AI can help lawyers begin research more efficiently by generating preliminary lists of potentially relevant cases, statutes, regulations, and secondary sources; suggesting search terms and issue trees; and summarizing authorities supplied by the lawyer. It can also extract procedural history, legal standards, cited authorities, and potentially relevant factual comparisons from legal materials.

For drafting, AI can turn lawyer-created outlines, notes, deposition excerpts, or deal terms into a working draft. It can assist with initial versions of client emails, engagement letters, demand letters, discovery correspondence, and internal memoranda. It may also help prepare plain-language explanations of lawyer-reviewed material for client review.

AI can generate alternative clauses, redline options, document structures, or argument outlines for a lawyer to compare and refine. Before relying on any output, the lawyer must verify facts, legal authorities, quotations, and conclusions; tailor the work to the client’s objectives; and approve the final product [1].

AI can also make internal knowledge easier to locate and reuse. A permission-controlled system may search the firm’s prior briefs, memoranda, precedents, model documents, practice-group guidance, and playbooks for approved language, comparable matters, common negotiation positions, and lessons learned. Firms should preserve matter-level access controls and provide links to the underlying source material so lawyers can evaluate context and continuing validity.

AI can summarize lengthy contracts, deposition transcripts, pleadings, medical records, regulatory filings, and correspondence. It can extract dates, people, obligations, monetary amounts, and defined terms; factual timelines, witness summaries, and document inventories; and identify duplicates, near-duplicates, unusual documents, or potentially missing material.

In e-discovery, AI can classify and prioritize documents by issue, custodian, topic, or likely relevance. Technology-assisted review, including predictive coding, can help legal teams work through large document collections and flag material that may contain privileged, confidential, or personally identifiable information. It can assist in the review process, but final decisions concerning privilege, responsiveness, redaction, and production require attorney review and supervision.

AI can also assist litigation preparation by organizing the record and extracting potentially disputed facts, admissions, inconsistencies, and references to key events. It can summarize pleadings, motions, discovery responses, expert reports, hearing transcripts, and deposition testimony. It may generate first-pass deposition outlines, witness-preparation materials, discovery requests and responses, privilege-log drafts, exhibit lists, witness lists, and demonstrative outlines. Lawyers must confirm that these materials accurately reflect the record, comply with applicable court rules, and support the litigation strategy.

Legal-analytics tools serve a different function: they identify patterns in public court and docket data involving judges, courts, opposing counsel, parties, venues, motion practice, case timing, damages, findings, and outcomes. For example, Lex Machina offers analytics concerning judges, courts, counsel, parties, case resolutions, damages, and timelines that may inform case assessment, budgeting, venue analysis, and litigation strategy [2].

In transactional work, AI can help teams review data rooms and contract portfolios at scale. It can identify and extract change-of-control provisions, assignment restrictions, termination rights, consent requirements, notice dates, renewal deadlines, payment obligations, and other terms that may affect a transaction. It can compare materials against diligence request lists, identify potential gaps, build contract inventories or diligence matrices, and organize open issues and follow-up questions. AI can also compare similar agreements to identify nonstandard terms or potentially missing provisions and prepare preliminary summaries of corporate records, leases, intellectual-property agreements, financing documents, and employment agreements.

For contract analysis, AI can extract important terms into a consolidated table, compare agreements against an approved playbook, flag deviations from preferred language or negotiation positions, compare versions, and summarize substantive changes. It can group contracts by risk factor, business unit, counterparty, term length, or governing law and generate a preliminary issue list for business stakeholders.

AI is especially useful for high-volume, repeatable work. It can generate first drafts of standard forms from structured client or matter data; assemble routine filings, engagement letters, discovery requests, notices, closing documents, and client questionnaires; and generate routine document packets using attorney-approved templates and rules.

AI can also extract information from PDFs, scanned documents, forms, invoices, and correspondence and convert it into searchable databases, timelines, spreadsheets, or tables. It may detect missing fields, inconsistent dates, incomplete form responses, and blank signature blocks.

AI can support client service and matter management by summarizing client communications and meeting notes into proposed action items or client updates; flagging unanswered client questions and upcoming obligations; drafting calendars of deadlines; and generating dashboards. Substantive legal advice, deadline changes, settlement discussions, and highly sensitive client communications require attorney review before they are sent.

AI-supported analysis can help firms estimate litigation duration, likely motion timing, potential cost ranges, staffing needs, discovery burden, and the risk of budget overruns or missed deadlines. AI can also support compliance work by monitoring regulatory developments, preparing first-pass summaries of potential business impact, mapping policies or contracts against regulatory or internal-control checklists, and identifying potentially missing disclosures, approvals, retention requirements, or contract provisions. It may flag possible conflicts of interest or privacy, confidentiality, and personal-information issues for human review.

