AI Tools for Practicing Lawyers

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 w

AI Tools for Practicing Lawyers is a business podcast hosted by Ron Drescher, with 47 episodes on record and a Required Pod Score of 80.

Ron Drescher hosts AI Tools for Practicing Lawyers, a business show with 47 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #31

  Episode 031 AI and Access to Justice

  Oct 1, 202651 minS3

  SHOW NOTES Maryland has about 40,000 lawyers. Roughly half a percent of them work in civil legal aid. Add more lawyers and you still hit a ceiling, because every lawyer can only serve so many people. Reena Shah , Executive Director of the Maryland Access to Justice Commission , joins Ron and Heather to ask what happens when technology breaks through that ceiling, and what happens when the general-purpose chatbots break through it first. What if AI's biggest contribution to law isn't making lawyers faster, but reaching the people who never had a lawyer at all? In this episode: - The civil justice gap in Maryland: no right to counsel in civil cases, 40% of households unable to cover basic needs, and legal aid organizations meeting roughly 20% of demand - Maryland's AI and Access to Justice Summit, one of the first state commission events of its kind, and why it was run as a hands-on workshop instead of a lecture - What a pre-summit survey of 187 legal aid staff revealed: few AI policies, few enterprise tools, and no clear organizational stance on staff use - Why Maryland Legal Aid's Vicki Schultz says policy comes first, and why that policy has to start with the organization's values - Self-represented litigants walking into Court Help Centers with 20-page AI-generated pleadings, and refusing to believe the lawyers who tell them it's wrong - The Innovations in Tiered Legal Services Task Force and its push for a UPL waiver to test safe, closed AI tools in low-risk areas of law - Ron and Heather's disagreement: teach pro se litigants to use AI responsibly, or hand them a vetted tool? And is the teaching itself UPL? - The Nippon Life lawsuit against OpenAI, and why Reena argues the big AI companies crossed the UPL line without ever asking permission - Users vs. builders for legal aid: Maryland Legal Aid's home-grown MLA GPT, rent ledger reviewer, and brief creator versus off-the-shelf tools like NotebookLM - A TurboTax-style expungement tool as a model for scaling high-volume legal help We also discuss: - Anthropic's Claude for Legal launch and the connectors and skills it included for pro bono and legal aid organizations - Whether AI companies should give legal aid organizations pro bono or reduced-cost access, and who is actually asking them - Why AI is a prediction model, not a search engine, and how that distinction shapes training - The People's Law Library and the work of making legal websites accurate enough for AI tools to pull from - Practice Signals: a Reddit lawyer asks where the access-to-justice gaps are, and Ron adds a fourth question about what AI can fill - The AI Doc: Or How I Became an Apocaloptimist, and a preview of the show's 50th episode Key Takeaway Reena's argument is about math, not efficiency. Every lawyer has an upper limit. Technology is the only thing on the table that scales past it, but only if it's built safely and vetted before anyone relies on it. Meanwhile the general-purpose chatbots are already giving legal advice to people who can't tell a good answer from a confident one. Availability is not authority. The pro se litigant arguing with the Court Help Center lawyer hasn't learned that yet. For users, the lawyers running off-the-shelf tools, this episode is a warning about who is sitting across the table: more and more often, it's someone holding an AI-drafted answer they trust more than you. For builders, Maryland Legal Aid shows what a small team with real subject-matter expertise can do at low cost: narrow tools aimed at narrow, repetitive problems. Solo and small firm lawyers live with the same constraint legal aid does. There's more demand than there are hours. Mentioned in This Episode - Reena Shah, Executive Director, Maryland Access to Justice Commission - Heather Gardner, co-host and summit speaker - Maryland AI and Access to Justice Summit - Innovations in Tiered Legal Services Task Force (Maryland Access to Justice Commission, Maryland Judiciary, Maryland State Bar Association) - Maryland State Bar Association - Maryland Legal Aid and MLA GPT - Vicki Schultz, Maryland Legal Aid - John Jeffcott, Maryland Legal Aid - Donald Tobin, former Dean, University of Maryland School of Law - Chief Justice Matthew Fader, Supreme Court of Maryland - Lindsay Bramble - Angela Tripp, National Center for State Courts (formerly Legal Services Corporation) - Legal Services Corporation - Stanford University national summit on AI and access to justice - Maryland Volunteer Lawyers Service (MVLS) - Maryland Court Help Centers and county family self-help clinics - People's Law Library of Maryland - Anthropic and Claude for Legal - OpenAI and ChatGPT - Google Gemini and NotebookLM - Grok - Nippon Life Insurance Company of America v. OpenAI (N.D. Ill.) - The AI Doc: Or How I Became an Apocaloptimist (2026) - Reddit (source of this episode's Practice Signals post) Highlight Reels on YouTube and Instagram YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

