AI Tools for Practicing Lawyers

Ron Drescher

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. 2d ago

    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

  2. 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

  3. 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

  4. Jul 23 ·  Bonus

    AI Builds: Gmail Digest

    Most lawyers think about AI as a chat box. Ron argues that's already the old way of thinking — and the real leverage is in systems that run without you, remember what they're supposed to do, and hand you a finished result before you've had your coffee. In this episode: - Why the podcast is shifting focus from prompting skills to persistent, automated AI workflows - The "airport test" — Ron's framework for deciding whether an AI workflow is actually worth adopting - Building a fully automated AI news producer using make.com - The four-step architecture: collector, bundler (text aggregator), producer (persona prompt), and formatter - Using a Gmail label and filter to isolate newsletter content from client and personal email - Why persona prompting ("you are the executive producer") beats a plain summarization request - Why GPT-4.0, an older model, was still the right tool for this specific job - Hitting and solving the "wall of text" formatting problem with a markdown-to-HTML converter - Using Gemini as a build partner because of its familiarity with Gmail - Real examples of what the finished daily digest surfaced, including breaking legal-tech news We also discuss: - Why LinkedIn, properly configured, is a strong AI news source - Newsletter overload and why Ron started ignoring sources he used to value - The PlayStation 3 vs. PlayStation 2 analogy for why newer AI models aren't always necessary - Heather Gardner's role in introducing Ron to this kind of automated workflow thinking - Where this build actually lands on the Flintstones/Simpsons/Jetsons spectrum Key Takeaway: The most useful AI adoption in a small firm doesn't happen in the chat window — it happens when a workflow disappears into the background and just delivers. Ron's four-step build (collector, bundler, producer, formatter) is a repeatable pattern, not a one-off trick, and it didn't require the newest, most expensive model to work. This episode sits right in Simpsons territory: a lawyer who's dabbled with AI tools individually but hasn't yet connected them into something that runs itself. Ron's point isn't "go build this exact thing" — it's that the leap from Simpsons to Jetsons is smaller and less technical than most solo and small firm lawyers assume. Mentioned in This Episode: - make.com - Gmail (labels, search filters) - ChatGPT / GPT-4.0 (OpenAI) - Google Gemini - LinkedIn - Artificial Lawyer (newsletter) - The Rundown (newsletter) - Legal Tech Blogs (newsletter) - Heather Gardner - Apple / OpenAI trade secret lawsuit (Johnny Ive hardware device) - Meta AI-biased layoff algorithm lawsuit To see and download the final outputs from these tools click here. info@drescherlaw.com

  5. Jul 23 ·  Bonus

    Field Note: In Defense Of AI Notetakers

    While AI note-takers grab the headlines, the real issue at stake in this episode is one lawyers have been dodging for decades: "Is AI notetaking actually riskier than everything else already sitting in your practice?" In this episode: - Why the claim "AI note-takers turn everything into data" misses the point — meetings were already data, AI just organizes it - Ron's take on recording everything (calls, meetings, even hallway conversations) as part of the AI-forward way of managing firm information - The real question lawyers should be asking: not "do I trust AI," but "do I trust this vendor with this information under this security model" - The information governance questions that actually matter — who has access, where data lives, encryption, retention, whether the vendor can train on your data, whether recording can be disabled - How the AI adoption debate mirrors the earlier fight over moving client files to the cloud - Why treating "AI" as fundamentally different from every other cloud service is a mistake - The shift from "never put client files in the cloud" to firms running on Microsoft 365, NetDocuments, Google Workspace, and Clio We also discuss: - Confidentiality, privilege, trade secrets, and personnel discussions as legitimate concerns worth taking seriously - Everyday, non-AI examples of data risk — email, Dropbox, Google Drive, even the cleaning crew - Podcast guest Carolyn Elefant's related commentary on AI vs. other cloud tools - The Fast Company article that prompted this Field Note Key Takeaway: The mistake isn't using AI note-takers — it's treating "AI" as a special category that suspends the ordinary due diligence lawyers already owe every vendor. Strip away the letters "AI" and the questions are the same ones firms have been asking about cloud storage and email for twenty years. This lands hardest for Simpsons lawyers — the ones dabbling with AI tools but without a governance structure behind them. Flintstones lawyers will read this as one more reason to stay away; Jetsons lawyers already have these vendor questions answered. The point of this episode is to move more of the audience from reflexive fear or reflexive adoption into an actual governance conversation. Mentioned in This Episode: - Fast Company: [Why You Should Think Twice Before Letting an AI Notetaker in Your Meeting](https://www.fastcompany.com/91571498/ai-notetaker-work-meetings-privacy-data) - Carolyn Elefant - Microsoft 365 - NetDocuments - Google Workspace - Clio - Zoom - Dropbox - Google Drive info@drescherlaw.com

  6. Jul 23

    Episode 021 Law Firm AI: Custom Made Or Off The Shelf?