AI can generate information that sounds credible but is false, incomplete, or misleading—a problem commonly called a hallucination or confabulation [3]. The tool may produce citations, quotations, facts, statutes, or case descriptions that do not exist, or it may inaccurately characterize an authority that does exist. Lawyers must independently verify legal authorities, factual assertions, quotations, and substantive conclusions before relying on AI-assisted work [1].

AI may rely on outdated or unverified law, including superseded statutes or overruled cases. It may apply the wrong jurisdiction’s rule or an incorrect legal standard. Because AI does not possess the comprehensive legal understanding or contextual judgment of a trained attorney, it may also overlook or misinterpret important information, particularly in poorly scanned, lengthy, or atypically drafted documents.

AI may reproduce or amplify biases reflected in its training data or outputs, potentially generating unfair risk assessments, screening results, or recommendations that disadvantage certain groups or favor particular parties. AI-generated output may also sound polished and authoritative even when it is uncertain, incomplete, or wrong. This can cause reviewers—particularly those working under time pressure—to defer too readily to the tool rather than scrutinize and independently verify its work. NIST identifies harmful bias, automation bias, and overreliance as important generative-AI risks that require context-appropriate human oversight [3].

Confidentiality is a significant consideration when using AI. Entering client information into an AI tool without appropriate contractual protections, privacy safeguards, security controls, or a clear understanding of the provider’s data practices may expose confidential information and violate professional obligations.

Before using an AI tool with client or firm information, firms should understand how the provider handles inputs and outputs, whether it retains or uses data, who can access it, whether data may be used for model training, and how the firm will be notified of a security incident or demand for information.

AI also lacks the complete context necessary to evaluate a legal matter. Unless a lawyer provides accurate and relevant information, the system may not account for the client’s history, objectives, circumstances, risk tolerance, business considerations, or procedural posture. Even when information is supplied, AI may fail to appreciate the nuances that shape the appropriate advice for a particular client or matter.

Once the firm’s needs are clear, evaluate available tools and prioritize those most likely to deliver a strong return on investment. Options range from general-purpose AI assistants and legal-specific research and drafting platforms to document-review and contract-analysis tools, firm knowledge-management systems, workflow automation tools, and privately hosted or enterprise AI systems. Each category offers different capabilities, security considerations, and integration requirements. The best choice depends on the firm’s specific use cases, existing systems, budget, and risk tolerance.

Vendor evaluation should extend beyond a product’s advertised capabilities. Firms should ask how customer data is used, whether it is retained for model training, where it is stored, and what security certifications and controls are in place. They should also determine whether the firm can audit usage and outputs, how the tool handles citations and sources, and what happens if the service changes, becomes unavailable, or fails. Compatibility with existing software and workflows is another important consideration, since even a capable tool may provide limited value if it is difficult to integrate into the firm’s daily operations.

NIST’s Generative AI Profile is a useful resource for assessing risks throughout an AI system’s lifecycle, including risks involving human-AI interaction, information integrity, privacy, security, and third-party systems [3].

Finally, firms should test tools before adopting them. A vendor demonstration rarely reveals how well a product will perform in the firm’s actual working environment or on its specific legal tasks. Instead, involve a small group of attorneys and staff in a structured pilot. Develop standardized instructions, provide relevant templates and sample materials, and ask participants to apply the same repeatable workflow to comparable tasks. The pilot should evaluate accuracy, consistency, usability, time savings, integration with existing work, and the amount of human review required.

This approach allows the firm to compare tools based on practical results rather than marketing claims. It also gives the firm an opportunity to identify safeguards, training needs, and workflows that must be changed before broader use.

The policy should establish an approved-tools register and clearly define which tasks may be performed with AI, which require restrictions or additional oversight, and which are prohibited. For example, a firm might approve AI for initial contract review while prohibiting its use for drafting wills without specialized safeguards and attorney oversight. The policy should explain which tasks AI is well suited to perform, such as preparing first drafts, identifying potential issues, and reviewing documents, as well as where human review must remain mandatory. Above all, it should reinforce that AI is intended to streamline legal work, not replace the attorney’s independent professional judgment.

A central component of the policy should be clear standards for verifying AI-generated output. Lawyers must understand why verification matters and how to conduct it effectively. Citations, quotations, statutes, factual assertions, and procedural statements should be checked for accuracy and reliability. Attorneys should use authoritative legal research tools and citators to confirm that cited authorities exist, remain current, and support the propositions for which they are cited. AI-generated citations should never be relied upon without independent verification.

The policy should also establish heightened review requirements for higher-risk work, including legal conclusions, client advice, filing-ready documents, court submissions, contract language affecting material rights, and determinations involving privilege or confidentiality. The responsible attorney must understand, approve, and be able to defend the final work product.

