AI Tools for Practicing Lawyers

Ron Drescher
  • 5.0 (3)
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AI Tools for Practicing Lawyers delivers practical, no-nonsense guidance on how attorneys can use artificial intelligence tools in their law practices — right now. This podcast is for practicing lawyers who want real-world answers, not hype. Each episode focuses on clear, understandable explanations of AI tools that can help attorneys work more efficiently, communicate more effectively, and make better business decisions — without requiring technical expertise or coding knowledge. We cover topics such as: • Using AI responsibly and ethically in legal practice • Drafting, research, summarization, and document review tools • Client communication and intake automation • Practice management efficiencies • Emerging AI platforms relevant to law firms • Real examples attorneys can apply immediately Whether you are a solo practitioner, small-firm attorney, or part of a larger practice, this podcast is designed to help you understand what AI can — and cannot — do for lawyers today. No futurism. No speculation. Just practical tools for practicing lawyers. Hosted by Ron Drescher

  1. 18h ago

    Episode 028 AI Skills and the $1500 Divorce

    SHOW NOTES A legal industry business professional spends her career building expertise, then watches AI automate the most valuable parts of that work. That's not a hypothetical — it's what this week's guest proved could happen. Yvonne Nath, founder of Counsel Commons, joins Ron and Heather to talk about a marketplace where lawyers and legal professionals can buy and sell the AI prompt libraries, skills, and agents they've built. But the conversation goes somewhere bigger: what happens when AI doesn't just make legal work faster, but cheap enough to create an entirely new market for the millions of people who can't afford a lawyer today?    In this episode:    - Yvonne Nath's path from law firm operations and strategy work to founding Counsel Commons, a marketplace where lawyers and legal professionals sell the AI automation tools they've created - How Yvonne productized most of her own   law firm management expertise in about a month, and what that says about the future of legal ops roles - Some distinction between skills and agents, and why Yvonne deliberately builds almost none of the latter - How Counsel Commons splits into two channels — bar-verified attorneys and paralegals versus business professionals — to manage unauthorized-practice-of-law risk - Counsel Commons’ vetting process: AI review for prompt injections and input/output accuracy rather than manually testing every submission - The pricing model for sellers — a flat listing fee for legal professionals who keep nearly all the revenue, versus a revenue split for business-side tools - Yvonne's decision to run her own automations on local, open-weight models rather than through Anthropic or another cloud provider, and the data-privacy reasoning behind it - The $1,500 divorce and the access-to-justice math — why the millions of people priced out of traditional legal help represent an untapped market, not a threat to existing practice - Ron and Yvonne's disagreement over whether productizing legal services cannibalizes a firm's existing client base or reaches an entirely different one - What Yvonne means by an "AI native" law firm, and why she thinks real transformation requires starting from scratch We also discuss: - Counsel Commons' review system, built around objective performance questions rather than star ratings - Heather's team meeting encouraging her paralegals and virtual assistants to build AI literacy now, not later - Ron's case that solo lawyers would get further mastering Word, Excel, and their phone systems than chasing AI tools haphazardly - This week's Practice Signal: a solo domestic-law attorney wondering whether they now need to hire a consultant just to use tools that were supposed to make everything easier - The NACBA webinar where consumer bankruptcy attorneys discussed building their own Claude skills Key Takeaway Yvonne's real point isn't that AI makes legal work faster — it's that legal work becomes a different business (and, hopefully, a more rewarding one) once it's cheap enough for ordinary people to actually buy. A $1,500 divorce or a $50 landlord dispute isn't a discount version of a $20,000 case. It's a client who was never walking through the door in the first place. That lands hardest for lawyers still treating AI as a way to shave hours off existing work rather than a way to reach people who currently have no lawyer at all. It's also this season's Users and Builders split in miniature: Yvonne builds, verifies, and sells; most solo and small firm lawyers just want something proven that works off the shelf — and she thinks that gap is exactly the market Counsel Commons should serve. Mentioned in This Episode - Yvonne Nath, founder of Legal InnovAI - Claude - Qwen the local, open-weight model Yvonne runs on her own hardware - Lawvable / Lawv.ai — an open-source skills library Yvonne referenced as possibly connected to Claude's built-in legal skills - Stripe - NACBA (National Association of Consumer Bankruptcy Attorneys) - divorce.com - Upsolve info@drescherlaw.com Highlight Reels on YouTube and Instagram info@drescherlaw.com Highlight Reels on YouTube and Instagram

  2. Sep 3

    Episode 027 Is it Safe to Go to the Rave?

