Smarter Data Practices for LegalTech Professionals

It's been about two years since law firms started adopting AI tools. Even though the results should be resoundingly positive, the feedback, quite frankly, is mixed based on all of the surveys I have read. Firms report concerns around inconsistent internal adoption, varied client permissiveness regarding AI usage, varying accuracy levels, lack of training, lack of clarity around workflow improvement, and little positive change to firm economics. The firms that are winning the AI-enabled law firm race share a common trait: they studied their data before implementing AI. Those that are struggling took the "ready-fire-aim" approach, jumping on the bandwagon after believing the promises of AI salespeople that GenAI is an out-of-the-box panacea. Make no mistake—I may be the BIGGEST fan of AI adoption in the legal industry, but AI must be adopted in a smart and strategic manner. Smart people don't start taking medicine without understanding their underlying condition; the same should be true of how firms adopt AI. Billing data continues to be the key component for guiding an AI strategy. The Critical Challenges: -Transitioning to Value-Based Pricing. Your billing data reveals which matters are actually profitable under the billable hour. This baseline is essential for setting competitive fixed fees that protect margins while meeting client demands for predictability. -Eliminating Training Inefficiencies. Historic matter data shows exactly where junior lawyer time added value versus where it inflated costs. As AI eliminates routine junior tasks, this analysis helps you redesign training programs and staffing models that work. -Shifting from Input to Output Pricing. Years of time entries tell you what it actually costs to deliver specific outcomes. Without this intelligence, you're guessing at project-based pricing—and likely leaving money on the table or overpricing yourself out of opportunities. -Investing in the Right AI Tools. Your billing patterns reveal which practice areas have the highest volume of repetitive, high-margin work—the sweet spot for AI automation. This data drives ROI-focused technology decisions, not vendor promises. Your billing data is a strategic goldmine. It shows you: • Where you're most efficient (and why) • Which clients and matters are actually profitable • What optimal staffing looks like by matter type • How to price competitively in a value-based world The firms winning in 2026 won't just blindly implement AI—they're leveraging years of operational intelligence to transform strategically, not reactively.

AI is no longer a pilot project for legal teams. It is already embedded in the tools many of us use every day. That makes the real challenge less about adoption and more about judgment. A few hard-earned lessons: • AI usually arrives bundled into platforms, not as a clean standalone decision. Treat it as an operational commitment, not a feature toggle. • Technical and data constraints matter more than demo performance. If the AI cannot integrate cleanly or respect your data boundaries, it will not scale. • Vendor maturity and AI maturity are not the same thing. Narrow, well-defined AI use cases tend to outperform ambitious, opaque ones. • Language and interface design are not cosmetic. If the system is imprecise, the AI built on top of it will be too. • Implementation timelines are almost always optimistic. AI does not fix messy data. It exposes it. • The most revealing question remains simple: if the AI fails, does the system still work? AI should accelerate legal judgment, not replace it. Teams that treat AI as additive, bounded, and governable are seeing durable value. Teams that chase novelty are still paying for it later. Curious how others are pressure-testing AI tools in their legal tech stack this year. I am Colin S. Levy and I serve as General Counsel at Malbek. I spend much of my time teaching, advising, and writing about how lawyers can work with technology in ways that are practical, responsible, and grounded in how legal work actually gets done. #legaltech #innovation #law #business #learning

Singapore’s Ministry of Law has published a sector-specific Draft Guide for Using Generative AI in the Legal Sector, a timely blueprint that seeks to reconcile the productivity gains of GenAI with the enduring professional obligations of legal practice. Released for public consultation (1–30 September 2025), the guidelines aligns the IMDA’s Model AI Governance Framework with the Courts’ guidance for court users and sets out practical, non-binding standards for responsible adoption. At the conceptual core the Guide foregrounds three interdependent principles: professional ethics (insisting on a “lawyer-in-the-loop” and preserving ultimate professional responsibility), confidentiality (data classification, preference for enterprise or on-premises solutions where client confidentiality is material, and contractual assurances against use of inputs for model training), and transparency (client disclosure, opt-out rights, and readiness to explain verification steps in court). The text is frank about GenAI limitations, notably hallucination and bias, and prescribes concrete mitigants such as grounding, retrieval-augmented generation (RAG), and robust vendor due diligence. Operationally, the Guide prescribes a five-step implementation pathway: develop an AI adoption framework; diagnose and priorities needs; identify and evaluate tools against data-security and performance criteria; implement with staged pilots and structured training; and institute continuous review. A copy of the draft guidelines is enclosed for reference. P.S. This is for academic discussion only. #GenerativeAI #LegalTech #ResponsibleAI #LegalEthics #DataPrivacy #AIinLaw #SingaporeLaw #AIRegulation #LawFirmInnovation #LegalAI #ProfessionalResponsibility

