Why trust varies in legal tech projects

🚨 “Why Legal Teams Are Pumping the Brakes on AI Adoption – And What Consultants Can Do About It" 🚨 As a consultant working at the intersection of tech and law, I’ve seen firsthand the glaring gap between the promise of AI solutions (including generative AI) and the cautious reality of in-house legal teams. While AI could revolutionize contract review, compliance, and risk management, many legal departments remain skeptical—and their hesitations are far from irrational. Here’s what’s holding them back: 1. "We Can’t Afford a Hallucination Lawsuit" Legal teams live in a world where accuracy is non-negotiable. One AI-generated error (like the fake citations in the Mata v. Avianca case) could mean sanctions, reputational ruin, or regulatory blowback. Until AI tools consistently deliver flawless outputs, “trust but verify” will remain their mantra. 2. "Our Data Isn’t Just Sensitive – It’s Existential" Confidentiality is the lifeblood of legal work. The fear of leaks (remember Samsung’s ChatGPT code breach?) or adversarial hacks makes teams wary of inputting case strategies or client data into AI systems—even “secure” ones. 3. "Bias + Autonomy = Liability Nightmares" Legal ethics demand fairness, but AI’s hidden biases (e.g., flawed sentencing algorithms) and the “black box” nature of agentic AI clash with transparency requirements. As one GC mentioned recently: “How do I explain to a judge that an AI I can’t audit made the call?” 4. "Regulators Are Watching… and We’re in the Crosshairs" With the EU AI Act classifying legal AI as high-risk and global frameworks evolving daily, legal teams fear adopting tools that could become non-compliant overnight. Bridging the Trust Gap: A Consultant’s Playbook To move the needle, consultants must: ✅ Start small: Pilot AI on low-stakes tasks (NDA drafting, doc review) to prove reliability without existential risk. ✅ Demystify the tech: Offer bias audits, explainability frameworks, and clear liability protocols. ✅ Partner, don’t push: Co-design solutions with legal teams—they know their pain points better than anyone. The future isn’t about replacing lawyers with bots; it’s about augmenting human expertise with AI precision. But until we address these fears head-on, adoption will lag behind potential. Thoughts? How are you navigating the AI-legal trust gap?👇 #LegalTech #AIEthics #FutureOfLaw #LegalInnovation #cmclegalstrategies

Legal Tech isn’t just another software play. And if you treat it like one, you’re setting yourself up to fail. Many smart founders from other fields—fintech, SaaS—try to break into legal. But time and again, they fail. Why? Because legal doesn’t behave like other industries. It’s slower. It’s more defensive. It’s wired around risk, not growth. Here’s what you’re really up against: 1/ You are selling to risk managers, not just users. Lawyers, GCs, and compliance teams care about minimizing downside, not chasing upside. 2/ Trust is everything. One bad redline, one blown call, one data breach, and you’re done. Legal buyers have long memories. 3/ Adoption is brutal. Lawyers are under pressure, billing by the hour, and resistant to change. If your product doesn’t fit their workflow exactly, it will be ignored. So how do you build something that lasts? Think like a legal insider. Understand the stakes. Build for dependability, not disruption. Focus on real problems — the kind that cost firms money, time, or clients — not just minor conveniences. And prove everything. Hype doesn’t move legal teams. Results do. Legal Tech isn’t about moving fast and breaking things. It’s about moving carefully and getting it right. The ones who win aren’t the loudest. They’re the most trusted.

Sunday morning thought: One of the more common responses to my viral AI posts has been "AI will get better. Give it time."   I’m not only hearing this from legal tech founders and investors, I’m also hearing it from some lawyers. The assumption is that accuracy improvements will eventually solve the AI trust problem.   But here's what that logic misses: The issue isn't just technological. It's structural.   Even if AI contract review reaches 99.9% accuracy, lawyers still face a binary professional responsibility problem. When you put your name on work, you're either right or you're wrong. There's no partial credit for "the AI was 99.9% accurate."   A medical analogy: A surgeon doesn't get to tell a patient's family "the AI diagnostic tool was 98% accurate, so you can't really blame me for the wrong diagnosis." Professional liability doesn't work on probability curves.   But there’s an even deeper problem: the AI decision-making process remains woefully opaque. Even perfect accuracy doesn't solve the fundamental question: "How do I explain to the state bar why I trusted a system I can't fully understand?"   I've been practicing law for 20+ years. The lawyers who sleep well at night aren't the ones using the most sophisticated tools. They're the ones who understand exactly how they reached every conclusion in their work.   "AI will get better" assumes lawyers are just waiting for higher accuracy rates. But what we really need is transparency in the decision-making process that lets us take full responsibility for the output.   Until AI can explain its reasoning in terms lawyers can verify and defend, the trust gap will persist regardless of accuracy improvements.   What matters more: tools that work 99% of the time, or tools we can defend 100% of the time? #LegalTech #AI #ProfessionalResponsibility  

