Lexemo’s Post

⚖️ AI in Legal: From Experiment to Essential A recent Gartner report shows that nearly 60% of legal leaders now rank generative AI as a top priority. The conversation has shifted from “Should we use AI?” to “Which tools should we scale, and how fast?” But many legal tech tools are missing the mark: ❌ Solving the wrong problem (surface-level Q&A vs real workflow relief) ❌ Living in silos instead of integrating with Salesforce, Microsoft 365, etc. ❌ Lacking legal nuance, accuracy, and governance The key is to focus on outcomes, not features. Ask: 🚩Does this move us closer to our business goals? 🚩Does it integrate where we already work? 🚩Is it accurate and secure enough for legal? The next phase of AI in legal isn’t about whether you use it — it’s about how well you align it to your outcomes. Firms that cut through the hype and pick tools that truly speed contracts, reduce low-value work, and integrate seamlessly will unlock AI’s real potential. Read more at: https://lnkd.in/gUkC5435

Legal AI isn’t failing because of hallucinations. It’s failing because firms have no infrastructure discipline. Everyone blames the model.  “GPT hallucinates.” “AI isn’t accurate enough.” That’s the comfortable story. But here’s the uncomfortable truth: 𝗹𝗲𝗴𝗮𝗹 𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗯𝗿𝗲𝗮𝗸 𝗮𝘁 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗹𝗮𝘆𝗲𝗿. 𝗜𝘁 𝗯𝗿𝗲𝗮𝗸𝘀 𝗮𝘁 𝘁𝗵𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗹𝗮𝘆𝗲𝗿. The Real Bottlenecks -  • 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗹𝗲𝗴𝗮𝗰𝘆 𝗱𝗮𝘁𝗮 → 80% of “contract repositories” are ZIP folders of PDFs. Without normalization (OCR, metadata extraction, clause segmentation), retrieval is noise.  • 𝗡𝗼 𝗱𝗼𝗺𝗮𝗶𝗻 𝗼𝗻𝘁𝗼𝗹𝗼𝗴𝘆 → You cannot scale issue-spotting without labeled clause libraries, taxonomies, and risk ontologies. Firms that skip this step aren’t “AI-enabled.” They’re automating chaos.  • 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗯𝘁 → If outputs don’t flow into CLM, ERP, matter management, or billing, you’re not augmenting workflows — you’re generating trivia in a sandbox.  • 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗶𝗰𝘁𝗶𝗼𝗻 → Immutable logs, citation packs, PII redaction, and counterfactual testing aren’t optional. Without them, “AI compliance” is theater. Why This Matters - Models will keep getting better, that’s a commodity race. The real compounding edge is 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲:  • Every normalized contract tightens retrieval precision.   • Every labeled clause improves downstream scoring.   • Every integration reduces adoption friction.   • Every audit pipeline builds defensibility. This is why infrastructure is not boring. It’s the 𝗳𝗹𝘆𝘄𝗵𝗲𝗲𝗹 that turns legal data from a liability into an asset. The firms shouting loudest about “AI adoption” are running proof-of-concept toys on garbage data. It looks good in press releases.  It dies in production. The real winners will look invisible.  They’ll be the ones investing in ontologies, metadata, dual-review pipelines, and system pipes.  They’ll look slow, boring, maybe even wasteful. But five years out, those “boring” firms will own the cleanest corpora, the strongest retrieval layers, and the only defensible data moats. AI doesn’t reward flash. It rewards 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲. Ekamm8

In today’s legal landscape, AI agents are rapidly taking on complex tasks from contract negotiation to compliance delivering significant efficiencies (Salesforce reports about 9,500 hours saved annually) while still requiring human oversight to guard against errors and hallucinations. Though AI may outperform in certain scenarios, the current low adoption rate less than 1% among large global companies speaks to the industry’s cautious approach and the enduring need for legal judgment and accountability. Read more on how hybrid models of AI and human collaboration are shaping the future of legal work. https://lnkd.in/ebHGRWqR #LegalTech #AIinLaw #Innovation #HybridWorkflows #FutureofLaw