This will change the legal world forever. My mind was blown learning this AI literacy is now a must for every lawyer. I never thought I would say that. But it is true. The legal field is changing fast. AI is not just a buzzword. It is a real tool that is already shaping how we work, think, and serve clients. Here is why AI literacy matters for lawyers: → AI is already in your daily work. From contract review to legal research, AI tools are everywhere. If you do not understand them, you risk falling behind. Clients expect you to know how to use the best tools to save time and money. → It protects your clients. Knowing how AI works helps you spot errors, bias, or risks in AI-generated work. You can give better advice and keep your clients safe. → It keeps you relevant. Law is a knowledge business. The lawyers who learn AI will lead the way. Those who do not will be left behind. → It helps you work smarter, not harder. AI can do the boring, repetitive work. You get to focus on strategy, judgement, and the human side of law. How to build AI literacy as a lawyer: → Start with the basics. Learn what AI is and what it is not. You do not need to code. You do need to know what tools are out there and what they can do. → Use real tools. Try out AI legal research platforms, contract review tools, or document automation. See how they work. Notice their limits. → Stay critical. AI is not magic. It makes mistakes. It can be biased. Always check the output. Use your legal mind to spot problems. → Keep learning. AI is moving fast. Read legal tech blogs. Join webinars. Talk to peers who are using AI. Share what you learn. → Focus on ethics. AI raises new questions about privacy, bias, and fairness. Know the rules. Think about how to use AI in a way that is safe and fair for everyone. My top tips for lawyers who want to get ahead: → Do not wait for your firm to train you. Take charge of your own learning. → Build a habit of testing new tools. Even ten minutes a week adds up. → Talk to clients about AI. They want to know you are on top of it. → Remember, your judgement is still your superpower. AI is a tool, not a replacement. The legal world is changing. AI literacy is not a nice-to-have. It is a must-have. The lawyers who learn, adapt, and stay curious will shape the future of law.

There's a persistent myth in legal tech: that you need to train AI models on your client data to get real value. Not true. Here's what progressive firms (like ours) are doing instead: 1. Leverage Foundation Models Modern LLMs like Claude Opus, GPT 5.2 already understand legal reasoning, contract structures, and statutory interpretation—no client data required. They're pre-trained on vast legal corpora and can handle sophisticated analysis out of the box. 2. Use Prompt Engineering & Context Instead of training, provide relevant context at inference time. Upload case law, relevant statutes, or firm templates directly into the conversation. You get customized outputs without compromising client confidentiality. 3. Build Workflows, Not Models The biggest gains come from workflow orchestration—routing documents, automating intake forms, generating first drafts, summarizing depositions. These process improvements don't require sophisticated model training. We are building both low code/no code and fairly sophisticated high code engineering workflows that combine software development approaches with LLM capabilities. 4. Create Knowledge Bases (Not Training Sets) Use retrieval-augmented generation (RAG) with anonymized precedents, public filings, and firm know-how. The AI retrieves relevant information without ever "learning" from confidential client matters. 5. Focus on Template & Pattern Reuse Standardize your best work—motion templates, due diligence checklists, research memos—and let AI help customize them for each matter. No client PII touches the model. Training custom models is expensive, time-consuming, and creates data governance nightmares. For 90% of use cases, firms see better ROI from smart implementation of existing models with proper guardrails. Your competitive advantage isn't in training AI. It's in deploying it thoughtfully across the workflows that matters. I do however believe that there is a world where law firms can build client specific Small Language Models (trained on their data and only used for them), but a lot of value capture from early stage GenAI adoption does not require this.

As lawyers, confidentiality isn’t just important—it’s foundational. Yet as we increasingly integrate generative AI and other tech tools into our practice, maintaining that confidentiality demands attention. Let’s walk through exactly what you need to know to keep client data secure, even as you harness powerful new technologies. 1️⃣ Know Where Your Data Goes Before using any AI-powered tool, clarify how your data is stored, who can access it, and whether it’s shared with third-party services. 2️⃣ Evaluate Data Security Practices Robust data security is non-negotiable. Always verify a vendor’s data security certifications, encryption standards, and access controls. 3️⃣ Limit and Control Your Data Inputs Use only the data you truly need to provide. The more data you input, the higher the confidentiality risk. 4️⃣ Use Built-in Privacy Controls Many reputable AI tools offer privacy modes or confidential environments that ensure your inputs won’t be used for model training or seen by unauthorized personnel. 5️⃣ Regularly Audit and Review Integrating technology is never a “set it and forget it” scenario. Regularly review and audit your chosen tools and their privacy compliance. - I’m Joe Regalia, a law professor and legal writing trainer. Follow me and tap the 🔔 so you won't miss any posts.

