AI in Legal Practice

CFO to General Counsel last week: "I read that AI can review contracts now. Why do we still need three legal FTEs?" GC's internal monologue: "Because AI can't negotiate with an angry customer at 9 AM, navigate a GDPR audit at 11 AM, and explain to the board why that 'simple contract' could expose us to €2M liability at 3 PM?" Welcome to 2025, where every General Counsel is expected to: ✅ Implement AI to "cut costs" ✅ Reduce legal headcount ✅ Still deliver faster contract turnarounds ✅ Maintain zero risk tolerance ✅ Be a strategic business partner All by yesterday. Preferably with no budget. Here's what leadership sees: --> AI reviews 100 contracts in minutes! Here's what they miss: --> Who reviews the AI's output? --> Who handles the 15 edge cases it can't process? --> Who negotiates when the customer pushes back? --> Who coordinates with Sales, Finance, and IT? --> Who makes the final call on acceptable risk? The pressure is real. CFOs read one article about "AI replacing lawyers" and suddenly expect the legal department to automate itself out of existence. But here's the truth: AI is powerful for legal teams - when used right. The goal isn't to replace lawyers. It's to free them from the repetitive work that buries them: → Initial contract reviews and risk flagging → Answering the same compliance questions repeatedly → Tracking obligations and renewals → Generating routine agreements That gives your team capacity for what actually matters: strategic negotiation, risk assessment, business partnership, and preventing the fires nobody sees. Smart legal leaders aren't asking "How do I replace my team with AI?" They're asking "How do I use AI to make my team 10x more effective?" How is your leadership team thinking about AI in legal right now?

Harvard University and Perplexity published a paper today that reframes the next five years of legal work. What makes it credible is the method. Instead of asking people how they feel about AI, the researchers pulled real production data and compared the same users doing the same tasks two ways: once with a conversational assistant, once with an autonomous agent. Ten thousand matched task pairs. Same person, near-identical query, two tools. About as close to a controlled experiment as you get outside a lab. The numbers aren't incremental, they're structural. The agent ran 26 minutes of autonomous work per session against 33 seconds for the assistant. Average task time fell from 269 minutes to 36. Estimated cost dropped 94%. And the part I didn't expect: dissatisfaction was 55% lower with the agent. It wasn't just faster, people judged the output as better. But the number that kept me up was about scope, not speed. Agent users worked outside their own primary occupation far more often than with the assistant. One professional plus an agent started absorbing work that used to be split across separate specialists, legal and financial and technical, inside a single workflow. The paper calls this a "reduction in coordination costs." Where I sit, it reads more like a quiet redefinition of how legal work gets bundled and billed. And law is not on the sidelines. Legal & Compliance already accounts for 5.5% of agent queries. More telling, the legal tasks weren't simple lookups: agent queries in law engaged about 60% more fine-grained work activities than the same users' assistant queries. Research, analysis, drafting, tool calls, document delivery, handled end to end. How you interact changes too. With an assistant, your follow-ups mostly clarify or correct, you're steering a search engine. With an agent, they shift toward verifying and extending, you're reviewing a deliverable that already exists. That's the difference between operating a tool and supervising one. For those of us in banking and insolvency work, the value isn't asking a smarter search engine a better question. It's the end-to-end workflows: large-scale due diligence across an NPL or UTP portfolio, restructuring scenarios braiding financial modeling, legal exposure, and regulatory checks. Exactly the multi-step, multi-domain, high-effort-but-verifiable tasks the paper flags as the agent's home turf. The lawyer's job moves up the stack: judgment, strategy, validation. To be fair: the study covers a 90-day window dominated by early adopters, and the authors say so. But the direction isn't ambiguous. So the question for any legal team isn't whether to use AI. It's which of your workflows actually involve multi-step execution across legal, financial, and documentary domains, and how you'd redesign them around a real division of labor between lawyers and agents. That redesign is the hard part. The technology is already here.

