Sample Uses of AI in Law Practice

Law firms are increasingly embracing artificial intelligence (AI) to streamline work and enhance client service. Approximately 79% of surveyed legal professionals report some use of AI in their firms. Clients, too, are beginning to expect law offices to leverage modern AI tools for efficiency. This white paper outlines practical examples of how AI is being used in legal practice today - and what's on the horizon - across four key categories: Reading, Writing, Learning, and Operations.

The focus is firm-wide, offering concrete use cases that any law firm - whether in Texas or elsewhere - can appreciate, without assuming any technical background. Each section below describes real-world applications in plain English to help demystify how AI can support everyday legal work.

Reading: AI as a Speed Reader and Analyzer

AI excels at "reading" and digesting large volumes of text. Tasks that once required teams of junior attorneys or staff poring over documents can now be accelerated with AI assistance.

Here are some ways firms are using AI to tackle reading-intensive work:

Contract Review and Due Diligence

AI tools can rapidly review contracts and complex documents to spot key clauses, deviations, or risks. For example, machine-learning contract review software can scan a stack of contracts for specific provisions or changes and output a convenient summary in seconds. This helps attorneys conduct deal due diligence faster and with greater consistency, flagging clauses that need attention. Instead of manually combing through hundreds of pages, lawyers get a snapshot of the important points (e.g. renewal dates, indemnity clauses, unusual terms) identified by the AI.

Case Law Research Summaries

Legal research platforms are beginning to incorporate AI that can read and summarize case law or statutes. An attorney can ask a question in natural language and receive a synthesized answer with references, rather than manually pulling and skimming dozens of cases. For instance, OpenAI's ChatGPT and similar generative AI chatbots can be used to conduct legal research or even draft answers to legal questions based on vast databases of law. Firms are also anticipating tools like Westlaw's upcoming "Ask Westlaw," which is reported to use generative AI trained on the entire Westlaw library to answer research queries in conversational form. While lawyers must always double-check the primary sources, these AI research assistants can save time by pointing attorneys in the right direction quickly.

Evidence and Transcript Analysis

Reviewing evidence and deposition transcripts is another reading-heavy task where AI lends a hand. New AI assistants can comb through deposition transcripts or discovery documents and summarize the key points or themes. For example, Thomson Reuters' CoCounsel (originally developed by Casetext) can summarize transcripts and even generate deposition preparation outlines based on those materials. In practice, an attorney could upload a lengthy deposition transcript and receive a concise summary of the witness's testimony organized by topic. AI can also highlight inconsistencies or key admissions across thousands of pages of discovery. On the horizon, AI may even handle more nuanced analysis - experimental tools are being developed to analyze video depositions for sentiment or body-language cues and flag moments where a witness showed discomfort (a concept that could one day support witness credibility assessments).

Document Review in Litigation

In big litigation cases, firms often face "data dumps" of emails, PDFs, and records. AI-powered e-discovery tools (a.k.a. predictive coding) have been used for years to find relevant documents, but newer generative AI goes further. It can "boil a trove of documents down to the most important information," effectively reading thousands of pages to surface the crucial facts. For example, an AI might sort through all emails in a case and flag which ones are likely to be hot documents. This not only speeds up document review but also suggests patterns or weaknesses in a case strategy that a human might miss until much later. In one example, CoCounsel's deposition prep feature can even suggest fruitful areas of inquiry and spot potential weak points in your case by digesting all the case documents. In sum, AI "reading" tools act as tireless junior associates that never get bleary-eyed, helping lawyers focus on strategy sooner.

Writing: AI as a Drafting and Editing Assistant

Beyond reading, AI is also a capable writing assistant. It can generate drafts, suggest language, and ensure consistency, all under a lawyer's supervision. Importantly, the goal is not to let the machine replace the lawyer's voice or judgment, but to save time on routine writing tasks and provide a strong starting point.

Below are some writing-related AI applications in legal practice:

Drafting Contracts and Documents

AI can produce first drafts of many standard legal documents based on prompts or templates. Lawyers can input a summary of the deal points or terms, and the AI will draft a contract, lease, will, or other document in seconds. For instance, generative AI systems like ChatGPT have been used to create initial drafts of contracts and leases. Similarly, Microsoft is integrating its GPT-4-powered Copilot into Word and Office, which allows attorneys to say, "Draft an NDA with these five provisions…" and get a rough draft to refine. Law firms using early versions of Copilot report substantial time saved - one firm, Husch Blackwell, found that using AI to draft routine correspondence and even internal documents (like attorney bios) saved thousands of lawyer hours in a year. The attorney still reviews and edits the output, but much of the boilerplate and structure comes together instantly.