When a law firm hires a new associate, trust doesn’t come overnight. You don’t hand them a client matter on day one and assume perfection. You build trust as they demonstrate accuracy, consistency, discretion, good judgment - and the courage to admit what they don’t know. Why should law firms treat AI different? Law firms face 5️⃣ big challenges with adopting AI today: ⚖️ Accuracy: models that sound right but aren’t. 🔒 Confidentiality: sensitive data sent into opaque clouds. 📑 Consistency: outputs that change from run to run. 🕹️ Control: tools lawyers can’t shape or fine-tune. 👀 Visibility: black boxes that can’t be audited. In my own work designing custom legal AI workflows and processes, I’ve seen these challenges frustrate legal professionals and derail adoption efforts. So how do we try and address this? Running open-source Small Language Models (SLMs) in agentic workflows has been revealing - they’re cheaper, faster, and most importantly predictable and controllable. I can fine-tune them quickly for their specific task, deploy them locally, and know exactly how client data is being handled. Recent research from NVIDIA backs this up: SLMs are often better suited to the repetitive, scoped tasks of agentic systems than massive general-purpose LLMs. (🔗 in comments) But SLMs are only half the puzzle. The other half is evaluation. A new paper from OpenAI and Georgia Tech shows why hallucinations persist: almost every model in use today has been pre-trained to guess rather than say “I don’t know.” Current benchmarks literally reward confident bluffing, which makes hallucinations inevitable. (🔗 in comments) The breakthrough for my workflows comes from putting the two together: • SLMs deliver control, confidentiality, and efficiency. • Better evaluation methods deliver accuracy, consistency, and trust. Together, they form the foundation for each custom Legal AI tool I am building - tools designed for the way lawyers actually work, and built on the principle that “I don’t know” is better than a confident fabrication. Just like a new associate, AI has to earn our trust over time before we should be willing to give it access to our most important and confidential information. I’m curious, 👀 for those already testing AI in practice, which of these five challenges has been the toughest to overcome? If you or your firm is ready to explore smaller, custom, modular, and honest AI systems, let’s connect! #SLMs #LegalAI #OpenSource

📬 Your inbox these days is probably crowded with: “Have you seen our AI contract analyzer?” “We’re the #1 legal tech tool!” “Got 15 minutes for a quick demo?” The truth is, as the legal tech market explodes, legal teams are being bombarded with shiny tools that all promise faster reviews, smarter compliance, and lighter workloads (of course ours really does do all that). When there are so many options, how do you evaluate and select the right vendor? That’s why we built the IMPACT Framework, to help you cut through the noise when you are ready to buy. Think of it as your due diligence checklist for legal AI. Less hype. More clarity. So the next time your inbox feels like Times Square at midnight, remember: 🌟 IMPACT helps you figure out when to stop scrolling… and finally swipe right on a vendor. 👉 Bonus: we even made an IMPACT RFP spreadsheet to help you get started. 📬 Your inbox these days is probably crowded with: “Have you seen our AI contract analyzer?” “We’re the #1 legal tech tool!” “Got 15 minutes for a quick demo?” The truth is, as the legal tech market explodes, legal teams are being bombarded with shiny tools that all promise faster reviews, smarter compliance, and lighter workloads (of course ours really does do all that). When there are so many options, how do you evaluate and select the right vendor? That’s why we built the IMPACT Framework, to help you cut through the noise when you are ready to buy. Think of it as your due diligence checklist for legal AI. Less hype. More clarity. So the next time your inbox feels like Times Square at midnight, remember: 🌟 IMPACT helps you figure out when to stop scrolling… and finally swipe right on a vendor. 👉 Bonus: we even made an IMPACT RFP spreadsheet to help you get started. https://lnkd.in/etpGSQ-X #LegalTech #AI #legalops #vendorselection

The hidden value of legal tech and AI isn’t in the manual. The real breakthroughs come when in-house teams push past “intended use” to find their own adaptive applications that multiply capacity in ways designers never imagined. More in the second article in the eight-part "GC x AI" series in Bloomberg Law linked here: https://lnkd.in/e6bjYbvv

Legal teams are becoming technology scouts, finding new applications for existing tools across business functions instead of waiting for custom solutions. This curiosity-driven approach is a measurable business advantage.

