Assessing Copilot Adoption in Law Firms

I've just asked a Top 100 Law Firm: "What's slowing your team using AI?" This is what they said + what I'm hearing from MULTIPLE firms: 1) Partners haven't activated their Copilot licences, yet they're the ones signing off the AI strategy. 2) Partners put AI in the IT budget when it needs attention from hiring and L&D spend. 3) Firms skip the Educate stage entirely and jump straight to buying or building something nobody's ready to use. 4) Innovation committees drag AI decisions through months of approval cycles while competitors ship. 5) Copilot rollouts happen before anyone has been trained to use it, so hundreds of licences sit dormant. 6) A lot of AI training teaches definitions and vocabulary but not practical workflows they can use on their files the next day. 7) Lawyers say they haven't had time to 'play with AI'. Firms believe the billable hour is disincentivising adoption. 8) Lawyers can't prompt effectively or evaluate AI output critically, so they can't tell good AI work from bad. 9) Lawyers know exactly where their work hurts, but they can't translate that pain into "this is what AI should do about it." 10) Every AI task is being given to Copilot with varying results. They would get better results by changing workflow / using a different tool. 11) Lawyers are rebuilding workflows in Excel instead of using tech the firm pays for 12) Confidentiality, liability and regulatory exposure make caution rational, but nobody's trained lawyers on how to de-risk AI for client work. 13) Getting lawyers to change how they work, and partners to lead from the front on something they've never used themselves, is harder than any of the tech. It's tough, but I'm seeing a lot of firms working through this. What have I missed?

After 18 months researching 20+ companies and interviewing dozens of law firms, Northzone concluded that Legal AI has reached a strategic inflection point. It's a must read for #legaltech founders. Here's what the study found: 1. Market Evolution Through Four Phases - Point Tool Era: Rules-based tools like Litera, Kira, iManage that were useful but rarely transformative - GenAI Spark: Broad AI copilots offering general capabilities (summarize, redline, draft) - Vertical Recalibration: Companies like DraftWise and Spellbook focusing on specific use cases - Trial and Fragmentation: Major firms now running 2-5 AI tools simultaneously in parallel pilots Their Investment Thesis - Winner will: 1. Workflow Depth Over Task Coverage: Own complete processes, not individual tasks. Capture end-to-end legal workflows like full M&A cycles rather than point solutions. 2. Native Environment Integration: Build where lawyers work: Word, Outlook, SharePoint. Focus on minimizing context switching and adoption friction. 3. Fine-Tuning on Proprietary Firm Data: Leverage firm-specific datasets for competitive advantage through clean RAG pipelines and retrieval tuning. 4. Building Trust Through Lawyer-Centric Design: Make tools feel built by lawyers, for lawyers. Focus on credibility and professional acceptance. 5. Balanced Positioning: Think platform but enter through deep vertical use cases. Avoid being too broad or too narrow. Here's what I think Northzone might be missing: 1. Legacy Integration Is Short-Term Thinking: I-native structures won't be constrained by Word/Outlook. Startup legal departments already live in Google Workspace/Jira ecosystems. (see Macro) 2. Data Access Requires Strategic Partnerships: Proprietary data and relationships are traditional firms' main defensive moats. Access requires strategic alliances, not just technical capability. 3. Partnership Structure Prevents Real Adoption: Law firms are confederations where every partner has veto power. AI needs standardized processes that partnership structures make impossible. 4. Efficiency Destroys Revenue Model: AI compresses 30-60% of billable work by 50-90%. Successful adoption means revenue destruction for firms billing on automatable tasks. 5. Replacement Beats Optimization: AI-native models like Crosby and Garfield AI use per-document pricing and deal velocity metrics. Different economics, not better tools for existing economics.

