How Do Lawyers Use AI? A Guide to Legal Automation with 10 Real-World Use Cases

Legal professionals and business teams across industries are facing mounting pressure to review more documents, manage growing volumes of agreements, and clear manual bottlenecks without compromising accuracy.

Artificial intelligence offers a practical path forward for navigating these growing demands, but exactly how do lawyers use AI today while maintaining strict professional conduct?

Rather than replacing qualified legal judgment, solutions like generative AI tools and machine learning applications serve as powerful support systems that help professionals work through information-heavy tasks more efficiently.

Here are some common use cases and examples that perfectly illustrate how these technologies are enhancing the way legal professionals operate.

Key takeaways

• Lawyers use AI to streamline routine legal workflows like summarizing documents, researching case law, drafting initial communications, and reviewing contracts.

• AI helps legal and business teams extract key terms, compare clauses against approved language, and manage large-scale discovery efficiently.

• While AI increases speed and visibility, legal teams can proactively manage risks related to accuracy, confidentiality, bias, and professional responsibility.

• When evaluating AI software, legal operations leaders often prioritize platforms with robust security controls, source traceability, and built-in human review checkpoints.

What does AI mean for the legal profession?

In a legal context, artificial intelligence generally refers to software designed to process large volumes of information, recognize text patterns, and assist with complex, data-heavy tasks.

Within the legal industry, these capabilities typically fall into a few distinct categories:

• Generative AI can help draft, summarize, and answer questions using large language models trained on legal documentation and domain-specific datasets.

• Legal research AI can assist in organizing inquiries and identifying case law starting points.

• Document and contract analysis AI can be used to extract terms, flag clauses, and summarize obligations.

• Workflow automation can help route approvals and follow-ups across business and legal teams.

Ultimately, these tools shift how legal professionals spend their billable hours. 

Rather than starting every document from scratch or manually hunting for specific clauses, practitioners use AI to establish a baseline or surface key data instantly. This allows attorneys to shift their focus from manual data processing to what technology cannot replicate: applying strategic judgment, providing nuanced legal advice, and overseeing complex legal matters.

How lawyers are using AI: 10 real AI use cases in legal workflows

AI adoption is reshaping how practitioners handle high-volume, data-intensive tasks. The following examples highlight how large law firms, in-house counsel, and legal assistants are applying these tools to practical legal matters.

1. Summarizing complex legal documents

AI helps lawyers summarize lengthy agreements, court filings, internal policies, and research materials into concise overviews of key points and next steps. Summarization provides a quick orientation to help prioritize review efforts before deep-diving into a text, ensuring time is allocated where it can make the most impact.

Real-world use case: In its enterprise rollout, A&O Shearman opens in a new tab reported using Harvey for routine tasks such as summarization, analysis, translation, and drafting support across thousands of staff members. By integrating this technology, attorneys reportedly save an average of 7 hours per contract review. This equates to cutting overall review time by 30%, giving their 4,000 staff members significant bandwidth back to spend on high-value, strategic client work.

2. Supporting legal research

AI systems assist lawyers in identifying starting points for inquiries, organizing complex legal issues, and generating targeted questions for deeper review. This assistance is often most valuable early in the process, helping teams understand the broader regulatory context and narrowing down focus areas. Verification of citations, jurisdictional authority, and current law remains an essential step in this process.

Real-world use case:Vanderbilt opens in a new tab notes that legal research platforms integrate natural language processing to summarize cases, while other AI tools support litigation analysis and flag risky clauses. Because AI can scan thousands of cases in mere seconds to highlight relevant precedents, it can dramatically accelerate the decision-making process. Consequently, surveys show opens in a new tab that most legal professionals now expect AI to have a transformational impact on their practice, expecting it to free up nearly 240 hours per year.

3. Drafting first-pass legal documents and communications

Producing initial drafts of memos, letters, clauses, client updates, and routine correspondence often consumes valuable time. AI helps lawyers overcome the blank page by generating a foundational draft. Attorneys can then take this starting text and revise it for accuracy, specific tone, overarching legal strategy, and jurisdiction-specific requirements.