    SHOW NOTES A lawyer running a massive document production doesn't read every page anymore — she can't, and nobody expects her to. So when does that stop being lawyering and start being blind faith? Jim Casey, Chief Privacy Officer at Akamai Technologies, joins Ron and Heather to argue there's a real answer, one that depends entirely on whether a bad AI output can still be caught before someone acts on it. When is an AI mistake actually reversible, and when have you already handed your judgment to the machine? In this episode: Jim Casey's framework for deciding when a lawyer can trust an AI-generated answer, drawn from his two published pieces On Knowledge in An Agentic World and Agentic Workflows in ComplianceThe "Wilma Simpson" hypothetical — a bankruptcy lawyer using AI to summarize a 180-page court order, and where the real risk actually sitsRon and Jim's sharp disagreement over whether a "reversible" AI mistake stays reversible once a client acts on the lawyer's advice built from itWhy reviewing a massive document production (sorting, summarizing, deduplicating) shifts trust from individual verification to system-level designAn e-commerce company's real-world case study training a model on product-safety complaint data — six months of retraining to reach 90–95% accuracyThe malpractice waiver hypothetical: what an in-house lawyer does when outside counsel wants to disclaim liability for AI-assisted workClient consent and data privacy limits on using AI to process a client's own personal informationThe difference between "human in the loop," "human on the loop," and "human out of the loop" — a framework borrowed from autonomous weapons policy"Compliance theater" — why an audit log nobody actually reviews isn't a safeguardThe "non-delegable duty to think" — what AI can never take off a lawyer's plateWe also discuss: The Flintstones/Simpsons/Jetsons framework applied to matching lawyers with the right clientsA Reddit-sourced Practice Signals case: a kind, hardworking associate who struggles with vocabulary and comprehension — does AI help, or just hide the problem?Chris Ryan's AI training tool for new associates, Benchsim.ai, now SOC 2 compliant and video-capableThe Season 3 shift from Flintstones/Simpsons/Jetsons to a new "Users and Builders" frameworkFirst tips for builders and users: "make failure visible" and "ask and ask again"Key Takeaway Jim Casey's real point isn't that lawyers can or can't trust AI — it's that trust isn't one setting you flip on. The question changes at every step in the chain: is this output feeding the lawyer's own thinking, or is it about to become the advice a client acts on? Reversibility disappears the moment a lawyer hands that answer downstream. Everything else — audit logs, compliance sign-off, vendor demos — is theater unless someone is actually positioned to catch the mistake before it moves. This matters most for Simpsons lawyers who've adopted a tool but haven't thought through where accountability actually sits in their own workflow. Flintstones lawyers aren't there yet, and Jetsons lawyers like Jim are already building the guardrails. But the solo or small firm lawyer running document review or drafting client advice off an AI summary needs a concrete answer to Jim's closing question: what do you do with the confidently wrong answer? Mentioned in This Episode Jim Casey, VP & Chief Privacy Officer, Akamai TechnologiesJim Casey's articles: Knowledge in an Agentic World and Agentic Workflows: A New Frontier for ComplianceChatGPTClaudeHarvey (AI legal tool)Legora (referenced as an enterprise legal AI tool)Chris Ryan and his AI training tool for new lawyers (previously featured on the show)Reddit (source of this episode's Practice Signals story)Highlight Reels on YouTube and Instagram info@drescherlaw.com