What I’ve learned from teaching lawyers how to use AI. For over two years, I’ve been teaching legal teams how to use AI. AI adoption isn’t like past legal tech waves. Lawyers are more engaged, excited, and optimistic about AI than past legal tech solutions. Here are nine trends I'm seeing in AI adoption in legal teams: 1️⃣ Early adopters are driving change. Lawyers that already use AI in their daily lives are advocating for AI use, teaching and pushing their legal teams forward. 2️⃣ Hesitant lawyers tend fall into two camps. (1) Skeptics (rightly questioning the results) and (2) Cautious users (worried about how data is used, and/or inputting confidential information or personal data). 3️⃣ Most teams recognize they need training to use AI effectively. Adoption happens when lawyers find their own use case(s). That requires access to tools, training, and freedom to experiment. Until then, AI remains a novelty. 4️⃣ Keeping up is hard. Everyone feels the intensity of the pace of change. Even Ethan Mollick and Allie K. Miller acknowledge it's hard to keep up. Although I've been impressed with Kyle Bahr's articles and posts! 5️⃣ AI champions are emerging. More legal teams are designating AI champions, lawyers, legal ops pros, legal engineers, governance leads, or internal AI advocates to drive adoption within their teams and also across the company. You have a unique opportunity to become an AI expert and make an impact across entire organizations. (For example, I taught a CTO how to improve the instructions for a company GPT!) 6️⃣ Broad-purpose AI tools are hitting limitations. Legal teams who started with in-house OpenAI ChatGPT solutions and similar tools, like Copilot, are running into walls. They are beginning to see they need legal-specific AI solutions. One major challenge is articulating this need to their organization to justify additional budget for legal specific tools. 7️⃣ Understanding AI is a tool, not magic. More legal professionals now understand that AI won’t replace them. It’s here to make their work more efficient, not take over entirely. 8️⃣ Integration is the key to long-term adoption. The legal teams making the most progress are the ones experimenting and exploring how they can embed AI into daily workflows. These teams are moving beyond prompting, and building assistants and embedding AI tools into workflows. 9️⃣ Adoption isn’t fast. Discovering how AI can work for you and actually building solutions are two different exercises. Both require investment to see real returns. I'd love to know whether you are seeing the same trends? Or have you experienced some of these observations play out?

Harvey just put its legal AI inside Microsoft Copilot and Copilot Cowork. The product isn't the story. Distribution is. Specialised legal intelligence now lives where lawyers already work, in Word, Outlook and Teams, instead of in a separate platform they have to remember to open. That kills the biggest barrier to adoption, which is friction, and it should worry every General Counsel more than it excites them. Now the counter-intuitive part. 💡 A standalone legal AI tool is visible and controllable precisely because it's separate, since every use leaves a footprint. Embed that same capability across everyday workplace software and AI-assisted work becomes untraceable. A lawyer can move from an email, to a contract, to legal analysis, to a finished draft without ever crossing a system boundary you can audit. The governance controls most firms have built assume a discrete tool. That assumption just expired. The vendor competition is shifting too, away from the best legal platform and towards whoever becomes the intelligence layer inside the platforms lawyers already use. That makes the procurement question obsolete. The one that matters now is how much of the legal workflow you're prepared to let AI enter, connect and eventually execute, and whether privilege survives when several agents share access to the same matter. Integration drives adoption. Governance decides whether it lasts.

Shoosmiths just raised the bar on AI in law. No pilot schemes. No cautious memos. They put real money behind it. Hit one million uses of Microsoft Copilot and they’d add £1m to the firmwide bonus pool. The target was smashed four months early. What matters here isn’t the tech, it’s the intent. Copilot isn’t replacing legal judgement. It’s cutting out the admin drag. Emails, summaries, meetings, early research. The time-wasters. The result is lawyers spending more time on clients and less on noise. While some firms are tightening controls or quietly pulling access back, Shoosmiths went the other way. Trust your people. Incentivise the behaviour. Share the upside. One approach says use it and benefit. The other says be careful and slow down. Only one of those feels like the future.

💡 Microsoft Copilot adoption isn’t failing because of technology… it’s failing because of how we’re rolling it out. Organizations are investing heavily in AI—hundreds of thousands of dollars in licenses—yet real usage tells a different story. 👉 ~35% adoption 👉 64% of users never even open it That’s not a product problem. That’s an adoption gap. From what I’ve seen, the pattern is clear: - We buy licenses before building capability - We expect results without structured training - We ignore prompt engineering as a core skill - We leave security questions unanswered - We don’t define clear use cases from leadership And the biggest one? 👉 We assume AI will “just work” like any other tool. It won’t. AI—especially tools like Microsoft Copilot—needs: ✔ Context ✔ Guidance ✔ Practice ✔ Leadership alignment Without that, even the best technology sits idle. What’s coming next makes this even more critical… We’re moving from simple copilots → to multi-agent workflows. And companies that haven’t built foundational skills today will struggle to keep up tomorrow. 🚨 The real cost isn’t the license. It’s the underutilization compounding over time. The organizations winning with AI aren’t the ones spending the most… They’re the ones investing in their people to actually use it. Curious—what’s been the biggest blocker to Copilot adoption in your org?

