How to Build AI Adoption Awareness in Large Firms

GenAI adoption is all about people, not about tools. Pharma giant Novo Nordisk offers a great case study of working out what supports useful uptake of AI across a large organization. A case study in MIT Sloan Management Review uncovers a range of useful lessons. Here are some of the most interesting. 🚀 Recognize a mid-cycle drop as normal. Novo Nordisk grew Copilot use from a few hundred to 20,000 users in just over a year, with 23% becoming frequent users within one month. However, by month three or four, 15% of early adopters dropped off and average time saved per week declined. Recognizing this dip as natural helped avoid panic and kept the focus on re-engagement strategies rather than getting staff to try tools for the first time. 🛠 Deliver function-specific training through champion networks. Generic AI onboarding failed to meet the needs of specialized roles. Novo Nordisk succeeded by creating domain-specific training, leveraging internal champions to contextualize AI use, and allowing teams to shape guidance based on their actual work. This addressed “AI shaming” and bridged confidence gaps across functions. 🤝 Use internal champions to overcome cultural resistance. Skepticism wasn’t solved by policy, it was shifted by influence. Novo Nordisk identified trusted, high-status employees to openly adopt and advocate for AI tools. Their visible endorsement encouraged hesitant peers to try AI without fear of judgment or failure. 📈 Treat adoption as a change process, not a tech rollout. Rather than pushing a one-time launch, Novo Nordisk framed GenAI as a long-term transformation. This meant investing in ongoing communication, support structures, and iterative learning. The approach acknowledged that adoption would ebb and flow, and prepared the organization to adapt accordingly. 🎯 Emphasize strategic value over time saved. Though average users saved about 2 hours per week, the most meaningful wins came from higher-quality work—more strategic thinking, clearer writing, and better planning. By highlighting these human-centric gains, Novo Nordisk built a stronger case for AI’s workplace relevance beyond mere productivity. 📊 Use employee data to shape the deployment strategy. Over 3,000 employee surveys and interviews helped Novo Nordisk spot where and why adoption lagged. This feedback guided real-time adjustments—like where to invest in new use cases, where to scale back, and how to tailor messaging. It also surfaced which functions became tool-reliant versus those needing more support.

BREAKING: UK law firms lead AI adoption Or do they... Study of 700 professionals over 6 countries found 31% of legal professionals use AI tools daily This is the highest rate of any country surveyed. → UK lawyers projected to save 140 hours per year. → £2.4 billion in productivity gains by 2026. The headlines sound super encouraging. But I think they mask a dangerous gap. → Adoption measured is mostly Copilot, ChatGPT, and document summarisation. → These are general purpose AI tools anyone can use → This is not measuring workflow transformation After training 4,000+ lawyers on AI and 10 years building AI systems in legal, here's the sequence I've seen actually work: 1. Audit what you're actually using → List every AI tool in use across the firm and what it's being used for → If the answer is "email drafting and research summaries" across the board, you know exactly where you stand → The audit itself is often a wake-up call 2. Educate beyond awareness → Move past "intro to ChatGPT" into critical evaluation of AI output → Can your lawyers spot when AI hallucinates a clause that doesn't exist? Can they write prompts specific to their practice area? → One training day creates shared vocabulary. A structured programme over weeks builds the skills that stick. 3. Discover your firm-specific use cases → Interview practitioners, not just the innovation committee. → Example workflows = real estate team spending 6 hours on title report reviews. Or a litigation team manually coding thousands of documents. → Prioritise by impact, feasibility, and readiness to adopt 4. Build bespoke into your actual workflows → Find where AI can fit into existing workflows without heavily changing behaviours → Opt for workflows that increase adoption rate → Build sequentially, run tests on smaller cohorts and expand usage over time. E.g. As adjudication team went from 10% implementation to 95%+ over 24 months and now AI handles 20,000 cases annually. Thoughts?