  3. Aug 27

    Episode 026 Volunteers and Hostages

    SHOW NOTES Chad Van Horn built an AI petition review process that cut his firm's amendments by 80 percent. He also thinks AI is making lawyers dumber. Both are true in the same law firm, and figuring out how to hold both truths at once is the whole game. If AI can teach a new associate everything a senior partner knows, what happens to the judgment that only comes from doing the reps yourself? In this episode: - How Chad's Claude-built bank statement analyzer replaced his highlighter-and-manual-review process - The "volunteers, not hostages" approach to rolling AI out across a hundred-person firm - Why Chad's AI-powered petition review step reduced amendments by 80 percent - How Chad uses himself as a human "AI agent" to bring reluctant staff along without forcing adoption - What Chad's firm built with GLADE, its all-in-one bankruptcy practice management platform, and how Claude and GLADE connect directly - Why Chad believes AI is making lawyers less capable critical thinkers, even as it makes them more efficient - How Chad trains AI skills to check judges' local rules, procedures, and order formatting, and updates them when orders get kicked back - Why bankruptcy filings are rising sharply and what that means for firm capacity - How Chad and Heather debate what percentage of bankruptcy work is really legal judgment versus process - Chad's prediction that practitioners who don't adopt AI may eventually be unable to compete We also discuss: - The gap between Gemini's early data advantage and its current market position - Whether token pricing stays this cheap once AI vendors need to turn a profit - Vendor reliability risk as new AI legal tools flood the market - The FSJ framework applied to Season 2's document-by-document AI onboarding approach - This week's Practice Signal: a solo practitioner asking an inexperienced legal assistant to build the firm's entire tech stack - Florida's vehicle exemption and how a small asset detail can cost a client their car Key Takeaway Chad's petition review process shows what disciplined workflow gets you: fewer amendments, tighter output, and a documented feedback loop that trains the humans as much as it trains the AI. But Chad's own warning is louder than his results. The same tools that make firms faster are, in his view, quietly eroding the critical thinking lawyers need to catch the case that doesn't fit the pattern. For Flintstones lawyers, GLADE and Chad's petition reviewer are proof that meaningful adoption doesn't require a hundred-person firm to pull off. For Simpsons lawyers building their own skills, Chad's volunteers-not-hostages approach and his habit of weekly skill refinement are a directly usable model. For Jetsons lawyers already running full systems, Chad's warning about critical thinking is the harder, more uncomfortable takeaway. Mentioned in This Episode - Claude - ChatGPT - Gemini and Google Workspace - GLADE, practice management and bankruptcy software (name as heard on the recording, confirm spelling) - Clio - Litify (referenced on the recording as "Lytify," confirm intended name) - Salesforce - HubSpot - Easy Filing - QuickBooks - Best Case - CoCounsel (Westlaw) - David AI - NACBA's "Me, Myself, and AI" webinar - Jenny Doling - Van Horn Law Group - Chad Van Horn, author of The Debt Life and Everything You Need To Know About Bankruptcy in Florida Highlight Reels on YouTube and Instagram info@drescherlaw.com

  4. Aug 20

    Episode 025 Do Lawyers Need New Rules for AI? Judge Elizabeth Gunn Says ... Maybe Not