Real-world use case:A&O Shearman opens in a new tab noted that its lawyers use AI to generate legal content. Teams typically use these tools as a starting point to rapidly produce initial drafts that attorneys later refine. During an initial trial phase, approximately 3,500 of the firm's lawyers leveraged the generative AI platform to perform around 40,000 queries for their day-to-day client work. This massive scale of adoption highlights how effectively AI can remove the friction of the "blank page," enabling teams to deliver faster and more cost-effective solutions to clients.

4. Preparing claims, demand letters, or pretrial materials

AI can structure routine claims workflows by analyzing uploaded contracts, invoices, and supporting facts. This information is then used by lawyers, who remain responsible for drafting demand letters and pretrial materials filed with courts. This application streamlines document preparation for repetitive matters where facts map easily to a standard template.

Real-world use case:Garfield AI opens in a new tab secured a county court victory in a small-debt case by using AI-supported workflows to efficiently prepare the matter. The firm utilized AI to generate pre-action correspondence and issue court proceedings, ultimately winning a £7,000 award for a freelancer seeking unpaid fees. By drastically reducing the cost and time of litigation preparation, these AI tools made it economically viable to pursue a claim that a business might otherwise have been forced to write off.

5. Reviewing contracts for risk

Managing high contract volumes often strains legal operations. AI technology helps lawyers scan agreements for risky clauses, missing provisions, unusual terms, or significant deviations from standard language. Initial triage with AI-powered tools enables legal teams to prioritize documents that require immediate human attention, accelerating the review cycle and mitigating risk.

Real-world use case: Docusign’s AI contract analysis is changing the review equation by helping accelerate risk detection and giving legal teams clearer visibility into what is happening in every agreement at scale. Organizations currently waste an estimated 55 billion hours globally each year due to manual review and disconnected workflows, causing an 18% increase in time spent. By automatically identifying key clauses and deviations within seconds rather than hours, AI software helps eliminate these bottlenecks and prevent revenue-stalling delays.

6. Extracting key terms and obligations from agreements

AI tools extract structured information directly from agreements, including renewal dates, payment terms, termination rights, and governing law. Instead of manually opening individual contracts, teams can use AI to convert raw agreement text into searchable data, improving project management by providing enhanced visibility into obligations.

Real-world use case: Docusign’s agreement AI allows legal teams to use AI workflows to automatically identify and record critical data buried in agreements and standardize unstructured metadata, such as renewals, payments, and termination rights. Rather than relying on tedious manual entry, the AI engine can capture custom agreement data at scale. This automated extraction turns static repositories into proactive systems that can send automated reminders for scheduled obligations, improving compliance.

7. Comparing clauses against precedent or approved language

Legal professionals frequently use AI to compare proposed clauses against existing precedents, approved templates, standard terms, or established fallback language. This ensures drafts maintain consistency with preferred corporate language. While AI accelerates precedent retrieval, lawyers still decide whether a suggested clause appropriately fits the specific deal, commercial context, and risk profile.

Real-world use case: To measure how well AI handles precedent retrieval, developers use the ACORD opens in a new tab dataset to evaluate an AI model's ability to retrieve and recommend relevant clauses for contract drafting, ensuring these time-saving tools maintain the accuracy and reliability legal teams require. The benchmark challenges models with 114 complex legal queries and over 126,000 query-clause pairs, expertly rated on a 1-to-5-star scale by attorneys. By rigorously testing AI against this massive volume of annotated data, developers can pinpoint exactly where models need substantial improvements.

8. Supporting negotiation and redlining

AI significantly supports the negotiation process by identifying recently changed terms, suggesting alternative language, and generating initial redlines. These systems help compare an incoming contract against a company playbook to reduce bottlenecks, making negotiation workflows more consistent and allowing teams to focus on high-level strategy rather than line-by-line comparisons.