AI adoption is failing at most companies. (it's not the technology) You use ChatGPT daily. Your team has random AI tools. No unified strategy. No measurement. Your VP keeps asking: "What's our AI plan?" You need frameworks, not more tools. 9 AI Adoption Frameworks: 1/ Workflow Audit Before Tool Selection → Map your team's top 10 daily tasks first → Flag repetitive work worth automating → Identify judgment calls for AI augmentation 2/ Build vs Buy Decision Matrix → Buy for standard ops (scheduling, emails) → Build only for competitive differentiation → Partner for specialized expertise gaps 3/ Pilot Program That Actually Scales → One department, one use case, 90 days → Define success metrics before you start → Document every lesson for VP presentation 4/ Executive-Ready Training Strategy → VP briefing: ROI projections and risks → Manager training: implementation roadmaps → User training: hands-on, role-specific 5/ ROI Measurement That VPs Care About → Track hours saved per employee per week → Measure quality improvements and accuracy → Calculate revenue impact, not just savings 6/ Data Governance Framework → Audit what data touches AI tools now → Create approval process for new platforms → Set data retention rules before scaling 7/ Change Management for AI Rollouts → Address "will AI replace me?" fears early → Show augmentation wins before automation → Create AI champion roles for career growth 8/ Smart Automation vs Augmentation Rules → Automate: data entry, report generation → Augment: strategy, creative work, decisions → Never automate: customer relationship calls 9/ VP-Level Adoption Mistakes to Avoid → Don't chase every shiny new AI tool → Never skip the governance foundation step → Stop letting AI adoption happen randomly AI adoption isn't a technology problem. It's a leadership strategy problem. Twice a week I send frameworks like this to 15,000+ operators in Tactical Memo. Join free: https://lnkd.in/eFNHsxmh

Everyone’s publishing “10 things your org should do for AI adoption.” Most of it is wrong. Or at least, incomplete. Here’s what I’ve learned working with orgs on the ground - not theoretically, but watching what actually moves the needle vs what sounds good in a strategy deck. AI adoption isn’t a rollout. It’s an energy problem. You need activation energy to get people to try something new. And you need to sustain that energy long enough for it to become habit. Most orgs get the first part. Almost none plan for the second. Here’s what actually works: 1. Hub and spoke, not top-down mandate. One central team setting direction. Multiple spokes embedded in real teams solving real problems. The hub provides frameworks and guardrails. The spokes provide context and use cases. Neither works without the other. 2. Leadership has to go first — visibly. Not “leadership supports AI.” Leadership uses AI. In meetings. In decisions. In front of their teams. If your CXO talks about AI but hasn’t rebuilt a single workflow, your teams will read that signal instantly. 3. Build activation energy deliberately. Most orgs do one big training, declare victory, and wonder why nothing changed three months later. Adoption needs repeated, structured nudges — workshops, office hours, challenges, showcases — spaced over weeks, not crammed into a single afternoon. 4. Celebrate the wins. Especially the small ones. Someone automated a 3-hour weekly report into 20 minutes? That’s not a minor efficiency gain. That’s proof of what’s possible. Make it visible. Make it a story. Let it pull others forward. 5. Encourage failure. Loudly. The biggest blocker to AI adoption isn’t access to tools. It’s fear of looking stupid. When someone tries to build a workflow with AI and it doesn’t work — that’s data. That tells you where the gaps in context, process documentation, or tooling actually are. Punishing that or ignoring it kills adoption faster than any technology gap. The org that gets this right doesn’t have “an AI strategy.” It has people who’ve changed how they work - and can’t imagine going back. —————- I am Priyadeep Sinha and I help AI Adoption Stick - for Leaders and Organizations at Work in Beta Every week, I share one complete AI workflow system for leaders, consultants and knowledge workers in my newsletter Work in Beta: https://lnkd.in/gPqYEzaJ