I was completely wrong about how Legal Professionals would adopt AI. The latest data show a significant advantage for smaller firms. Three months ago, I expected non-starters worried about compliance to seek strategic guidance first. Reality? Early adopters who've proven AI works are the ones seeking frameworks to scale systematically. In this article, I explain why early adopters require strategic assistance more than non-starters. And what practices are under 150 people doing to compete with firms ten times their size? The firms succeeding aren't treating AI as a secret efficiency tool. They're positioning it as evidence of innovation, thoroughness, and competitive capability. What small firms have that global firms don't: → Speed (decisions in days, not months) → Entrepreneurial culture (try, fail fast, pivot) → Client intimacy (tailored AI-enhanced services) → Agility (switch tools and workflows immediately) The latest data: → 96% of UK law firms use AI → Only 17% embedded strategically →30% of small firms actively exploring → Gap widening monthly Firms that started six months ago are now planning international expansion. Firms still debating the basics are falling further behind. Practical guidance for three adoption stages: If you haven't started yet: Start now, start small, start strategically. The gap is widening monthly. If you've experimented: This is where strategic mentoring creates exponential value. Move from "we use AI sometimes" to "we're an AI-enhanced practice." If you're scaling systematically, you're building advantages others will take years to match. Focus on governance that enables speed, not bureaucracy. What the next three months will bring: Such is the pace of change that we can only work in three-month windows now. The gap between early adopters and the cautious majority will become a chasm. Client expectations will shift from "nice to have" to "expected." Virtual office models will become mainstream for small practices. What surprised me most: The speed. The confidence. The entrepreneurial ambition. The bold questions, not cautious ones. If you recognise your practice in this—you've experimented, you've seen the potential, you're thinking entrepreneurially about scale and competitive advantage—you're ready for strategic frameworks, not basic AI training. And if you want to dive deeper: Join my webinar Shadow AI to Strategic AI 📅 Tuesday 18th November | 12:15 PM GMT 🎯 Limited to 30 Places Register: https://lnkd.in/eXpTGzMr #AIStrategy #ProfessionalServices #ShadowAI

🔍 New EDPS Guidance on AI Risk Management – A Must-Read for Legal and Tech Professionals On 11 November 2025, the European Data Protection Supervisor (EDPS) released a comprehensive Guidance for Risk Management of Artificial Intelligence Systems. This is a key resource for EU institutions and agencies (and, why not, controllers in general) developing, procuring, or deploying AI systems that process personal data. 📌 The guidance: • Provides an overview of the risk management methodology according to ISO 31000:2018. • Focuses on technical mitigation strategies for risks related to: • Fairness • Accuracy • Data minimisation • Security • Data subjects’ rights • Emphasizes the importance of interpretability and explainability as prerequisites for accountability and compliance. 💡 Particularly relevant for legal counsels, DPOs and IT engineers, the document offers: • A lifecycle-based checklist • Risk scenarios and countermeasures • Annexes with metrics and benchmarks ⚠️ While not a compliance manual, it provides a structured approach to identifying and mitigating risks that could impact fundamental rights. #AI #Privacy #RiskManagement #DataProtection #GDPR #EUDPR #EDPS #ArtificialIntelligence #LegalTech #AICompliance #ISO31000 #AIAct #PrivacyByDesign

🤔 What am I thinking about when everyone asks me about AI and Legal Ops? Spoiler alert: It’s not “How fast can we automate everything?” It’s actually this ⬇️ 💭 Are we designing AI so lawyers do less manual/administrative tasks… or just different admin? 💭 Are we using AI to amplify judgment or trying to replace it? 💭 Are we solving real workflow friction or just adding shinier tools? 💭 Are we finally centralizing and capturing data in a way people actually trust and use? Because here’s the thing I keep seeing 👀 in Legal Ops: AI doesn’t fail because the technology isn’t powerful. It fails when we skip the people, process, and data part and hope the tool will save us. The real unlock 🔐 is human-centered, data-driven AI: ✔️ Automate the busy work ✔️ Standardize what should be standard ✔️ Centralize and capture data once so it can be reused everywhere ✔️ Build trust in the data before asking people to act on it ✔️ Free people up to operate in their zone of genius So yes, I’m thinking about #AI. But I’m thinking even more about how legal leaders lead change, create clarity, and bring their teams along. Because the future of #LegalOps isn’t AI-first.It’s human-first, data-smart, and AI-enabled. Now… back to my noodling. 😉 #AIinLegal #HumanCenteredAI #DataDriven #LegalTech #TransformationLeadership #Mitratech #FutureOfWork #ChangeManagement #GC