Real-world use case: Docusign’s AI-assisted contract review can help teams analyze agreements, suggest edits, draft playbooks, and reduce manual redlining work. By leveraging these conversational AI tools, Docusign’s internal legal team saved up to 15 minutes on every non-disclosure agreement (NDA) they processed. Furthermore, they successfully reduced the time spent negotiating Master Services Agreements (MSAs) by 30 to 60 minutes, proving that AI can significantly accelerate the path to signature.

9. Reviewing large document sets for discovery or investigations

During internal investigations, due diligence, or complex litigation, legal teams often need to find specific facts hidden within electronically stored information. AI helps legal teams classify, prioritize, summarize, and organize massive document populations. This technology-assisted review accelerates the discovery process, although attorneys maintain strict supervision over the software for privilege-sensitive review decisions.

Real-world use case:EDRM opens in a new tab has used generative AI review to organize documents in discovery through prompt testing and hybrid workflows. Facing a need to categorize 7,100 conceptually similar documents across nine complex sub-issues, attorneys created targeted prompts for the AI to sort the data. By testing these prompts on curated sets of 10 to 15 documents, the team successfully optimized the AI’s categorization accuracy in a fraction of the time required by traditional methods.

10. Monitoring regulatory changes and compliance obligations

AI helps legal teams monitor regulatory updates, summarize complex legislative changes, and turn new requirements into clear action items. This proactive tracking helps organizations stay ahead of compliance obligations while human experts interpret nuances, prioritize responses, and advise on implementation.

Real-world use case: Taylor Wessing uses an AI-powered Horizon Scanning opens in a new tab solution that pairs legal expertise with AI efficiency to deliver tailored regulatory updates, impact analysis, and actionable next steps. The underlying AI continuously scrapes and scores thousands of trusted sources, using large language models to produce plain-English abstracts and impact heatmaps. This automation eliminated the need to manually sift through hundreds of disparate channels, dramatically reducing resource overload.

What are the risks and limitations of AI in legal work?

Legal AI tools can create substantial value but require appropriate oversight, robust governance, and professional review to function effectively within a firm. Integrating technological advancements into the practice of law introduces specific ethical questions that teams must address proactively. Commonly observed risks and mitigation practices include:

• Accuracy and hallucinations: AI systems can generate incorrect information, false citations, or unsupported conclusions. Standard practice involves verifying citations and cross-referencing source materials.

• Confidentiality and data security: Entering sensitive client data into unapproved public models poses privacy risks. Many organizations mitigate this by using approved, enterprise-grade tools with strict security controls.

• Bias and incomplete context: AI outputs sometimes reflect gaps in data, training, or source materials. Maintaining human review checkpoints across all legal workflows helps address missing context.

• Professional responsibility: According to the American Bar Association model rules, lawyers remain entirely responsible for legal advice, court filings, client communication, and final review. Firms often establish internal guidelines detailing exactly when and how their teams may use AI.

AI supports legal work and saves valuable time, but it serves as a supplement rather than a substitute for an attorney's duty to provide legal services with independent, qualified judgment.

Choosing AI tools for legal and agreement workflows

Selecting the appropriate AI-powered solution depends on the specific use case and whether it involves legal research, drafting, or cross-functional operations. Because the legal profession demands rigorous standards, successful adoption requires platforms built for the ethical nuances of legal work.

Legal teams should focus on solutions that naturally support their professional obligations by prioritizing enterprise-grade security, strict data governance, and seamless system integration. The most effective tools also emphasize transparency by offering clear source traceability, comprehensive audit trails, and built-in human review checkpoints, enabling attorneys to confidently verify AI-generated insights.

Ultimately, modern legal practice transforms when organizations combine the speed of automation with the qualified oversight of human professionals. By thoughtfully integrating the right tools, legal teams can eliminate administrative bottlenecks and dedicate their expertise to the strategic work that matters most.

Discover how the Docusign IAM platform helps legal and business teams apply AI to agreement workflows, uncover contract insights, and manage agreements with greater speed and control.