💡 The Secret to Successful AI Adoption? It’s NOT Just About the Tech 🤖✨ Everyone’s talking about AI models, tools, and algorithms… but here’s the truth: Technology alone won’t make your AI initiative succeed. The real differentiator? People, leadership, and culture. Here’s how top-performing companies are making AI work for everyone. 👇 1️⃣ Why the Human Side of AI Matters ♠️ AI fails when teams feel left out, blindsided, or unprepared. ♠️ Clear leadership vision + open communication builds trust and engagement. ♠️ AI adoption is a change management journey, not just an IT rollout. 2️⃣ Leadership, Vision & Culture Make or Break AI ♠️ Transparency: Show teams what AI will change and what will stay human-led. ♠️ Ethics & Trust: Encourage open dialogue about bias, fairness, and privacy. ♠️ Reskilling: Equip teams — from front-line staff to executives — to work confidently with AI. ♠️ Culture of Experimentation: Encourage learning, iteration, and collaboration between people and tech. 3️⃣ How to Align People, Processes & Technology ♠️ Establish Leadership & Vision: Set clear, strategic AI objectives tied to business goals. ♠️ Engage Stakeholders Early: Co-create AI use cases with managers and key employees. ♠️ Invest in Training: Deliver hands-on AI training, mentoring, and continuous education. ♠️ Redesign Workflows: Integrate AI into daily processes to remove busywork and enhance impact. ♠️ Embed Governance: Create clear policies on privacy, ethics, and accountability. ♠️ Monitor & Evolve: Track adoption, engagement, and results — then refine your approach. 4️⃣ Real-World AI Adoption Wins ♠️ Enterprises with governance + staff engagement report smoother rollouts and higher trust. ♠️ Financial services & healthcare leaders focusing on reskilling saw faster adoption AND better results. ♠️ SMEs piloting with employee input achieved stronger morale and early ROI. 🌟 Bottom Line: AI success isn’t just measured in teraflops — it’s built on trust, teamwork, and a clear, human-first vision. 💬 Your Turn: Where have YOU seen AI adoption succeed (or fail) because of leadership, culture, or communication — not just tech? Drop your story in the comments and let’s help each other get it right. #AI #DigitalTransformation #Leadership #ChangeManagement #AIAdoption #FutureOfWork #OrganisationalCulture #Innovation #ResponsibleAI #PeopleFirstAI #WorkforceTransformation

In our recent work with organisations, I keep seeing the same patterns emerge when it comes to adopting AI. Yes, there are technical considerations like security and privacy, but at the heart of it these are people issues. Nobody wants to use a technology if they feel it puts them or the business at risk. Trust matters, and without it, adoption stalls. Change management and training are also critical. Helping people develop an AI mindset allows them to use these tools in increasingly creative ways, producing higher-quality outcomes rather than just faster ones. Another big one is executive-level commitment. This cannot sit only with the CIO. Every leader, from the CEO to the CFO and beyond, needs to be able to explain why AI matters for the organisation. When leaders can clearly articulate that story, it signals to the whole business that this is a strategic priority, not just an IT project. Equitable access is just as important. Too often I see organisations give AI tools to a select group to control costs. While that makes sense in the short term, the result can be a cultural divide between the haves and the have-nots. People left out either disengage or start using unapproved tools, both of which create risk. Providing broad access, with the right guardrails and support, helps avoid that divide and encourages responsible experimentation across the organisation. These human, cultural, and leadership factors are what really drive successful AI adoption. The technology is only part of the equation.

Microsoft research shows 67% of the value your people get from AI comes down to your managers and culture, not the tools. For the last decade I’ve been working with organisations to embed data, analytics and AI in the way they work. What I’ve come to see is that building AI and data fluency requires more than a one-off project, but the budgets and bandwidths of people usually starts small. So my approach is to start with the bandwidth available, then build on top of it as an organisation matures. I think of it as a portfolio to layer up over time. You can get started by sorting every initiative two ways: is it building adoption (getting people started) or capability (getting people much better), and how much effort does it take to run? That gives you four groups, and a sensible order to add them. 1/ Whatever your bandwidth, start with easy adoption: training, a prompt library, lunch and learns, a tips newsletter, access for all. Gets everyone onto the on-ramp. A smart first move, not a lesser one. 2/ Keep adding to develop capability: leaders using AI in front of their teams, a champion on every team sharing real examples, communities of practice. Small budget, real capability, because it spreads through people. This is the highest-leverage layer most teams haven't switched on yet. 3/ Invest as capacity grows with coaching on live work, skill sprints and hackathons on a real problem, AI built into actual workflows. More effort, deeper capability, and where fluency compounds. And if you have the appetite for a bigger push to drive adoption look at a structured rollout or certification. The evidence backs building toward the mastery side as you go. Microsoft's 2026 Work Trend Index found 67% of AI's impact comes from your culture, managers and systems, not the tools. When managers visibly used AI themselves, their teams reported a 17-point lift in the value they got. BCG found the leaders pulling ahead put 60% of their AI budget into their people. So the move is not to do everything at once. Start with what your bandwidth allows, get the on-ramp working, then add capability on top, beginning with the cheapest high-leverage step: your leaders. ♻️ Repost to help a leader build AI capability. 🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Employees are noticing the lack of clarity around AI. They want to know what happens to them and their daily work. Saying "you will be more efficient" is not reassuring or sufficient. Many large firms are integrating AI, including highly intelligent systems and autonomous agents. But they often fail to clearly articulate what their organization will look like in the future. This creates uncertainty among employees. They need more than vague promises. They need a vision. A clear vision includes: • How AI will change daily tasks • New roles and opportunities • Training and development plans Firms must provide: → Transparency → Detailed plans → Reassurance Employees want to know: ↳ How their roles will evolve ↳ What new skills they need ↳ How they will be supported A well-communicated vision helps: → Reduce anxiety → Build trust → Boost engagement It’s not just about efficiency. It’s about creating a supportive and clear path forward. Steps to articulate your AI vision: 1. Define: What will your organization look like with AI? 2. Communicate: Share detailed plans with employees. 3. Support: Provide training and resources. 4. Engage: Involve employees in the transition process. Be proactive. Be clear. Be supportive. Help your team understand the future. Create a roadmap that guides them. Show them that the future with AI is bright.