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  Episode #30

  Episode 030 Workflow Options: Irys

  Sep 24, 202643 minS3

  SHOW NOTES Lawyers are building Claude skills, custom GPTs, and homegrown intake bots faster than ever. Most of what they're building will be obsolete in six months, because the foundation models keep swallowing whole product categories. Sabih Siddiqui left Big Law to co-found Irys.ai , a general-purpose legal AI platform. What started as a $299 off-the-shelf app turned into something closer to a custom infrastructure shop. His view is that a skill is a feature and a platform is a different thing. Should you build your own AI tools, buy them off the shelf, or pay someone to build them for you? In this episode: Why Sabih thinks lawyers are "behind" on AI, and why the conference rhetoric hasn't moved past ROI and basic privacy fears The sustainability problem with building on Claude skills when the platform underneath changes every week An in-house legal team that ran its intake on Claude skills, and what changed when Irys rebuilt it with 36 engineering hours Irys's "dirty little secret": the $299 off-the-shelf app is an entryway into custom, white-labeled builds How a two-attorney pilot grew to 77 users across legal ops, compliance, and finance Growing to roughly 400 law firms and in-house teams through word of mouth, Facebook groups, and bar association channels, with no marketing spend Harvey and Legora as first movers, and what Sabih calls "wave 2" of legal AI Flat-fee opinion letters at $3,500 that take one hour, and why clients are paying for decades of judgment rather than the hour In-house clients demanding proof of how outside counsel uses AI, including shared workspaces and per-client-folder ROI reports Ron and Heather's disagreement over whether rank-and-file lawyers want custom builds or something that simply works off the shelf We also discuss: Every CLE is about AI now, including Ron's skepticism about who's attending the Subchapter V session Heather's pushback that lawyers are racing to the AI CLEs, not implementing anything back at the office Tools versus platforms: whether a firm gets better ROI from mastering Word, Slack, and the phone system or from adopting an AI platform Practice Signals: a PI firm on Reddit losing track of who owns the next step on new cases. Sabih's answer is to set it up in Claude or ChatGPT if you don't want a vendor. Claude for Legal versus OpenAI's legal offering, and why Sabih thinks features are commoditizing while workflows hold the value Token-based billing and what happens when two attorneys run up a Claude bill in two weeks Key Takeaway Sabih's sharpest point is that a feature isn't infrastructure. A Claude skill gives the model a layer to work on, but it does nothing about ingestion, memory, permissions, or the three systems your documents actually live in. Every builder has to decide whether they're building something durable or something the next model release will make irrelevant. The judgment behind the work stays with the lawyer. Everything else is up for grabs. That split runs straight through the Simpsons crowd. The lawyer who's already building skills at night is on a Jetsons path and needs to know where the ground is stable. The lawyer who shows up for a 30-second hearing and goes back to the office doesn't want to build anything. He wants something that works for $299 a month and someone who answers the phone when it doesn't. Heather and Ron don't agree on which group is bigger, and neither answer is obviously right. Mentioned in This Episode Sabih Siddiqui, CEO & Co-Founder, Irys (irys; formerly Akita / Iqidis) Harvey Legora Claude, Claude skills, Claude Code, Cowork, Claude for Legal (Anthropic) ChatGPT and OpenAI's legal offering Eve Legal Lexis (Harvey integration) Palantir (the "Palantir approach") Skadden Gmail, Google Drive, Slack, Microsoft Teams, Zoom, Skype, Microsoft Word TTAB litigation Highlight Reels on YouTube and Instagram info@drescherlaw.com