15 hours saved per week = 50% more people helped. This is why we build AI. Over the past year, we've watched legal nonprofits equipped with CoCounsel save up to 15 hours per week and increase their capacity to serve clients by as much as 50%. But the real story isn't in the numbers - it's in the impact: ✅ Veterans Legal Institute preparing motions in minutes instead of hours, keeping hundreds of veterans housed ✅ The Innocence Center cutting petition preparation time by 50%, potentially freeing innocent people from prison years earlier ✅ Legal Aid Society of San Bernardino serving 50% more urgent calls for legal advice on critical cases such as domestic violence This program shows how AI can address one of society's most pressing challenges: the justice gap affecting 90% of civil legal needs in the US. When Pablo Ramirez from Legal Aid Society of San Bernardino says "AI allows us to cut through hours of paperwork and focus on what truly matters — standing with a survivor in court," that's the future of professional work we're building toward. This is just the beginning. Excited to continue expanding access to justice through responsible AI innovation. Read more: https://lnkd.in/gyKS7XTG #AI #LegalTech #Justice #Innovation #ProfessionalWork #CoCounsel

Most coaches & consultants don’t have a time problem. They have a systems problem. AI doesn’t fix chaos. It scales whatever system you already have. Here are 5 AI tools that actually plug into your daily workflow (with real use-cases): 1. ChatGPT: Use it to think, not just write. Daily integration: Pre-call: Generate 5 sharp questions based on client background Post-call: Convert notes into insights and next steps Sales: Practice objection handling before discovery calls Example: “Here are my client notes → identify blind spots and suggest 3 tough questions for next session.” 2. Notion AI :Your second brain for client delivery. How to use: Create client dashboards with auto summaries Maintain SOPs for your programs Turn session transcripts into insights + next steps Example: Upload session notes → “Summarize key breakthroughs + assign action items” Your client gets clarity instantly. 3. Descript: Content creation without the headache. How to use: Edit podcasts/videos by editing text Remove filler words automatically Repurpose long-form content into shorts Example: Record a 20-min coaching insight → Cut it into 5 LinkedIn videos + 10 reels in under an hour. 4. Otter.ai.: Never miss what your client actually said. Daily integration: Record and transcribe coaching calls Highlight key patterns across sessions Build a repository of client insights over time Example: Spot recurring phrases like “I feel stuck” and use that language in your next session to go deeper. 5. Make: Where everything connects. Daily integration: Auto-send session summaries after calls Connect forms to CRM, email, and task managers Build end-to-end onboarding flows Example: Client fills a form, gets a calendar link, books a call, receives a prep doc, and you get a summary. All automated. Here’s the shift most people miss: Don’t ask, “Which AI tool should I use?” Ask, “Which part of my workflow is still manual?” That’s where AI fits. Because the goal isn’t to use more tools. It’s to free up more thinking time. What’s one task in your workflow you’d love to automate right now?

A LegalTech Startup Promised You “70% Time Saved” with GenAI? Read this before you sign anything. In the rush to embrace generative AI sweeping through India’s legal ecosystem, it’s easy to mistake innovation theatre for real value. After testing and advising on a number of solutions in this space, here’s what I’ve learned: 1. Start with skepticism—not excitement Don’t get swayed by glossy demos. Insist on 2–4 week trials under NDAs. If they can’t work with your data, they’re not ready. 2. Train your team before buying tools Start with free tools like ChatGPT to build internal fluency. This helps your team spot what’s actually useful—and what’s just shiny packaging. 3. Test it on your documents Vendor samples are designed to impress. True value shows up when tested on your contracts, your regulatory documents, and your jurisdictional nuances. 4. Start with low-hanging fruit: extraction, summaries, translations LLMs shine in narrow, high-volume use cases. Think: - pulling out renewal dates, termination clauses - summarising long filings or judgments - translating regional documents with legal nuance 5. Ask for evidence—not promises Ask for real benchmarks. Indian vendors often skip this. The VALS.ai framework is a great place to start understanding what “good” looks like. 6. Check for implementation muscle Beyond the model, ask: • Is it secure? • Will it integrate with our systems? • Does it handle hallucinations? Solutions built with legal SME input tend to be far more grounded in reality. 7. Ask the hard questions What models power it? What’s the training data? Can it handle edge cases? These answers reveal whether it’s true innovation—or just another repackaged interface. The best GenAI tools for law aren’t the flashiest—they’re the ones that get how lawyers actually work. What’s been your experience navigating this space?