    SHOW NOTES A federal bankruptcy judge just told us she doesn't personally use generative AI in her own chambers - and that's exactly why this conversation is worth your time. Judge Elizabeth L. Gunn, who sits on the U.S. Bankruptcy Court for the District of Columbia, spent nearly an hour with Ron and Heather working through what AI actually changes about the practice of law, and what it doesn't. Central question: If AI can now do in seconds what used to take a junior associate hundreds of hours, who trains the next generation of lawyers - and who's accountable when the machine gets it wrong? In this episode: - Why Judge Gunn deliberately limits AI use in her own chambers, to protect it as a training ground for law clerks and interns - How judicial opinions on AI vary widely across the bench - "as many opinions as there are black robes" - Whether it matters, evidentiarily, if a summary took an associate 700 hours or an AI tool 700 seconds to produce - How firms could develop internal AI tools for chapter 11 practice, and what foundation they'd need to lay for the court - Why Judge Gunn believes generative AI is hollowing out the mid-level associate pipeline - How fee applications are starting to raise questions about AI subscriptions and engagement agreements - Judge Gunn's real experience with hallucinated cases in her courtroom, including sanctions and a running database of violations - Why she believes a sliding scale of culpability makes sense for different kinds of hallucinations in legal briefs We also discuss: - Whether courts should look behind a flat-fee agreement to question consumer bankruptcy AI tool charges - How Lexis and Westlaw brief-checker tools are used in chambers to catch bad citations - Why Judge Gunn is skeptical the UAE's centralized, nationwide AI judiciary approach would work in the U.S. federal system - Differing philosophies among law schools on AI in the classroom, from banning electronics to teaching practical AI skills - Whether it's the court's role to inquire into AI-related terms of an engagement agreement when reviewing a fee application Key Takeaway: Judge Gunn's core message is that AI doesn't require a parallel set of rules. Rule 9011, the rules of evidence, and existing duties of competence and candor already cover it. What changes isn't the standard - it's how carefully lawyers have to apply it, and how quickly courts have to learn to tell the difference between an inaccurate pin cite and an argument built on a case that doesn't exist. For Flintstones lawyers, that's reassurance: you don't need to master new technology to stay compliant, because your existing ethical obligations already cover AI-created work product. For Simpsons lawyers dabbling with tools like Claude or ChatGPT, Judge Gunn's evidentiary framework is a warning - if you can't establish the foundation for how a summary was created, speed doesn't save you. For Jetsons lawyers building AI into chapter 11 workflows, her comments point to exactly what a judge will want documented before she trusts the output. Mentioned in This Episode: - Judge Elizabeth L. Gunn, U.S. Bankruptcy Judge, District of Columbia - Bad Boys of Bankruptcy podcast (Judge Gunn's podcast) - Claude - ChatGPT - Harvey - Lexis - Westlaw - Best Case (bankruptcy case management platform) - Rule 9011 - Northern Pipeline Construction Co. v. Marathon Pipe Line Co. - Stern v. Marshall - Nancy Rappaport, UNLV School of Law - University of Chicago School of Law - New York and Florida AI ethics rules - DC Bar ethics rule on AI HIGHLIGHT REELS YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

