground a response in firm information, but source retrieval and answer verification remain separate checks. A relevant source can still be summarized incorrectly.
The NIST Generative AI Profile identifies confabulation, including confidently presented false content, as a risk. A citation therefore helps a reviewer inspect an answer; its presence alone is not proof of correctness. NIST Generative AI Profile, section 2.2
Map which system owns each fact before adding an agent. The practice-management system might own the matter number and assigned team; the document repository owns file versions; the billing system owns invoice status. Identify those responsibilities explicitly so the workflow does not treat an old email as an authoritative update to the accounting record.
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| Connection | What to establish before implementation |
|---|---|
| Practice and matter management | Stable matter identifiers, assignment changes and permitted field updates |
| Documents and email | Matter access, version identifiers, attachment handling and deletion signals |
| Research collections | Licensed access, coverage and links to verifiable authorities |
| Calendar and task system | Proposed versus confirmed entries, timezone handling and accountable owners |
| Billing and finance | Read/write boundaries, approved definitions and reconciliation responsibilities |
| Identity service | User roles, matter restrictions and behavior when access is revoked |
A connector being available does not establish that it supports the firm's required permissions or actions. Check the actual API, credentials, event delivery and operational limits. If updates are delayed, the review screen should reveal the age of the information used.
Keep matter-specific notes and retrieved material isolated. Any persistent agent memory needs an explicit purpose, access controls and retention policy. A useful fact from one matter must not become unscoped context in another client's answer.
Consider a fictional request for an update on matter M-104. The matter record says a draft is with the client. A newer email says revised instructions were received. The document repository contains two versions of the draft.
The agent finds the discrepancy and prepares an internal review packet. It lists the source dates, links to both versions and asks which document reflects the new instructions. The client-facing draft leaves the unresolved milestone out or marks it for completion before release. It does not guess which record is correct.
The assigned lawyer resolves the discrepancy and approves a specific draft, recipient and attachment. The send action checks that approval against those exact values. If the attachment changes, the previous approval no longer authorizes that communication. If the email service returns an uncertain delivery result, the workflow checks the recorded send state or escalates instead of blindly repeating it.
Completion means a confirmed outcome recorded against the matter, with an owner for anything unresolved. This example can guide an acceptance test: conflicting sources, changed attachments and uncertain delivery must each produce the expected behavior.
Permission to read a document does not imply permission to edit it, share it or send a message based on it. Specify each connected action and enforce authorization in the application and underlying services.
Retrieval should respect the firm's matter and user permissions. Check those permissions when information is requested. Recheck relevant authorization before a consequential action, particularly if access or assignment could have changed while an item waited for review.
OWASP identifies excessive functionality, permissions and autonomy as causes of excessive agency in LLM applications. That is a useful reason to give a workflow only the tools and authority required for its agreed task. OWASP guidance on excessive agency
Confidentiality and professional obligations require the firm's own legal assessment. Technical controls should implement the resulting decisions; an agent's prompt cannot establish them.
In the United States, the ABA's Formal Opinion 512 discusses generative AI in relation to duties including competence, confidentiality, communication and fees. It is a reference for the firm's professional review, not a universal rule for every jurisdiction. The firm should determine the requirements that govern its own work. ABA overview of Opinion 512
For SRA-regulated firms and individuals in England and Wales, the SRA's August 2026 warning addresses inaccurate outputs and confidentiality risks, including the need to assess contractual and technical safeguards. Paying for a tool does not by itself establish that it is appropriate for client information. SRA warning on misuse of AI
Before real client data enters the workflow, trace where it goes: extraction services, model endpoints, search indexes, support systems and logs. Ask what each provider retains, whether it can use the data for training, who can access it, and what deletion or incident handling involves. Hosting location is one part of that review; it does not answer every confidentiality or access question.
The firm should also decide how applicable client instructions, disclosure or consent requirements, and professional rules affect the chosen use. Engineering implements those decisions. It does not determine privilege or supply the firm's legal assessment.
The broader questions about providers, retention and evidence are covered in compliance readiness from day one.
For a hypothetical matter-status workflow, show the reviewer the draft alongside the sources it uses, their dates and any unresolved gaps. Make it clear which recipient and attachments would be included in a send.
Approval should refer to the exact material reviewed. If the text, recipient or attachment changes afterward, the system should handle that change according to the agreed approval policy. A general “approved” flag attached to an evolving task can leave the team uncertain about what was authorized.
Attorneys retain legal strategy, advice, approval, signature and filing decisions. The agent's proposed responsibility here is preparation and coordination within an agreed scope.
Ask what happens when the source is stale, records disagree, an attachment cannot be read, or the assigned reviewer is unavailable. Define what the system can continue preparing and what must wait.
Also test operational failures. If a connection times out after a send attempt, a retry must avoid delivering the same message twice. If the system cannot determine the outcome, route it for resolution rather than repeatedly attempting the action.
Documents and messages can contain misleading instructions. Treat retrieved content as information to inspect, and keep permission checks and action rules outside the model's discretion. A document asking the agent to reveal unrelated matters should never grant that access.
The workflow needs an accountable destination for unfinished work. Otherwise, an automation can appear healthy while difficult items quietly accumulate.
Start by checking the tools the firm already licenses. A supported feature may meet the requirement with less integration and maintenance than a new application. Evaluate it with the same matter samples and review criteria you would use for a custom build.