AI in law firms without governance is like letting interns file pleadings unsupervised. Everyone’s excited about speed. Fewer people are thinking about risk, privilege, supervision, and client trust. Here’s my take: AI governance isn’t a policy. It’s a framework for safe, repeatable execution. If your firm is using ChatGPT, Clio AI, CoCounsel/Vincent, Grammarly, transcription tools, or “helpful” browser copilots - you already have AI in the building. The only question is whether it’s controlled. What I believe every law-firm AI governance framework should include: 1) A short list of authorized tools (and banned tools) -If it’s not approved, it’s not used. -Firm-managed accounts only (no personal logins), MFA enforced, privacy settings locked down. 2) Data classification rules that normal humans can follow -What can be pasted into AI as-is -What must be redacted/anonymized -What must never leave your environment (even to “secure” tools) 3) Human-in-the-loop isn’t optional -AI can draft, summarize, outline, suggest. -A lawyer must verify sources, edit substance, and approve anything external (clients, courts, opposing counsel). 4) Verification gates for hallucinations -No citation gets filed unless a human pulled the case and read it. -Treat AI output like a junior assistant: helpful, fast… and frequently wrong without supervision. 5) Audit logs + incident tracking -If an AI tool caused a near-miss, log it. -If someone pasted sensitive content somewhere they shouldn’t, log it and respond like a security incident. 6) A real owner (not a PDF on SharePoint) -Someone must be accountable for tool approvals, training, monitoring, vendor reviews, and policy updates. The following should also be addressed within the framework: -A prompt + template library (approved clause prompts, memo outlines, email drafts) -A tool intake checklist (vendor terms, data retention, training usage, breach history, DPAs) -Role-based access + DLP controls (limit who can use which tools + prevent copy/paste exfiltration) -Billing transparency guidance (how AI efficiency impacts billing narratives and client expectations) -Quarterly tabletop exercises (simulate: AI fabricated a citation / staff pasted client data into a public tool) -Metrics that leadership cares about (time saved, error rates, incident rates, adoption by practice area) If you’re a managing partner or firm administrator, here’s the simplest litmus test: Could you explain, today, what tools your people are using, what data they’re putting into them, and how you’d prove it if there was a dispute? If not, governance isn’t overhead. It’s insurance. #AIGovernance #LegalTech #LawFirmManagement #LegalInnovation #VerumAnalytica

I've spent the last year watching how law firms adopt AI. Some are getting it right. Most aren't. The gap between the two groups is about to get much wider. I put together 10 predictions for what changes in 2026. Here are the ones I'm most confident about: Voice takes over. You speak 4x faster than you type. Microsoft just rolled out voice across Copilot. OpenAI and Google are racing to catch up. The keyboard becomes optional for many tasks. Copilot loses its grip. One platform can't do everything well. Smart firms will build AI portfolios instead—Claude for nuanced writing, GPT for research, Gemini for deep analysis, specialized legal AI for document review. AI literacy becomes non-negotiable. Thomson Reuters research shows law firm AI adoption nearly doubled this year. But adoption without training is just expensive experimentation. The shift from "asking AI questions" to "working collaboratively with AI" separates the leaders from the laggards. AI-native firms emerge. Partners are leaving BigLaw to build lean, tech-first practices that compete on sophisticated work—at a fraction of the cost structure. Mid-size firms punch above their weight. A 50-lawyer firm with strong AI integration can now produce what once required 200 lawyers. The resource gap is shrinking fast. The billable hour cracks. When due diligence that took 40 hours now takes 4, hourly billing stops making sense. Expect more experimentation with value-based pricing. Clients start asking questions. "How are you using AI on our matters?" moves from occasional inquiry to standard RFP requirement. The through-line: AI is maturing from novelty into infrastructure. 2026 is the year that experimentation turns into advantage—or regret. Full breakdown in the article. Curious which predictions resonate, or which ones you think I got wrong. #lawfirmstrategy #artificialintelligence #legalAI

