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Generative AI can help lawyers research, review documents, summarize records and prepare first drafts faster—but it does not take responsibility for the legal answer. The firms most likely to benefit will pair useful automation with lawyer verification, confidentiality safeguards, staff training and clear measures of client value.
What “supercharged” legal work looks like
In legal practice, generative AI is most useful as an assistant for bounded, reviewable work—not as an autonomous lawyer. It can reorganize information, produce a starting point or help surface issues. A licensed lawyer still needs to determine what the work means, whether it is correct and what advice or action is appropriate.
That distinction matters because a fast draft is not necessarily a reliable one. The aim is to use AI to expand a team’s capacity while preserving the professional judgment, accountability and client service that make the work valuable.
Where law firms can use generative AI
Strong early workflows have defined inputs, repeatable steps and a human checkpoint before the output affects a client or matter. Legal research, document review and document summarization are among the leading legal GenAI use cases identified by Thomson Reuters.
Research preparation
Ask an approved tool to turn a legal question into a research outline, identify issues to investigate or organize authorities and questions for follow-up. Treat the result as a map for research, not as proof that a case exists or remains good law. Verify every authority and proposition against primary sources.
Document review and extraction
For a defined collection of contracts or other records, AI can help extract clauses, identify recurring terms or flag documents that merit closer review. Set the scope in advance, test whether the system misses relevant material, and have a lawyer review consequential findings.
Chronologies and summaries
A tool can create a first-pass chronology or summarize a closed document set for a lawyer to check. Keep references to the underlying material so reviewers can trace factual statements back to their source; do not rely on an untraceable summary where a missed detail could affect advice or strategy.
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AI can help prepare internal templates, an initial draft for lawyer revision or questions for a client meeting. The responsible lawyer should confirm that the language fits the matter, reflects the client’s facts and does not introduce unsupported legal or factual claims.
Rank #2
How to make AI output trustworthy enough to use
A 2024 reliability study by Stanford and Yale researchers found false-information rates of 17% to 33% in outputs from several leading AI legal-research tools tested in the study. That result is a warning about the tools and outputs studied, not a universal error rate for every legal AI product or task. It does establish why verification belongs in the workflow.
- Define the task and source set. Specify the jurisdiction, relevant dates, document collection and question. For legal research, seek traceable authorities and primary-source retrieval.
- Check the substance. The responsible lawyer should verify every citation, quotation, factual assertion, calculation and jurisdictional assumption against the underlying authority or record.
- Test edge cases. Use representative matters and questions, including unfamiliar or adversarial prompts, to see whether the tool misses issues or produces plausible but unsupported answers.
- Review before external use. Do not file, send to a client or otherwise rely on unverified output. Record who reviewed it and what was checked when the matter’s risk warrants an audit trail.
Good verification is part of the work, not optional polishing. If the review needed to make an output safe costs as much as doing the task without AI, the workflow may need redesign—or may not be a good AI use case.
Protect client information and meet professional duties
ABA Formal Opinion 512 (2024) says lawyers using generative AI must “fully consider their applicable ethical obligations,” including competence, confidentiality, client communication and reasonable fees consistent with time spent using GAI. The opinion is an important U.S. professional-guidance reference; lawyers should also apply the rules and requirements that govern their own jurisdiction, engagement and practice.
Turn confidentiality into operating rules
Do not enter client information into an unapproved public AI tool. Before approving a system, assess its data handling, retention and training-data terms, access controls, administrative settings and vendor agreements. Classify information so staff know what may be used, in which tools and under what protections. Define how to report and respond to a suspected disclosure.
Set responsibility and supervision
Identify which tasks require licensed-lawyer judgment, who supervises AI-assisted work and who is accountable for the final output. Train lawyers and staff to challenge results, recognize unsupported answers and preserve independent analysis rather than treating fluent language as evidence of correctness.
Communicate and charge fairly
Determine when the client or engagement terms require disclosure or consent, and communicate material changes in service as applicable. Track how AI affects time, staffing and deliverables so fees remain reasonable and consistent with the work performed; do not bill as though time was spent when it was not.
For a deeper policy and implementation reading list, the ABA Task Force and ABA Science & Technology Law Section released Artificial Intelligence: Legal Issues, Policy, and Practical Strategies, with contributions from more than 40 authorities.
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What a practical law-firm AI policy should cover
A policy should tell people what they may do, how to do it safely and what happens when something goes wrong. It should be operational enough that a lawyer or staff member can apply it on a live matter.
- Approved tools and use cases: name authorized systems and the tasks permitted for each, including any restrictions on public tools.
- Information rules: define data classifications, prohibited inputs, retention expectations, access permissions and review of vendor terms.