In case you’re looking for some holiday season reading, I am pleased to give you access, via this newsletter, to my new white paper, "AI and the Law: What’s Working, What’s Not and What’s Next." This 8,000-word resource is based on my three years as an adjunct professor at Bond University researching how AI is transforming legal practice. It gives lawyers a practical, evidence-based guide for navigating AI safely, productively and with confidence. Warning: Side effects from reading the white paper may include confidently correcting your relatives about AI at festive season gatherings. Key takeaways from reading the white paper: • Real-world clarity: See the true impact of AI on people, pricing, processes and client expectations - beyond the hype • Which tools work: Identify the Legal AI tools that genuinely deliver value and how to integrate them into your practice • Proven strategies: Learn the methods that consistently underpin successful Legal AI projects in firms and in-house teams • Training reinvented: Explore seven evidence-based ways to develop junior lawyers as AI takes over traditional entry-level tasks • Effortless prompting: Forget the stress of prompt engineering and use the simplest, most effective way to prompt – meta-prompting • Future-proof insight: See the steps lawyers must take now to thrive, not struggle, as AI becomes embedded across the profession   Over the next few weeks, may your holidays be long and your inbox merciful. I wish you some well-earned rest, time with the people who matter most and an energising start to 2026. Kind regards Nick

Doctors already know that patients are asking generative AI bots like ChatGPT, Claude and Gemini for medical advice. Used wisely, and with clinician guidance, they can help patients bridge the gap between mere medical knowledge and medical expertise. Used carelessly, however, GenAI can do harm. In The New York Times, reporter Simar Bajaj asked healthcare and technology experts how to use large language models safely. I shared a few ideas alongside others, including Michael Turken, MD, MPH, Ainsley MacLean, MD, FACR, Raina Merchant, and Ravi B. Parikh. Key takeaways for patients and clinicians: ➡️ Practice when the stakes are low. Test prompts on questions from a past visit and compare answers with what your doctor said. ➡️ Share context, not identity if you wish to keep your medical information totally private. Include age, meds and conditions to personalize advice, but skip names, addresses or other identifiers. For added privacy, turn on "temporary chat" or similar features in LLMs. ➡️ Do regular reality checks. In a long chat, ask the model to summarize what it knows about your history and correct it when needed. ➡️ Invite follow-ups. Prompt with “Ask me any additional questions you need to reason safely” so it doesn’t skip what a clinician would ask. ➡️ Remember AI's role. Right now, chatbots are not ready for "prime time." They can prepare you for better conversations with your doctor, not replace them. ➡️ Look to the future. The combination of AI-empowered patients, dedicated clinicians and genAI will prove exponentially more powerful and healing than any of the three alone. Thanks to Simar and the NYT for a thoughtful piece on how to get the benefits of GenAI in medicine while avoiding the risks. (Article: “5 Tips When Consulting ‘Dr.’ ChatGPT,” Oct. 30, 2025. Link in the comments.)

RIBA Journal recently asked me how I would be approaching AI if I were a sole practitioner... We mainly work with large orgs where we are trying to enable their team with AI at scale; but I expect many of the same challenges exist for small and micro practioners too. AI can easily become a massive time-sink for solo architects if you are haphazard in your explorations. If you want to integrate AI into your practice without wasting hours tinkering and failing to launch, here is the pragmatic playbook: A. Stop using free AI tools that harvest all your data and cause inaccuracy. Practically everyone starts with the free version of ChatGPT. My advice? Don't. Free tools usually retain your data permanently and train their models on it. They also have the smallest context window of all, so more prone to error. Instead, find an entry-level paid subscription and check the terms to protect your practice's (and others') IP. B. Treat AI like a "digital co-worker" AI is now highly proficient but needs good data and instruction to work well and you likely need some training to understand risks and limitations. Don't jump straight into complex "vibe-coding" or trying to get AI to do your core job. Use it for word-heavy lifting: drafting appointment contracts, fee planning, spec writing and answering contractor RFIs all from trusted source material. If you want more of a visual impact and hand-drawing and 3D skills, pair them with Nano Banana Pro for quick results - this model is incredibly coherent to your instructions and simple to use. C. Score your ideas before you start. Write down 10 ideas for how AI could help your practice today. Then, score them on three criteria: 1️⃣ Is it simple? 2️⃣ Is it low risk (i.e., you are competent enough to spot any errors in this particular task)? 3️⃣ Will it generate value (save your essential time or win you work)? Then, start ONLY with the highest-scoring ideas one at a time. Solve each idea before moving onto the next. Article link in the comments 👇