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  Episode #29

  Episode 029 AI and the Rise of the Superpowered Solo

  Sep 17, 202655 minS3

  SHOW NOTES Every lawyer who's ever complained that no software vendor understands their practice has secretly wondered the same thing Tara Salinas actually did something about: what if you just built it yourself? Tara, a consumer bankruptcy lawyer in Denver, didn't buy a client intake system — she built one, on a stack most lawyers have never heard of, and she's convinced you could too. If you've never touched code, does "build it yourself" actually mean anything for your practice — or is it Jetsons talk that only works for people who were already halfway there? In this episode: - How Tara went from a folder of sideways phone photos labeled "scan001" to a Claude-built client portal that auto-identifies, renames, and sorts every document a client uploads - The nuts and bolts of that portal: a Supabase database on a Vercel/Next.js front end, running an API that scores its own confidence and routes anything uncertain to a "needs a human eye" folder - Why Tara moved from ChatGPT to Claude in February 2026 and started building with Claude Code, Projects, and custom skills - ABA Formal Opinion 512 and the argument over what actually counts as a "self-learning" AI tool requiring client disclosure and consent - Individual vs. team AI subscriptions — why sharing one login with staff likely isn't enough to meet the compliance bar, regardless of platform - The difference between a "skill" and an "agent" — Ron's Family Guy arms analogy, and where agentic activity starts creating real risk - Why AI chatbots are steering financially distressed consumers toward debt settlement instead of bankruptcy, and what that means for the profession - NACBA's incoming AI Advantage program: a prompt library, a skill library, and a microlearning track for training the next generation of bankruptcy lawyers - Tara's client AI policy — the privilege risk of running case documents through a third-party AI tool, and why she bills clients who hand her "AI slop" - The Users and Builders framework: what a user does with a business problem versus what a builder does with the same one We also discuss: - Tara's path from a near-miss patent law career and an unfinished electrical engineering degree into bankruptcy law - The bankruptcy bar's demographic gap after 15 quiet years under BAPCPA, and why the profession needs more young lawyers now - Practice Signals: a burned-out seventh-year associate on Reddit weighing solo practice against leaving law to sell AI software - How a bankruptcy lawyer can go solo with volunteer cases, trade groups, and a notoriously collegial bar - Screenshot hoarding and how to actually manage the AI tool tips piling up on your desktop - AI as "durable infrastructure" — Ron's comparison to the internet and the electrical grid Key Takeaway Tara Salinas didn't set out to become a software developer. She set out to stop losing hours to a folder of unnamed scans, and one afternoon with ChatGPT turned into a purpose-built client portal running its own confidence-scored document API. The lesson isn't that every lawyer needs to learn Supabase and Next.js — it's that the line between "using a tool" and "building one" is a lot thinner than most lawyers assume, and crossing it starts with one file, not a development budget. This lands differently depending on where a lawyer sits. Flintstones lawyers need permission just to try the one file Tara keeps preaching. Simpsons lawyers are the ones actually deciding whether to stay users forever or start building small — a bank statement analyzer, a skill, a prompt library entry. Jetsons lawyers like Tara are already past that question, and the show's real pivot this season is that Users and Builders, not Flintstones and Jetsons, is becoming the more useful way to sort the room. Mentioned in This Episode - Tara Salinas, consumer bankruptcy lawyer, Denver, Colorado - NACBA — Me, Myself, and AI webinar; incoming AI Advantage program - ChatGPT - Claude, Claude Code, Claude Projects and skills - ABA Formal Opinion 512 (2024) - Supabase, Vercel, Next.js - Glade.ai - Yvonne Nath and last week's episode on Counsel Commons and skills vs. agents - Jen Grondahl Lee, Lawyers Success Network - Complete Bankruptcy/Team Accelerator (Ron's referenced training program) - Family Guy (referenced for the skills-vs-agents analogy) - ECF and Infusionsoft (referenced in Ron's email-automation story) - DocuSign info@drescherlaw.com Highlight Reels on YouTube and Instagram

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  Episode #28

  Episode 028 AI Skills and the $1500 Divorce

  Sep 10, 202640 minS3

  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

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  Episode #27

  Episode 027 Is it Safe to Go to the Rave?

  Sep 3, 202656 minS3

  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 Compliance The "Wilma Simpson" hypothetical — a bankruptcy lawyer using AI to summarize a 180-page court order, and where the real risk actually sits Ron and Jim's sharp disagreement over whether a "reversible" AI mistake stays reversible once a client acts on the lawyer's advice built from it Why reviewing a massive document production (sorting, summarizing, deduplicating) shifts trust from individual verification to system-level design An e-commerce company's real-world case study training a model on product-safety complaint data — six months of retraining to reach 90–95% accuracy The malpractice waiver hypothetical: what an in-house lawyer does when outside counsel wants to disclaim liability for AI-assisted work Client consent and data privacy limits on using AI to process a client's own personal information The 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 safeguard The "non-delegable duty to think" — what AI can never take off a lawyer's plate We also discuss: The Flintstones/Simpsons/Jetsons framework applied to matching lawyers with the right clients A 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-capable The Season 3 shift from Flintstones/Simpsons/Jetsons to a new "Users and Builders" framework First 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 Technologies Jim Casey's articles: Knowledge in an Agentic World and Agentic Workflows: A New Frontier for Compliance ChatGPT Claude Harvey (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

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