AI adoption doesn't start with tools. It starts with people. Most businesses jump straight to software and skip the foundation. That's where adoption fails.   Not in the technology, but in the team. There are four levels to AI adoption that every company moves through. 𝗟𝗲𝘃𝗲𝗹 𝟭: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 This is where it starts. Get your team trained. Build confidence with AI tools. More importantly, leadership has to lead.  Mindset has to shift. Trust has to be established. Without this, nothing above it holds. 50% of companies are still here. 𝗟𝗲𝘃𝗲𝗹 𝟮: 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Before you can automate anything, you have to document everything. Capture how work actually gets done.  You need to encode the context. This is what makes automation possible and valuable. 👉 Encoding is the process by which domain knowledge gets captured so that it can be used by AI. 30% of companies are here. 𝗟𝗲𝘃𝗲𝗹 𝟯: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Pick your highest-value, most repeatable tasks. Automate them. Communicate the wins. Build reusable frameworks your team can rely on. 15% of companies are here. 𝗟𝗲𝘃𝗲𝗹 𝟰: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰𝘀 Build agents for complex, multi-step scenarios.  This is where AI starts working independently. Only 5% of companies are here. Jumping from Upskilling to Agentics is a big leap.   Follow the process and build incrementally over time. Getting your people on board is the foundation. Start at Level 1. Do it well. Everything else will follow. ♻️ Share if this resonates ➕ Follow Jason Moccia for more insights on AI and leadership.

Somebody has to say it: some AI tools are causing more harm than good. Not because the technology is bad. Not because people are resisting change. But because we keep rolling out tools without guidance, training, or context and calling it “innovation.” When employees are expected to figure it out on their own, confusion replaces confidence. Work slows down. Trust erodes. AI at work doesn’t fail loudly. It quietly creates friction when enablement is missing. If we want better outcomes, we have to design for adoption, not just deployment. If you’re rolling out AI at work and want it to actually help, here’s a simple place to start: 1. Start with the “why,” not the tool ✅ Be clear about the problem AI is meant to solve. Productivity, quality, speed, decision-making. If people don’t understand the purpose, they won’t trust the tool. 2. Define when and when not to use it ✅ Ambiguity creates hesitation. Give real examples of appropriate use cases and clear boundaries so employees aren’t guessing. 3. Train for workflows, not features ✅ Skip the generic demos. Show how the tool fits into existing day-to-day work, step by step. 4. Equip managers first ✅ If managers can’t explain or model usage, adoption stalls. Enable leaders before expecting teams to follow. 5. Build feedback loops early ✅ Create space for questions, friction, and adjustments. Early feedback prevents quiet frustration from turning into resistance. 6. Treat adoption as ongoing, not a launch event ✅ AI enablement isn’t a one-time rollout. It’s reinforcement, iteration, and support over time. AI works best when people feel prepared, not pressured. ——— ✦ ——— 🌱 More on AI + Workforce Development → Janet Perez