AI isn’t replacing you. It’s sitting next to you. At Copenhagen Legal Tech’s First Tuesday, Werner Valeur shared so many great insights, but this one stuck with me: 🤖 Technology is your new colleague. I’d take it a step further: 🤖 AI is your new colleague. It’s not just another tech tool. Treat AI like a new coworker. Like any good colleague, AI requires context and interaction to deliver real value. The better you communicate with your new coworker - the better the results. The more you work together, the more you learn about their strengths and weaknesses. Laura Frederick helped further refine and visualize this concept yesterday while we were chatting about challenges of AI adoption in contracting. Use AI like you would when we worked in offices, and would drop by one of your office besties to run an idea by them, get a different opinion, refine argument or get a gut check. Here are some ways that you can use AI right now across all genAI chat tools like ChatGPT, Copilot, Claude, Gemini, Perplexity and legal specific AI tools like Wordsmith. How AI Can Assist Legal Professionals Right Now: 🧠 Brainstorming & Idea Generation - Generate new ideas and explore different perspectives. - Provide counterarguments to strengthen legal reasoning. - Get suggestions for alternative approaches to problems. 🤝 Negotiation & Scenario Testing - Play out different negotiation scenarios and refine your position. - Run hypotheticals or play devil’s advocate to stress-test legal arguments. 📑 Document & File Management - Spot differences between contract versions or precedent documents. - Organize messy notes into structured documents. - Structure messy drafts, clean up formatting, and standardize layouts. - Easily convert between file formats while maintaining all the information. 📝 Summarization & Transcription - Quickly extract key points from lengthy agreements or case law. - Transcribe and/or summarize meeting transcripts or notes to capture key takeaways and action items. 👀 Clarity & Refinement - Test writing for clarity and readability. - Ask AI to simplify or refine complex legal language. - Make writing more concise by cutting unnecessary details. - Turn text into bullet points, a table, or image (tip: Claude is better at making slide images). ⚠️ Risk & Consistency Checks - Highlight potential red flags in agreements. - Check for inconsistencies in responses or across multiple documents. - Ensure legal solutions align with specific legal rules, frameworks, or precedents. - Identify assumptions made in legal arguments. - Validate responses against the latest case law or regulatory updates. - Stress-test whether legal advice holds under different conditions. 🗣️ Client & Internal Communication - Tailor responses based on tone and audience. - Provide second opinions or alternative views on legal arguments or advice. - Prepare clear, concise explanations for clients or stakeholders. - My favorite: check for typos!

AI can be a differentiator for law firms -- but only if the value can be explained in plain language. ✅ Most clients want predictability, transparency, and regular communication with their attorneys, all at a reasonable total cost. ✅ These days, most clients assume you are using AI somewhere in the background; what they may not know is where, how, and with what safeguards. That gap is where trust can be built or broken. If your firm has not yet defined the value of AI-enabled legal work to clients, consider creating a clear, client-facing narrative that covers three things: 1. Where AI shows up in your workflows (research, drafting, review, budgeting, etc.). 2. How lawyers/partners stay in the loop and review to ensure accuracy, quality, ethics, and judgment. 3. How clients will see the benefits in outcomes, speed, and total matter spend The attached questions are designed to help law firm clients ask better questions about AI use - and it’s also a great internal checklist for outside counsel partners, practice leaders, and pricing teams. Use it to pressure-test your firm’s story: ➡️ Do you have and can you provide concrete examples of how AI has improved cycle time, reduced costs, or increased consistency in your work product? ➡️ Can you articulate guardrails, governance, and training, not just mention tools and vendors? A few practical ways for firms to use this document: -- As a framework for partner meetings, planning pitches, RFPs, or talking points during client QBRs. -- To align marketing, BD, and innovation teams on how the firm positions AI in the market. -- As a starting point for updating OCGs (outside counsel guidelines), playbooks, and pricing conversations. To prepare your lawyers for the questions sophisticated in-house counsel and other buyers of outside legal services are already asking. Firms that define their AI value proposition and welcome these conversations will stand out as more transparent, more innovative, and ultimately more trusted. If you find this helpful, consider saving it and reposting it for others. Thank you. #lawfirms #AI #legalprofession