Emails and Meeting Summaries

Writing polished emails or summarizing meetings into memos can eat up a lot of time. AI writing assistants are helping generate these routine communications. For example, if a lawyer needs to draft a follow-up email to a client after a meeting, an AI integrated in Outlook or a practice management system can draft a courteous, well-structured message based on the meeting notes. Microsoft 365 Copilot has been used in firms to summarize meetings and draft correspondence automatically, allowing lawyers to quickly customize and send out communications. The tone and detail can be tailored by prompt (e.g. "Write this email in a formal tone explaining next steps in the case."). For internal purposes, AI can also summarize a long email thread or meeting transcript into a brief memo for the file. This means lawyers spend less time distilling information and more time acting on it.

Legal Research Memos and Briefs

Some advanced legal AI tools can take a legal question and produce a structured memo or even an outline of a brief. For example, CoCounsel can draft a legal research memorandum addressing a specific question - the attorney provides the prompt (say, "Explain the standard for summary judgment in a slip-and-fall case under Texas law.") and the AI generates a memo with analysis and even cites some authority. This can serve as a rough draft for the lawyer, who can then refine the reasoning and ensure accuracy. AI can also help overcome "blank page" syndrome by suggesting a logical brief outline or key points to cover. In addition, specialized tools assist with editing and quality control. For instance, Clearbrief is an AI-driven brief drafting aid that automatically checks whether each sentence in a brief is supported by the evidence or case law. It can even generate additions like a Table of Authorities or a chronology of facts with minimal manual effort. In short, AI writing assistants help lawyers produce high-quality drafts faster - the attorney remains the final editor, but much of the drudgery in drafting and citation-checking is handled by the AI.

Learning: AI for Knowledge Management and Training

AI's role in "learning" within a law firm is two-fold: it helps lawyers learn and stay up-to-date more efficiently, and it helps firms capture and manage their collective knowledge.

These applications range from continuing education to on-the-job training and institutional knowledge management:

Continuing Education and Training Support

Lawyers face ongoing learning obligations (for example, CLE deadlines in Texas and other states). AI can ease these administrative burdens. Instead of a staff member manually updating CLE tracking spreadsheets, an AI-driven system can monitor and alert attorneys of upcoming CLE deadlines, suggest available courses to fulfill specific requirements, and keep digital attendance certificates on file. AI can also personalize learning plans for attorneys. For example, if a lawyer frequently works on insurance cases, the system might recommend relevant CLE courses or recent articles on insurance law updates. This kind of AI assistance ensures attorneys stay compliant and informed with minimal manual effort.

Training Simulations and Coaching

Some firms are exploring AI-driven training simulations to help attorneys (especially juniors) build skills in a low-stakes environment. For instance, AI chatbots can simulate a client interview or even a courtroom argument, allowing junior attorneys to practice their questioning and argumentation techniques. While still experimental, these tools hint at how AI could act as a tutor - providing guided practice and even feedback. Even simpler implementations are proving useful: an AI-powered writing coach, for example, can point out overly complex sentences or legalese in a draft and suggest plainer language. By integrating such AI "coaches" into the tools lawyers already use, firms can help their teams sharpen skills over time through immediate, tailored feedback.

Knowledge Management and Precedent Retrieval

Law firms have long struggled to organize and tap into their troves of documents (briefs, contracts, memos, emails). AI is finally making it easier to leverage this gold mine of institutional knowledge. With AI-driven search and smart categorization, firms can instantly retrieve prior work product that is relevant to a current matter. For example, if the firm handled a similar motion last year, an associate can ask the AI: "Find a sample motion to compel from our files that succeeded, and summarize the main arguments." The AI will surface the document and provide a synopsis of how the issue was argued. By quickly connecting attorneys with precedents and insights across the organization, AI not only saves time but also promotes consistency and quality in legal work. In essence, AI-driven knowledge management ensures that learning is a continuous, on-demand part of practice - attorneys can learn from their firm's collective experience on the fly, whenever they need it.

Operations: AI in Firm Management and Client Service

The final category, operations, covers the behind-the-scenes workflows that keep a firm running - from client intake and scheduling to billing and data analytics. These areas are rife with repetitive tasks, making them perfect targets for AI and automation. At a firm-wide level, adopting AI in operations can reduce administrative burdens and free up more time for billable work.

Below are concrete examples of how AI is streamlining law firm operations today:

Client Intake and Chatbots

First impressions matter, and AI is helping firms handle client intake more efficiently. Instead of lengthy paper intake forms, some firms use conversational chatbots on their websites to engage new clients and gather information. For example, the Gideon chatbot platform can interact with prospective clients, ask the right questions to gather facts, and even help qualify leads. A potential client might visit the firm's site and be greeted by an AI assistant asking, "How can we help you today?" Through a guided Q&A, the bot collects essential information (e.g. What type of legal issue? Any court dates set?) and then routes the prospect to the right attorney or sets up a consultation. Gideon's AI can also answer common questions and replace long intake forms with a simple conversation, making the client experience smoother. Internally, this means less data entry for staff and a faster response to inquiries. AI intake systems can even prioritize inquiries - for instance, flagging urgent matters or high-value leads so the firm can follow up promptly. In sum, AI-driven intake is improving responsiveness and ensuring no potential client falls through the cracks.