- Human review: set verification and sign-off expectations for research, factual analysis, drafts and client-facing work.
- Client and billing practices: explain when communication or consent may be needed, how AI-related changes are documented and how fees are kept reasonable.
- Supervision and training: assign owners, train users to evaluate outputs and maintain the skills needed to perform legal analysis independently.
- Testing and incident response: test tools against representative matters and define a reporting path for errors, confidentiality concerns or unexpected behavior.
- Review cadence: revisit approvals and rules as products, contracts, professional guidance and firm workflows change.
Choose tools for fit, not just fluent answers
A tool evaluation should compare more than the quality of a demonstration. Test the system on work resembling the firm’s real matters, and include the people who will use, supervise and administer it.
| Evaluation area | Questions to answer |
|---|---|
| Authority coverage and traceability | Does it cover the relevant jurisdictions and practice areas? Can users trace legal propositions to sources and verify them? |
| Accuracy and robustness | How does it perform on representative, unfamiliar and adversarial questions? What does it do when it lacks a reliable answer? |
| Confidentiality and controls | What are the data retention and training terms? Are access controls, administrative settings and audit logs adequate? |
| Workflow integration | Does it fit the firm’s document-management and billing systems, and can it support the intended matter workflow? |
| Practice and jurisdiction fit | Has the firm tested its performance for the jurisdictions, languages, client populations and work types it handles? |
| Implementation burden | What training, supervision and change management will adoption require, and who will maintain the policy? |
| Cost and client value | What is the total cost, including review time and integration, and how will any efficiency benefit improve service or pricing? |
The ABA’s Formal Opinion 512 and practical-strategy resource support treating governance and implementation as part of the decision, rather than as afterthoughts to procurement.
Measure whether AI improves client service
Time saved alone does not show that a firm has created value. Establish a baseline for the selected workflow, then monitor whether the change improves the work without shifting hidden costs or risk to clients.
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- Rework and citation-error rates
- Knowledge reuse and consistency across matters
- Matter outcomes and client satisfaction
- Whether efficiency gains are reflected in better service, more useful deliverables or appropriate pricing
Thomson Reuters reports that clients increasingly expect firms to examine GenAI options and deliver efficiency benefits. That expectation makes it important to explain how the firm uses the technology and to connect adoption to a tangible service improvement.
Best Value
How AI may change law-firm work and billing
AI can shorten some tasks, but that does not by itself determine how a firm should price a matter or whether a lawyer’s role disappears. Firms still need to account for review, supervision, advice and responsibility. Where AI changes the time or staffing involved, firms should document the difference and charge reasonably under applicable obligations.
Thomson Reuters describes a market progression from experimentation with public tools, to embedded business and industry-specific systems, and then to emerging agentic capabilities. Its 2025 report says 70% of legal professionals expect AI and GenAI to have a transformational or high impact on the profession within five years. These are reported expectations, not a guarantee that any particular firm or task will change on that timetable.
What adoption figures say—and what they do not
Survey figures indicate increasing interest and use, but they measure reported attitudes or adoption rather than legal accuracy, client outcomes or return on investment. The populations and questions differ, so the numbers should not be treated as directly interchangeable.
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| Finding | Population and source | What it indicates |
|---|---|---|
| 81% said GenAI can be applied to industry work; the figure was 85% among law firms and corporate legal departments | 1,128 professionals surveyed; Thomson Reuters Institute, 2024 | Perceived applicability, not verified deployment or results |
| Organization-wide active GenAI use nearly doubled to 22% in 2025 from 12% in 2024 | Thomson Reuters Institute, 2025 | Reported active use at organizations |
| 55% said they were hopeful or excited about GenAI’s future | Professional-services respondents; Thomson Reuters Institute, 2025 | Sentiment about future prospects |
| 59% of law firms and 57% of corporate legal departments believe GenAI should be applied to their work | Thomson Reuters Institute, 2025 | Belief that the technology has a role, not proof of safe or effective use |
| 70% expect AI and GenAI to have a transformational or high impact on the profession within five years | Legal professionals; Thomson Reuters analysis | Expectation about future impact, not a measured forecast |
A sensible starting point for a firm
- Select one bounded workflow with repeatable inputs and a clear lawyer checkpoint, such as summarizing a closed document set or extracting clauses.
- Choose an approved tool only after reviewing its data practices, access controls, traceability and fit for the relevant work.
- Define the review standard, responsible lawyer, confidentiality rules and client-communication expectations before a pilot begins.
- Train the users, test on representative material and record errors, review time and rework as well as speed.
- Expand only if the workflow produces a demonstrable client or firm benefit without weakening confidentiality, competence or accountability.
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