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| Option | A reasonable fit when | What to examine |
|---|---|---|
| Configure an existing application | The work stays mostly within one system | Permission behavior, review controls and exportability |
| Use a specialist legal tool | Research, document review or drafting is the main task | Source coverage, licensing, quality on your material and review experience |
| Connect several existing tools | The delay is between systems or teams | Matter identity, permission mapping and recovery after partial failure |
| Build a custom workflow | Required actions or review behavior cannot be supported adequately | Engineering ownership, support, evaluation and long-term running cost |
Ask a supplier to demonstrate an incomplete document, revoked access and a failed action, as well as a successful example. Request clarity on what is recorded, what can be exported and what changes when the model or provider is replaced. A screenshot of a finished answer cannot establish how the surrounding workflow behaves.
There is no need to begin with several collaborating agents. Separate components can be useful when responsibilities differ, but each additional handoff creates state, access and failure cases to manage. A single bounded workflow is often easier to evaluate before adding that complexity.
Use authorized test material that includes routine work and known exceptions. Review factual accuracy, source support, missing information, permission enforcement and the effort required to correct an output.
A controlled pilot can then show whether people can complete the task under real operating conditions. Record where staff intervene and why. Faster preparation has limited value if checking the result creates more work elsewhere.
Agree the expected behavior for difficult examples before running the test:
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| Test situation | Expected behavior for this proposed workflow |
|---|---|
| The requested fact is absent | Identify the gap and request review rather than inventing an answer |
| A user loses matter access while work is queued | Deny further retrieval and execution requiring that access |
| A source document contains instructions to disclose unrelated files | Treat the text as untrusted content; deny access and actions beyond the authorized scope |
| A reviewer changes the recipient after approving | Require approval of the changed communication before sending |
| A send times out without a confirmed result | Reconcile delivery state or escalate; do not blindly send again |
Measure unsupported statements, source errors, reviewer corrections and completed work separately. One overall accuracy percentage can hide the specific failure that matters most to the firm. A serious permission failure should block expansion until it has been resolved and retested, even if drafting quality looks good.
Keep enough operational evidence to investigate a run: the item involved, relevant source references, version reviewed, authorization outcome and action result. Protect those records and agree their retention. Avoid copying unnecessary confidential content into general-purpose logs.
For contract review or document triage, use examples labeled by the responsible reviewers. Count both the relevant issues found and the relevant issues missed. Separately count the flags reviewers reject. Missing an important exception and producing an unnecessary comment have different consequences; report them separately.
Evaluate each material category of work, such as scans versus native documents, different languages, or routine versus unusual agreements. A good average can conceal poor behavior on the category that the firm actually intends to use. A model's self-reported confidence is not a substitute for this evaluation.
Keep a set of previously reviewed cases to rerun after changes to prompts, models, retrieval or integrations. Include access and action tests alongside content checks. The quality of the draft and the reliability of the system are separate acceptance questions.
Model usage is only one cost. Include document extraction, search infrastructure, application licenses, integration maintenance, monitoring, reviewer time and the handling of exceptions. Ask which costs grow with the number of pages, requests, users or connected systems.
For a purely illustrative calculation, suppose a firm processes 100 comparable updates per month. If average staff handling falls from 18 minutes to 12 minutes, including review and corrections, that releases 10 hours of staff capacity. If maintaining the workflow consumes another three hours that month, the net capacity gain is seven hours before considering software spend. These are hypothetical inputs, not an expected result.
Capacity is not automatically cash savings or additional revenue. The firm needs to decide whether the released time reduces overtime, improves response times or supports additional work. Keep the benefit model consistent with actual staffing and billing arrangements.
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| Measure | What it tells the firm |
|---|---|
| Staff handling time per completed item | Whether preparation, review and correction effort have fallen |
| Elapsed time to completion | Whether waiting and handoffs have improved |
| Unsupported statements and missed issues | Whether useful speed comes with unacceptable quality loss |
| Exception backlog and its age | Whether difficult work is being handled or merely displaced |
| Total operating cost per completed item | Whether the workflow remains economical at actual volume |
| Use by the intended team | Whether people can and choose to incorporate it into their work |
Record a baseline before the pilot and compare similar work. A change in matter complexity or staffing can otherwise look like an improvement caused by AI.
Start with an agreed scope: who uses the workflow, which records it can access, what completion means and which actions remain outside its authority. Name the business owner, reviewer and technical maintainer. Confirm source access before committing to a delivery schedule.
Then test on authorized historical or purpose-built material. Review errors with the people who understand the work. Fix missing source coverage, unclear rules or inadequate review screens as needed; changing a prompt will not repair every problem.
Where appropriate, run alongside the existing process without taking external actions. This reveals whether real inputs differ from test material and whether someone can handle the exceptions. Moving to limited live use should depend on agreed evidence, including content quality, access checks and operational recovery.
Keep the manual path available and define who can suspend the workflow. Show staff how to inspect sources, correct a draft and report a problem. Useful feedback records the type of failure and its effect, rather than only a thumbs-up or thumbs-down.
Before broadening a workflow, review its failures and confirm who will maintain integrations, permissions, evaluation material and escalation routes. An agent connected to changing firm systems needs continued ownership.
The next step may be a better source connection or a clearer review screen. It may be another task in the same process. Greater autonomy is one possible change, and it needs its own justification.
The next opportunity should come from the same examination of real work. A successful update workflow may reveal that document versioning needs attention before another agent is useful. An intake pilot may expose unclear routing responsibilities. Those findings are valuable even when the resulting improvement is ordinary software or a clearer process.
AI agents can support a wide range of work across a firm. A sound implementation makes that work easier to complete, check and hand over. Start where the team can verify the result, then extend the scope on evidence from actual use.
To scope implementation, bring one recurring task, its source systems and the decisions that need a person. Legal AI engineering describes how those requirements translate into integrations and reviewable software.
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