  5. Aug 15 ·  Bonus

    Field Note: Five Tips for Fighting Pro Se Litigants Who Use AI

    SHOW NOTES An insurance company just spent $300,000 fighting a pro se litigant who had no lawyer — just ChatGPT. That number should worry every practicing litigator. What happens when the other side has access to an inexhaustible junior associate that never bills, never tires, and never stops drafting the next motion? In this episode: - Nippon Life Insurance Company of America's lawsuit against OpenAI over a pro se litigant's AI-generated filings - How Graciella Della Torre used ChatGPT to try to reopen a case that had already settled and been dismissed with prejudice - Why the $300,000 in legal fees is the real story here, not who ultimately wins the Nippon v. OpenAI lawsuit - The new asymmetry AI creates between represented parties and self-represented litigants - Narrowing the battlefield with procedure instead of trying to out-produce an AI on paper - Verifying every citation, quotation, and legal proposition in an AI-assisted filing, not just whether the case exists - The Matthew Elliott case and hidden white-text prompt injection aimed at an AI system that wasn't even part of the court's process - Judge Walter Spader's sanctions against Elliott, and Spader's own disclosure that he used AI to help draft the sanctions order - Whether a pro se litigant's ChatGPT prompts and conversations are discoverable - Warner v. Gilbarco and Tremblay v. OpenAI, and what they say about work product protection for AI use We also discuss: - Matt Lafferman and Rick Shearer's writing on the developing case law around AI and discovery - Why courts are treating AI tools as tools, not persons, for work product purposes - Judge Sarah Smith's public statement on the limits of her own AI use on the bench - The distinction between using AI and committing misconduct with AI - UAE courts' nationwide AI procedures, compared with Connecticut's approach Key Takeaway: Lawyers have spent years asking whether AI will replace lawyers. That's not the immediate problem. AI doesn't need to replace opposing counsel — it just needs to give the person without a lawyer the ability to litigate like they have one. The right response isn't more paper. It's narrower issues, verified authorities, documented patterns, and a clear line between AI use and AI misconduct. This is a Flintstones-lawyer blind spot waiting to happen — the attorney who assumes a pro se opponent's filing quality reflects their legal knowledge, and gets buried in volume. It's also a Simpsons-lawyer governance problem: verification has to become a standing practice, not a one-off gut check. And it's a Jetsons opportunity — a citation-verification workflow built now is the difference between a manageable pro se case and a $300,000 one. Mentioned in This Episode: - 21 Ways AI Can Hallucinate in Your Legal Brief - Nippon Life Insurance Company of America v. OpenAI, (1:26-cv-02448) District Court, N.D. Illinois - Graciella Della Torre - ChatGPT - Claude - Gemini - Matthew Elliott v. New York Bariatric Group - Judge Walter Spader, Connecticut State Court - Ars Technica, Suspecting court of using AI, man injected prompts in filings to try to win case - Reuters - Matt Lafferman, Dentons, Legal AI Lab - Rick Shearer - AI and Privilege: When AI Becomes Evidence - Warner v. Gilbarco, Inc . (2:24-cv-12333) District Court, E.D. Michigan - Tremblay v. OpenAI, Inc. (4:23-cv-03223) District Court, N.D. California - Judge Sarah Smith, Third Judicial Circuit, Illinois, Standing Order Highlight Reels on YouTube and Instagram YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

  6. Aug 13

    Episode 024 AI, Wellness and the 4th Bucket

    Show Notes Lawyers wear exhaustion like a badge of honor — but what if the data on your wrist already knows you're running on empty? In this episode, Ron sits down with  consultant Megan Grover of MBG Wellness and physician Dr. Kathryn Boling to talk about what happens when you point AI at your own biometric data. The question isn't whether lawyers work too hard — everyone already knows that. The question is: **once you can actually measure the damage, what do you do about it?** In this episode: - Why lawyers (and other high-stress professionals) struggle to recognize their own burnout signals - The difference between wellness data (Oura, Whoop, Apple Watch) and medical data (continuous glucose monitors) - How resting heart rate and HRV (heart rate variability) reveal stress your calendar can't - Why sleep quality matters more than sleep quantity — and how to actually get quality sleep on six hours - Gendered stress load: why women in male-dominated fields like law often carry a heavier physiological burden - Practical entry points for busy lawyers who "don't have time" to exercise - Meal strategies and delivery services for professionals who won't cook - Where AI is genuinely useful for interpreting wearable and lab data — and where it falls short - The Practice Signal segment: a Reddit post about a lawyer who turned down a job requiring 2,700 billable hours a year We also discuss: - The "red light, yellow light, green light" system Ron and Kathryn used to survive her medical residency - VO2 max workouts vs. recovery workouts, and why lawyers tend to over-index on the former - Using ChatGPT to vet supplement interactions (and why it's not a substitute for an actual expert) - Why "looking up your symptoms" online is a bad idea, but structured biometric data is a different story - Building sustainable routines instead of chasing 10,000 steps Key Takeaway: The tools have changed, but the problem hasn't: lawyers don't lack information about their own burnout, they lack a boundary. What wearables and AI add isn't willpower — it's proof. When your HRV and resting heart rate confirm what you've been ignoring, "I'm fine" gets a lot harder to say with a straight face. This one's for the Simpsons and Flintstones lawyers who've never looked at their Apple Watch as anything but a phone-caller-ID. The Jetsons lawyer in this conversation is the one who takes the data seriously enough to feed it to AI and actually change something — not just track it and keep grinding. Mentioned in This Episode: - Oura Ring - Whoop - Apple Watch - Garmin - Stelo by Dexcom (continuous glucose monitor) - ChatGPT - Reddit (source of the Practice Signal case) - Megan Grover, wellness consultant - Dr. Kathryn Boling, physician Highlight Reels on YouTube and Instagram YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