The policy must also address client confidentiality, data security, and the ethical obligations governing AI use. These considerations should shape implementation from the outset rather than be addressed only after a problem arises.

Firms should evaluate how each approved tool collects, stores, retains, and reuses data. When relying on an external vendor, firms should review the vendor’s data-handling practices, retention terms, contractual protections, security controls, and incident-notification commitments. Firms that host or control their own AI systems should establish rules governing data retention and deletion, audit logging, access controls, and user permissions.

The policy should identify what information may and may not be entered into an AI system. It should address confidential and privileged information, personal data, trade secrets, and information subject to contractual or regulatory restrictions. It should also provide procedures for anonymizing or redacting sensitive information and for writing prompts that minimize unnecessary disclosure [1, 3].

Risk management should be incorporated into the governance framework. Firms should develop a risk register or mitigation matrix that identifies potential harms, assesses their likelihood and severity, and establishes appropriate safeguards.

An incident-response plan should specify how to respond if confidential information is exposed, sensitive data is mishandled, or an AI tool produces materially misleading or harmful output, or a vendor experiences a security event. Resources such as the National Institute of Standards and Technology’s AI guidance can help firms develop structured approaches to identifying and managing these risks. NIST AI 600-1 specifically addresses risk identification and mitigation, incident-response planning, incident disclosure, error tracking, continuous monitoring, and third-party AI-system risks [3].

The policy should address communications about AI use with clients, courts, opposing parties, and vendors. Whether disclosure is required depends on applicable professional obligations, court rules, client expectations, contractual requirements, and the specific circumstances of the representation.

The policy should require lawyers to assess disclosure questions under the governing rules and, when necessary, consult firm ethics counsel or another appropriate authority. Formal Opinion 512 discusses lawyers’ obligations to communicate with clients about the means used to accomplish representation objectives [1].

AI governance requires ongoing oversight. Firms should consider designating an accountable executive or establishing an AI steering group to oversee implementation, monitor compliance, evaluate emerging risks, coordinate policy updates, and review significant incidents.

Approved AI tools should be tested periodically for accuracy, reliability, security, and potential bias. Policies should be reviewed regularly as technology, professional guidance, court rules, client expectations, and applicable law change. The ABA Task Force’s work underscores that the legal profession must address AI’s impact on practice, governance, ethics, and public trust on an ongoing basis [4].

All users should understand the fundamentals of responsible AI use, but training should reflect the different responsibilities and risks associated with each role. For example, lawyers need to understand verification, confidentiality, client communication, supervision, and professional accountability; while IT and security staff need to understand approved-system configuration, access controls, vendor risk, data handling, monitoring, and incident response.

Training should cover AI’s capabilities and limitations, common failure modes, effective prompting techniques, and best practices for verifying AI-generated output. Staff should learn to recognize potential bias, identify hallucinations, unsupported claims, incomplete analysis, and instances in which the system lacks important context.

Training should also cover data protection, confidentiality, and the firm’s rules governing what may be entered into each approved AI system. Practical scenarios can demonstrate how errors occur, how they can affect legal work, and when a user should seek guidance, escalate a concern, or report a potential incident.

Formal Opinion 512 states that lawyers using generative AI must satisfy their professional obligations, including the duty of competence and the duty to protect client information [1].

Importantly, AI training should be treated as an ongoing professional competence requirement rather than a one-time onboarding exercise. Firms should provide regular updates as tools evolve and as professional guidance, firm policies, security risks, and client expectations change. The ABA Task Force’s 2025 report identifies legal education and the responsible use of AI as significant issues for the profession [4].

Efficiency can be assessed by comparing the time required to complete tasks with and without AI. Turnaround time should reflect the entire workflow, including the time spent reviewing, verifying, and correcting AI-generated output, to determine how quickly the firm can deliver a finished product to the client.

Firms should also evaluate the quality and accuracy of AI-assisted work by tracking error rates, rework rates, and the frequency of corrections needed before a deliverable is finalized. They can estimate cost savings per case, matter, or workflow by comparing the resources required for a given task before and after adoption.

Where appropriate, firms may examine case outcomes as one contextual indicator, while recognizing that outcomes reflect many variables and cannot reliably be attributed to AI use alone.

Firms should assess client satisfaction and retention to determine whether AI-assisted services are improving quality, responsiveness, and perceived value. They should also monitor lawyer and staff adoption to understand whether approved tools are being used effectively and consistently.

Security and compliance incidents should be tracked to identify data exposures, policy violations, inaccurate outputs, or other operational risks. Together, these measures help a firm decide whether a tool should be maintained, improved, expanded, restricted, or retired.