    SHOW NOTES Most AI pitches to law firms start with a product. This one starts with an audit — and a question about whether buying software was ever the right move for a solo or small firm in the first place. Jerry Dearden of David AI joins Ron and Heather to unpack what it actually takes to build AI around a firm instead of forcing a firm into someone else's software. It's the David AI origin story, the pricing, and the pitfalls — no punches pulled. IN THIS EPISODE: - How David AI's "fitting" audit works before any code gets written — firm size, niche, workflows, tech stack - The difference between reactive AI (you prompting a chatbot one task at a time) and proactive AI (an agent that acts on its own and reports back) - Why firms keep bumping into Harvey and Legora, then walking away because the pricing and scale don't fit solo and small practices - How David AI builds agents that live wherever a lawyer already works — iMessage, Slack, Teams, or inside existing case management software - The "company brain" concept: connecting a firm's tools so AI has full context instead of starting from zero every time - Data protection, DPAs, and how David AI's own contract terms changed after a listener-style question exposed a gap - Build-versus-buy: when David AI tells a firm to keep its existing software instead of building something new - Pricing structure — audit fees, build costs, and ongoing maintenance - A real AI paralegal build for a small Utah firm, including a lawyer-avatar project still in testing - The insurance defense billing bottleneck, and how AI could translate time entries into carrier-acceptable language automatically WE ALSO DISCUSS: - Kirkland & Ellis's $500 million, multi-year investment in a proprietary AI platform, and why Jerry calls building your own LLM "frying an egg on a volcano" - Claude's connector ecosystem and what it means for firms trying to build a unified knowledge base - Staff resistance to AI and the "Iron Man suit" framing David AI uses to get buy-in - This season's ongoing FSJ knowledge-standardization exercise — this week's step is making firm documents AI-readable - A tease for Season 3: "users and builders" KEY TAKEAWAY Jerry's answer to almost every question comes back to the same place: don't buy a tool and force your firm into it, fit the tool to how you actually work. That's a different pitch than most AI vendors make, and it only works if a firm is willing to open up its internal processes to an outside audit first. This episode's FSJ moment comes from the ongoing build series Ron's been running with listeners since the start of the season. Four weeks in, the assets are identified, organized, and cleaned. This week's step is standardizing them: a Flintstones lawyer writes a plain description at the top of one form, a Simpsons lawyer turns client questions into a consistent Q&A format, a Jetsons lawyer breaks a workflow into sequential steps. It's a reminder that "AI-ready" starts with structure a human puts in place — long before any model touches the data. MENTIONED IN THIS EPISODE: - David AI - Harvey - Legora - Claude / Anthropic - ChatGPT / OpenAI - Reddit - Clio - Microsoft Teams, Slack, iMessage - Spellbook - Kirkland & Ellis - TimeSolv (billing software Ron used in practice) - Claude's connector ecosystem info@drescherlaw.com