  7. Aug 6

    Episode 023 Deepfakes and the Liar's Dividend

    Has AI fundamentally changed the law of evidence, or has it just forced us to revisit principles that have always been there? That's the question at the center of this episode, and there's no better person to answer it than retired federal judge Paul Grimm — one of the people who has literally tried to write the new rule. IN THIS EPISODE: - Judge Paul Grimm's background: 25+ years on the federal bench in Maryland, now emeritus director of the Bolch Judicial Institute at Duke Law - The multi-year federal rulemaking process (the Rules Enabling Act) and why it can't keep pace with generative AI - The difference between "acknowledged" and "unacknowledged" AI-generated evidence, and why the distinction matters - Proposed Rule 901(c) for authenticating AI-generated evidence — and why the Evidence Rules Advisory Committee has still declined to publish it after three years - How the existing 51% authentication standard (Rule 901(b)(1)) creates a low bar for admitting evidence, real or fake - The "liar's dividend": how the mere existence of deepfakes lets litigants dismiss real evidence as fabricated - State of Washington v. Puloka (No. 21-1-04851-2 KNT, 2024): a court excludes AI-enhanced video evidence despite a legitimate clarification goal - State of Florida v. Albisu (Broward County, 2025): a defense lawyer uses AI-generated VR goggles to let a judge see a self-defense claim from the client's point of view - Rule 107, the new federal rule distinguishing "illustrative" AI-generated exhibits from actual evidence - Why ABA Formal Ethics Opinion 512 already tells lawyers exactly how to avoid AI hallucination sanctions — and why lawyers keep getting sanctioned anyway WE ALSO DISCUSS: - The "witness washing" problem in facial-recognition photo lineups - How deepfake detection expert Hany Farid's confidence has shifted as the technology improved - Maura Grossman's deepfake-detection research at the University of Waterloo - Judge Grimm's "wow moment" using AI to pressure-test a speech outline - The origin of the term "deepfake" and how fast it entered the mainstream - Why illustrative AI evidence — not fake evidence — may be the bigger near-term risk KEY TAKEAWAY: AI hasn't broken the rules of evidence. It's exposed how thin they always were. A 51% authentication standard and a jury instruction to "disregard" what they just saw and heard were never built for a world where anyone can manufacture convincing fake audio and video for free — and Judge Grimm's own proposed fix has been sitting in committee for three years. For solo and small firm lawyers, the fix isn't waiting on Washington. Notice, disclosure, and pretrial motion practice around AI-generated evidence are tools you can use right now, rule change or not. Lawyers still dabbling with AI without a real verification habit are exposed either way — and the ones building real workflow discipline around it are the ones who'll actually benefit from what these tools can do. MENTIONED IN THIS EPISODE: - Judge Paul Grimm (Ret.), Bolch Judicial Institute, Duke Law - Professor Maura Grossman, University of Waterloo - Hany Farid, Dartmouth (formerly UC Berkeley) - Federal Rules of Evidence 901(b)(1), 901(b)(5), 901(b)(9) - Proposed Rule 901(c) - Federal Rule of Evidence 403 - Federal Rule of Evidence 107 (illustrative evidence) - State of Washington v. Puloka (No. 21-1-04851-2 KNT, 2024) - State of Florida v. Albisu (Broward County, 2025) - ABA Formal Ethics Opinion 512 (August 2024) - "Learned Hand" (AI tool for courts) - ChatGPT, Claude, Gemini - The Rules Enabling Act YouTube: https://youtube.com/@AIToolsforLawyers Instagram: https://instagram.com/aitoolsforlawyers info@drescherlaw.com