I learned that the in-house legal team of a legal tech company doesn't trust their own AI software enough to use it themselves. During an interview, I chatted with an engineer who built AI contract review capabilities and an embedded Word add-in. These features complement their existing CLM, so I assumed their in-house legal team would be an ideal audience. This felt like a reasonable assumption, given how much marketing focus has been placed on AI capabilities over the past two years. Yet their legal team's usage was next to none. Not because the technology isn't valuable, but because building AI that legal teams actually trust is incredibly nuanced. Here's the truth: Creating AI for specialized legal workflows isn't just about having the technical capability. It's about deeply understanding how lawyers work, building features they can verify and trust, and earning their confidence through transparency and reliability. Simply being a large incumbent and "adding AI" doesn't automatically translate to user adoption. Trust has to be earned through purposeful design, workflow integration, and a deep appreciation for how lawyers and other business users actually work. At Docsum, this reality drives everything we build. We know that AI in legal tech isn't just a feature checkbox - it's a commitment to building solutions that lawyers will actually trust and use over time.

DocuSign is worth $11B.  ROSS AI shut down in bankruptcy. Both were legal tech companies with talented founders and strong VC backing. The difference? After guiding multiple legal tech transformations and speaking to dozens of law firms and legal departments, I've identified 5 non-negotiables for success: 1. Segment Relentlessly "Lawyers" aren't a monolith. A family law practice has entirely different needs from a multinational GC. Deep segmentation and tailored solutions win every time. 2. Solve One Pain Point First DocuSign didn't start with contract analytics—it started with signatures. Master one problem exceptionally well before expanding. 3. Respect the Culture Lawyers value precedent, confidentiality, and control. They resist the "move fast and break things" mentality for good reason. Tech that ignores these cultural values will fail, regardless of innovation. 4. Iterate with Purpose Clio balanced agility with compliance and ethical obligations. In legal tech, precision matters as much as speed. 5. Trust Is the Currency Features and pricing matter, but trust matters more. Lawyers don't adopt tools - they adopt partners they can trust. Build credibility through transparency, reliability, and legal literacy. The legal tech companies that succeed do so by solving real problems, earning credibility, and honoring the values embedded in legal practice. If you're a legal tech vendor - listen to feedback, enhance your product based on real workflows, and respect the profession's norms. You will emerge stronger. I'm curious to hear from you: Which of these five success factors do you think is most overlooked in legal tech today?

I found this compelling thread on Reddit that really resonated with me. If you want lawyers to use your AI product, confidentiality cannot be a side note. It has to be the foundation. A law firm manager shared their experience digging into legal AI tools. What they found was troubling: vague promises, buried disclaimers, and a lack of real security certifications. In legal practice, especially, this is not just a trust issue. It is an ethical one. ✅ Want lawyers to adopt your tool? You must show:  • You are not training on their data  • Data is encrypted at rest and in transit  • Ownership of data remains with the lawyer  • Data location and handling are clearly defined  • All of this is backed by actual audits and certifications My take? Legal AI startups often underestimate how deeply legal professionals scrutinize privacy. Flashy features are fine, but if your terms of service contradict your trust page, you have already lost the room. We are currently building a free data anonymization tool for lawyers. It is a small but necessary step to help legal teams experiment safely with AI. It will not solve everything, but it will reduce friction and build trust from the first interaction. If you are building for this space, ask yourself. Are you designing with real legal constraints in mind, or are you hoping for forgiveness later? #LegalTech #AI #Confidentiality #LegalEthics #Cybersecurity #Startups