Last month, I spoke to a law firm who spent thousands on AI tools but only used 30% of the tools they pay for. I told them to stop blaming it as a software issue. In fact, I’m tired of watching law firms blame software for what is actually a human issue... Most firms use less than 30% of the features they’re paying for. Not because the tool is broken. But because the rollout was broken. I have seen the following happen way too many times: - The vendor gave a slick demo. - Made huge promises about what the software could do. (And to be fair — those promises can be true, if the tools are actually used to their fullest.) - Scheduled a couple of onboarding calls (with the partner, not the staff). - Then handed over a knowledge base and disappeared. Meanwhile, the team: - Sticks to old workflows. - Struggles through half-understood features. - Gets frustrated and stops using the tool. And three months later, leadership concludes that “This software doesn’t work.” But the uncomfortable truth? It’s not a software problem. It’s an ADOPTION problem. Adoption means: - Designing workflows that actually fit your firm - Training *every role* on how their day improves - Managing the short-term productivity dip that always comes before the long-term lift Until law firms understand this, they’ll keep wasting money on platforms that gather digital dust. If you’re a law firm owner signing your next tech contract, here’s what I recommend you do: 1/ Ask how adoption will be managed - not just for you, but for every role in your firm. 2/ Demand role-specific training – intake, paralegal, associate, partner. Each needs to see their day improve. 3/ Get clarity on long-term support – if the vendor disappears after a few onboarding calls, you’re paying for a 1h demo, not a long-term solution. Because the most powerful legal tech in the world is useless if it sits unused after the demo.

I’m hearing story after story of Microsoft Copilot failures. Not because the technology lacks potential-but because adoption is broken. Here’s what I keep seeing in real organisations: employees aren’t using Copilot. Either they don’t see the value, or they don’t even know how to. And honestly, I think I know why it keeps failing. What’s going wrong? All the fancy licenses, the generative AI hype, the “Copilot for every app” rollout-it’s mostly noise unless you fix the fundamentals. Right now organisations are: - Throwing licenses at employees - Forcing top-down adoption without frontline buy‑in - Assuming people will “figure it out” on their own - Focusing solely on technology rather than people + process The truth? Having AI isn’t enough. Effective adoption is what makes the difference. What successful companies are doing differently: The 5 E’s Here are five levers that smooth the path to real usage: 1. Educate – show what AI can do, with concrete use‑cases & real benefits. 2. Empower – don’t just hand out access; invest in training, coaching, support. 3. Enable – let people experiment in low‑risk settings (sandbox, pilot teams). 4. Engage – surface concerns (privacy, fear of replacement, “AI errors”) and address them openly. 5. Execute – adopt a clear strategy: purpose, milestones, feedback loops. Major objections & what the data shows 1. People say: “The AI features aren’t worth it.” I've heard it too. Basic issues-bugs, wrong outputs, poor UX-erode trust far more than lack of features. 2. Copilot isn’t inherently bad. In some places it speeds things up. But the fundamental issue isn’t the model or compute-it’s how Microsoft is pushing Copilot everywhere with an AI‑first/feature‑layered everywhere mantra, rather than asking: “Do users want this? Does this solve a problem?” This leads to: frustration, feature fatigue, confusion, and in many cases rejection. 3. Another big gap: most organisations treat AI adoption as a “roll‑out” not a redesign. They retrofit old workflows rather than imagining what work could become. (The really transformative companies are the ones reimagining agency: between people, systems, roles—not just plugging in AI into legacy tools.) What could Microsoft & others do differently? - Re‑prioritise: get the basics (UX, reliability, accuracy) right before pushing ubiquitous rollout. - Give users control: let them opt in, provide feedback, customise what AI touches. - Focus on role‑based relevance, not blanket feature dumps. - Build metrics & transparency: how many are using Copilot daily, how many prompts fail, what outputs need “fixing.” Use that to iterate. - Ethically sound data governance & privacy. Without trust, usage will stall. Having AI is one thing. Real success lies in human adoption. What do you think? Have you seen similar adoption hurdles? What stories have you heard of organisations doing it well? #AIAdoption #Copilot #GenAI #Automation #TechLeadership #Innovation #PeopleFirst