  7. Jul 16

    Episode 020: Pain Points, Grok And The Homeless Summer Associate

    SHOW NOTES What if the smallest law firms are actually the best positioned to win the AI era? That's the provocation Ron opens with this week — and it comes from someone with a genuinely rare vantage point: a solo attorney who also works as a professional AI trainer for the labs building these models. In this episode: Dr. Sara Kubik's path from a gerontology-focused PhD to family law, estate planning, and now legal AI trainingInside the AI training industry — how OpenAI, Anthropic, Google, and Microsoft contract out trainer roles through staffing layers, and why it's become a multi-billion dollar businessWhy older clients are often faster and more enthusiastic AI adopters than lawyers expect, once they see it's usefulHow different AI models produced very different results on the same creative request, and what that reveals about picking the right tool for the jobVibe coding a Medicaid penalty calculator, a community spouse calculator, and a child support chatbot without a formal development backgroundThe case for solo and small firm lawyers having a structural advantage over Big Law in adopting AIUniversity of Chicago Law School's ban on electronics in 1L lectures — and why Sara thinks the policy won't survive the yearWhat lawyers consistently get wrong about how AI models work, including training cutoffs and hallucinationsWe also discuss: ChatGPT's new wave of specialized models and the ongoing comparisons to ClaudeRon's controversial take on lawyers recording everything as firm data infrastructurePractice Signals: a Reddit post from a homeless summer associate, and where AI actually helps — and where it doesn'tThis week's Season 2 AI Build segment: cleaning firm knowledge assets before teaching them to AI, including PII anonymization and "grader guidance"The difference between vibe coding and understanding what you're buildingData-focused stats on solo and small firm representation in the legal marketKey Takeaway AI adoption was never really about the tool. It's about the pain point. Sara's advice to solos is blunt: stop shopping for tools and start naming what's actually broken in your practice — then go find out what fixes it. That reframe is what makes AI usable instead of overwhelming, and it's also what makes the "solo advantage" argument real: for the first time, small firms have access to something close to the same horsepower as Big Law, without the budget Big Law required to get it. This one lands hardest for Simpsons lawyers — the ones already poking at ChatGPT on their phones but who haven't connected it to a real problem in their practice yet. It's also a reality check for anyone tempted to treat AI as a universal fix: the practice signals segment on the homeless summer associate is a reminder that some problems are human problems first, and AI is a research tool for finding help — not the help itself. Mentioned in This Episode Dr. Sara Kubik (guest)Claude, ChatGPT, Gemini, Grok, Midjourney, DaVinci ResolveCodex (ChatGPT)Power BI, SQL, ExcelClioPerplexityCooley Law SchoolPurdue UniversityUniversity of Chicago Law SchoolIndiana State Bar AssociationRedditinfo@drescherlaw.com Note: As mentioned in the episode: Is Mitch McConnell's photo real or fake? — Poynter/PolitiFact Note: This photo has been the subject of online debate regarding its authenticity. We're linking to it as referenced in the episode and take no position on that controversy.

  8. Jul 9

    Episode 019 Bankruptcy Meets The AI Revolution

    Show Notes Most bankruptcy lawyers still don't know what their own firm actually makes each month. Jenny Doling built a revenue dashboard in Claude to find out — and then kept going, building analyzer skills for pay stubs, tax returns, bank statements, and Chapter 13 objections that her staff now runs without her. If a nationally recognized bankruptcy attorney and NACBA's incoming president is running her practice this way, what's the excuse for everyone else? In this episode: Jenny Doling's path from paralegal to bankruptcy attorney to NACBA president-elect, and how she got hooked on consumer bankruptcy work back in 2004The scale of the current bankruptcy surge — commercial and individual filings both up sharply since 2022 — and what firms without systems riskWhy Jenny moved her practice from ChatGPT to Claude, and how she uses Claude skills, projects, and Cowork day to dayThe analyzer stack she built: pay stub analyzer, document renamer, bill analyzer, tax return analyzer, and bank statement analyzerHer Chapter 13 Opposition Builder skill — loaded with trustee objections, Collier on Bankruptcy material, docket reports, and key case lawConfidentiality practices: a closed Claude Team environment, a written office AI policy, and PII lockdowns before staff touch any toolWhy Jenny separates time saved from work-product elevation, and argues the second is the real benefit of AIClient-facing AI disclosures in her fee agreements, and why she avoids Zoom's built-in AI transcription specificallyA frank take on legal tech vendor Glade versus incumbents like Clio, and why responsiveness matters more than market shareThis week's Season 2 build step: organizing the firm knowledge assets identified last week, tiered by Flintstones, Simpsons, and Jetsons lawyersWe also discuss: Case law and citation practices for Chapter 13 objections, including Hamilton v. LanningRecording practices — why "team record" doesn't mean "team Zoom record"AI transcription and notetaking tools, including Whisper, Granola, and wearable recordersNACBA's upcoming AI-focused conference programming in Orlando and MauiA Practice Signal segment on a solo PI lawyer's shrinking case volume and how Jenny would advise him to diversifyKey Takeaway Jenny Doling isn't impressive because she uses AI. She's impressive because she treats her analyzer skills as infrastructure — quality control built in, confidentiality locked down before staff ever touch a tool, and her name still on every pleading that goes out the door. That's the difference between dabbling and running a practice. This one's for the Simpsons lawyer wondering how far a solo or small firm can actually take this. It gives Flintstones lawyers permission to start small, and it gives Jetsons lawyers something sharper to aim for — the difference between an old-school Jetsons practice and what Jenny calls "AI Jetsons." Mentioned in This Episode NACBA (National Association of Consumer Bankruptcy Attorneys)Claude (skills, projects, artifacts, Cowork)ChatGPTBest Case DesktopJubileeNext ChapterSharePoint / OneDriveClioGladeTara SalinasMatt McCuneinfo@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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