Scheduling and Calendar Management

Attorneys' calendars are notoriously full, and keeping track of appointments, deadlines, and court dates is a constant challenge. AI assists by automating parts of this process. Some practice management systems now include AI that can automatically create calendar events, set task reminders, and even calculate court deadlines based on procedural rules. For example, if a new trial date is entered, the AI can instantly populate all related pre-trial deadlines on the firm calendar (discovery cutoff, motions due, etc.) according to the court's rules - all without human error. Microsoft's Copilot can also help by integrating with Outlook and Teams: it might summarize a team meeting and suggest action items with due dates, or find open slots for a group meeting by analyzing everyone's availability. In one AI integration, lawyers can simply tell the system, "Schedule a deposition with John Doe and Jane Smith in the next two weeks," and the AI will find a suitable time, book it, and send out the invitations. Similarly, smart task management assistants can prioritize to-do lists. Clio's practice management platform, for example, now has an AI helper that can remind you of upcoming deadlines and even draft a to-do item or time entry at your command. All of this reduces back-and-forth emails and manual calculations, keeping operations running on time.

Document Management and Docketing

Keeping legal documents and correspondence organized is another operational challenge being improved by AI. Machine-learning algorithms can file and tag documents in the right places automatically. One notable example is an integration between a docketing system (American LegalNet's eDockets) and an AI email management tool (Zero) that automatically sorted and filed litigation-related emails and attachments into the correct client/matter folders in the firm's document management system. By reducing the risk of something being misfiled or lost, the AI was consistently applying the firm's filing rules. Beyond email, AI can assist with automated document assembly and e-filing - for instance, generating standardized filing forms or bundles of documents for court submissions based on the case data input, significantly cutting down on staff labor.

Time Tracking and Billing Automation

Recording billable hours is a necessary chore in law firms, but AI is making this task less of a headache by automatically tracking work and preparing timesheets. AI-powered time-tracking tools monitor attorneys' activities (emails sent, documents edited, calls made, calendar entries, etc.) and capture potential billable events in the background. Instead of relying on an attorney to recall hours at the end of the month, the AI might log that "2:13-2:45pm was spent editing Contract_ABC.docx" and suggest it as a time entry. Tools like PointOne and Billables.ai integrate with practice management software to sync these entries automatically. The result is more accurate and complete time records - studies show this can significantly reduce missed billable hours and minimize human error in billing. For the firm, that means increased revenue capture without extra work. For attorneys, it means no more scrambling at month's end to reconstruct their time. AI can also help review bills: for example, some corporate legal departments use AI to audit outside counsel invoices for compliance with billing guidelines, flagging any improper charges automatically. In short, billing becomes faster and more precise, with lawyers spending minutes instead of hours on the process.

Analytics and Decision Support

Beyond day-to-day tasks, firms are starting to leverage AI for higher-level insights. AI analytics can sift through large datasets - from internal firm metrics to external information - to support management decisions. For instance, some AI platforms analyze firm data (case outcomes, billing patterns, client feedback) to help law firm leaders spot trends and make better decisions. AI can also scan publicly available data to detect emerging legal risks or potential new case opportunities. The Darrow platform, for example, uses AI to monitor news, regulatory filings, and social media for facts that might give rise to legal actions (such as patterns of consumer complaints suggesting a class action). Darrow's own legal team has been partnering with firms on the opportunities it discovers, and it plans an enterprise offering so firms can receive alerts about "potential legal violations that would otherwise go unnoticed." This approach essentially serves as an AI lookout for business development and risk management, spotting issues early. Even internally, a firm might use AI predictive models to forecast the likelihood of winning a case based on past data, or to allocate resources by predicting how complex a new matter might be. While these strategic uses of AI are just beginning to emerge, they suggest that many operational decisions (from case strategy to staffing levels) could soon be informed by AI-driven analysis of big data.

Conclusion

AI is no longer a theoretical concept for law firms - it's here now, improving everything from how we review a contract to how we onboard a new client. The overarching theme is that AI handles the heavy lifting on routine tasks - reading mountains of text, drafting boilerplate, organizing information, and keeping track of details - thereby allowing legal professionals to focus on higher-level work, exercise judgment, and provide the personal counsel that clients expect. In a rapidly evolving legal tech landscape, experimenting with these AI tools is becoming not just an advantage, but a necessity to remain efficient and competitive.