Can Lawyers Truly Trust Artificial Intelligence (AI) in Their day-to-day Practice? AI is moving into legal practice, from the local firm to the global law arena—but trust? That's a tougher sell. Whether drafting contracts under national law or battling multinational treaties, AI generates questions that keep lawyers awake at night. Here's where the tension arises: 1. Accuracy Under Scrutiny: AI is faster at reading treaties or case law than humans. However, suppose it misreads a convoluted clause of the UN Charter or a fine point of any court precedent? Speed is great—until it is wrong. 2. Jurisdictional Confusion: International law is a maze of conflicting rules. A mechanism in AI might watch over deadlines or warn of clashes in treaties, but can it truly grasp the political subtleties of an international dispute—or the quirks of civil as opposed to common law? 3. Ethical Gray Zones: Applying AI to risk assessment or compliance is genius, but whose fault is it if it doesn't pick up on a material regulation? The lawyer, the firm, or the algorithm? Trust is lost when responsibility is unsure. 4. The Human Disconnect: AI can create data, but can it replace the gut sense that seals a negotiation or wins over a judge? Heavy reliance might dull the skills that define legal practice—nationally or internationally. To counter risks to precision, supplement AI tools with human discretion, cross-checking outputs with sources. Ethically, establish strict accountability—utilize AI as a tool, not a decision-maker, and record its utilization to avoid liability. To keep the human touch, use AI for grunt work (research, timelines) but reserve strategy and advocacy in your hands. #LegalTech #ArtificialIntelligence #InternationalLaw #LawPractice #Ethics #CIFILE #sustainabledevelopment #internationalenvironmentallaw #climatechange #environmentallaw #governance #justice #Immigration #UNEP #cifilejournalofinternationallawcjil #Canada #lawyers #law #abbaspoorhashemi #lawyer #immigrationcanada #ICC #education #business #networking #francophonie #UNDP #UN #innovation #opportunities #people

This is the fourth post in my series on explainability and trust in legal AI. So far, we’ve talked about tracing outputs, checking token flow, and moving past fragile benchmarks. Now we move on to something harder to measure: instinct. If you want a model to behave like someone who’s done the work, you need to show it more than final drafts. The real decisions in legal practice often happen outside the document. Voice notes that say, "They tried this last time." Late-night comments about wording that’s fine on paper but stalls the deal. Messages where something small gets added, then disappears quietly the next day. These moments don’t usually make it into training sets. They’re not edge cases, they’re how legal thinking works in the real world. Most legal AI tools are trained on curated, formatted, and often sanitised examples and so it’s no wonder they sound like junior lawyers trying to impress a partner. Confident, clean, but lacking in context and instinct. Tracing helps us spot that gap, if the model hits the right clause but never activated on the term that would’ve raised a red flag in practice, you’ve got a ghost of competence, not real understanding. You can’t simulate instinct if all the model sees is published precedent. If we’re serious about behaviour-aligned AI, we need to ask harder questions: - Did it respond because the clause mattered, or just because the word "risk" appeared nearby? - Did it recognise the strategic weight of a client instruction buried in a side note? - Is it following the flow of the deal? Now these are subtle signals, but they’re everywhere in legal work, and if tracing shows the model is blind to them, that’s not a fault, it’s really useful feedback. Next, I’ll look at how this kind of tracing helps when lawyers and models disagree, and why token-level evidence might be the thing that brings better collaboration back to the table. #legaltech #AI #trainingdata #behaviouralAI #explainability

Will lawyers ever be able to trust AI enough to use it effectively? Really pleased to be featured in The Lawyer discussing with Lucie Cruz how UnlikelyAI is addressing one of the biggest barriers to AI adoption in legal: the hallucination problem. Law firms today are caught between the pressure to innovate and the reality that most AI systems can't explain their reasoning, which is a non-starter for a profession built on accountability. "Nobody wants to be the test case" - I think perfectly captures where the legal industry stands with AI today. At UnlikelyAI, we're taking a different approach by combining neural networks with symbolic reasoning to create systems that can either give you a traceable answer or admit when they don't know. For highly regulated industries, having that auditable trail isn't just nice to have, it's essential for building the trust needed to deploy AI in sensitive tasks. The goal isn't to replace human expertise, but to free lawyers from repetitive work so they can focus on the high-value strategic thinking that clients really need. Exciting to see growing recognition that the legal industry's cautious approach to AI isn't a weakness, it's exactly the kind of rigorous thinking we need to build AI systems worthy of professional trust. I really loved this conversation!! Read it via the link in the comments. #LegalTech #AI #LegalInnovation #FutureOfLaw