GCs and legal departments are getting a lot of pressure to think strategically about the enterprise, rather than being pigeon-holed as a risk-managing cost center. And AI promises to free up time for in-house lawyers to do this, right? But AI can do more to raise Legal's profile beyond freeing up some time. Cox Media Group's GC Eric Dodson Greenberg writes that the AI tools you're implementing may have other "off-label" uses that can make legal a better collaborator with other business units. Eric shares some examples of how his team have achieved this in Part Two of his Eight-Part GCxAI series with Bloomberg Law. Link below is outside paywall

CIOs: the legal AI chasm is coming into view The LexisNexis–Harvey partnership isn’t just a product release. It’s a signal. LexisNexis is embedding its core legal content and Shepard’s citations directly into Harvey’s AI platform. Not via plugin. Not as a bolt-on. As infrastructure. This is what agentic legal AI looks like when enterprise-grade trust meets emergent reasoning. #SeanFitzpatrick, CEO of LexisNexis, says this wasn’t dreamed up in a strategy workshop. It was driven by client demand. Large law firms wanted integration, verifiability and real-time legal research inside their AI tooling — and they wanted it now. What matters for CIOs: * It reduces the hallucination risk with tethered source data * It builds regulatory defensibility into the AI workflow * It compresses the ROI window: productivity gains are already being reported * And it sets a new integration bar that many other tools may now fail to reach This isn’t yet the full paradigm shift. But it is the sort that happens beforehand – the sorting of serious players from the rest. These are stepping stones across a river that’s becoming a chasm. Each one is further apart. If your firm hasn’t started the journey, soon there won’t be a safe way across. Harvey has $300m in fresh funding, a $5bn valuation, and now RELX backing. It’s not a startup knocking at the door – it’s already in the room. CIOs who treat this as optional risk being left behind. Full interview with #BobAmbrogi here: 🔗 LexisNexis & Harvey reshape legal AI I'm preparing a detailed briefing note to help CIOs identify the winning software. if you want a copy, let me know.

Not all legal AI is created equal. For in-house teams, it falls into 4 categories with each delivering very different outcomes. Here’s the breakdown from my research into how in-house legal teams are experimenting with GenAI: ⚙️ 1. General productivity AI Purpose: Workplace-wide efficiency boosters, already embedded into familiar tools. Tools: Microsoft Copilot, Google Gemini, ChatGPT, Slack GPT, Notion AI. Outcomes: Faster drafting of emails and board papers, meeting summaries auto-generated in Teams, Excel analysis done in minutes instead of hours. Low barrier to entry → “easy wins” that free up time immediately. 📑 2. Use-case specific legal AI Purpose: Point solutions designed for legal workflows like contracts, discovery, or practice management. Tools: Spellbook, RobinAI, LawGeex, Luminance (contracts); Ironclad, Evisort, ContractPodAI (CLM); RelativityOne, Reveal, DISCO (discovery); Clio, LEAP (practice management). Outcomes: Contract reviews cut from hours to minutes, with risk clauses flagged automatically. Intake, billing, and workflow tasks streamlined so lawyers spend less time on admin. ⚖️ 3. Multi-purpose legal AI Purpose: AI “legal assistants” trained on broad legal datasets, flexible across many tasks. Tools: Harvey.ai, Lexis+ AI, Thomson Reuters CoCounsel, vLex Vincent, Westlaw Precision. Outcomes: Drafting compliance memos, running legal research, or generating first-pass advice notes. These tools act like a “legal co-pilot,” giving lawyers more leverage, but with humans always doing the final check. 📊 4. Precision Analytics & Automation Purpose: Advanced AI for analytics, insights, and high-stakes drafting. Tools: LexisNexis, Thomson Reuters, Harvey.ai, vLex Vincent. Outcomes: Litigation teams predicting case outcomes with data-driven insights. Benchmarking risk exposure across large contract portfolios. Automated but auditable drafting of documents at scale. These tools demand more governance, but the payoff is sharper strategy and better decision support. Bottom line: General productivity AI is the low-hanging fruit. But the real game-changer comes when AI is embedded directly into legal workflows, no matter which category of AI it falls into. Turning administrative tasks and repetitive work such as contract reviews from bottlenecks into value drivers. 👉 Which of these categories feels most relevant for your legal team right now?