  8. Jul 30

    Episode 022: The Jetsons Vacation In Florida

    Show Notes How much work has to happen before AI can actually help a law firm? Everyone wants an AI-native practice, but almost nobody asks what has to be true before the AI can do anything useful. This episode answers that question with someone who's already built the answer from the ground up: Leisa Wintz, a Florida family law attorney and founder of Legal Authority Lab, whose firm runs on AI from intake to final judgment — not because she bought a product, but because she spent years teaching her systems everything she knows. In this episode: Why the hardest part of AI adoption isn't the AI — it's teaching the firm what it already knowsHow Leisa built Legal Authority Lab out of her own Florida family law practice, and where it's expanding next (real estate and title)The difference between AI "users" and AI "builders" — and why builders are the rare exceptionWhy Leisa ran Anthropic's Claude legal skills against her own systems and concluded they were too generic to adoptHow her firm delivers AI as Claude skills tied to a GitHub repository, rather than as a standalone appUsing Claude as a de facto case management system — and the one thing it still can't do well (a visual dashboard)How Leisa evaluates whether a "Flintstone" firm is even ready for AI, and what she builds first when it isn'tFlorida's new statewide AI-use disclosure rule, and how it compares to New York'sThe UAE's AI system for judges, and whether courts are the next frontier for legal AIWeek four of the show's AI Build series: turning firm knowledge into Markdown so AI can actually use itWe also discuss: Leisa's husband, a computer engineer, joining Legal Authority Lab as a technical partnerDiscovering and evaluating unfamiliar tools like GoHighLevel on the flyA Reddit "AITA" post about a lawyer paid $100/hour to evaluate AI's legal answersFlorida's PDF/A and OCR e-filing requirements, and what they mean for AI-ready documentsWhether courts might someday accept Markdown-formatted pleadingsLeisa's do-it-yourself legal coaching app for pro se litigants in Florida family courtFree Download: AI Build Series — Markdown Examples ( Sample markdown-formatted documents from the show's AI Build series, showing how to turn firm knowledge into a format AI can actually use.Examples of a client intake process rewritten in markdownTemplates for Flintstones, Simpsons, and Jetsons-level starting pointsGuidance on pairing markdown with a clean PDF reference for better AI performanceKey Takeaway An AI-native firm isn't built by adding AI — it's built by capturing everything the firm already knows in a form AI can actually use. The bottleneck was never the model. It's the spreadsheet: the categories, the naming conventions, the workflow logic that has to exist before any tool can be useful. Flintstones lawyers shouldn't skip straight to AI — Leisa's first move with an unready firm is basic infrastructure like lead forms and intake, not a chatbot. Simpsons lawyers are the ones already dabbling who need to formalize what they know into structured documents. And Jetsons lawyers — the rare builders like Leisa — are the ones who can package their own systems into reusable skills, and in her case, into a business. Mentioned in This Episode: Legal Authority LabClaude (Anthropic) and Claude's legal skillsGitHubClio Grow, Smokeball, Cleo, Rocket MatterGoHighLevelJotFormSlackFlorida Supreme Court statewide AI-use ruleLeisa's LinkedIn Discussion of the Florida RuleNew York statewide AI-use ruleCalifornia pending AI regulationUAE AI judicial assistance platformHighlight Reels on YouTube and Instagram 📺 YouTube: https://www.youtube.com/@AIToolsforLawyers 📸 Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

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About

AI Tools for Practicing Lawyers delivers practical, no-nonsense guidance on how attorneys can use artificial intelligence tools in their law practices — right now. This podcast is for practicing lawyers who want real-world answers, not hype. Each episode focuses on clear, understandable explanations of AI tools that can help attorneys work more efficiently, communicate more effectively, and make better business decisions — without requiring technical expertise or coding knowledge. We cover topics such as: • Using AI responsibly and ethically in legal practice • Drafting, research, summarization, and document review tools • Client communication and intake automation • Practice management efficiencies • Emerging AI platforms relevant to law firms • Real examples attorneys can apply immediately Whether you are a solo practitioner, small-firm attorney, or part of a larger practice, this podcast is designed to help you understand what AI can — and cannot — do for lawyers today. No futurism. No speculation. Just practical tools for practicing lawyers. Hosted by Ron Drescher

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