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.

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.

 AI is doomed to fail if you don’t put your employees first. Here’s how you can do that.  When it comes to AI transformation, most organizations fall into the trap of focusing solely on technology but the truth is, without considering people, even the best AI solutions struggle to deliver real impact. Research shows that 70 percent of AI projects fail to meet their objectives, largely due to poor adoption by employees. That’s where the FriendlyCHRO Method comes in. It’s a 3-step framework I developed that puts human connection at the core of AI adoption, ensuring sustainable and effective change. Here’s how it works: 📌Involve everyone:  Engage all levels of your organization early on. Invite leaders, team members, and frontline employees to AI strategy meetings. Let them participate in defining the transformation’s vision and roadmap. This way, they feel ownership in the process and have a stake in its success. 📌Create emotional buy-in:  Address fears and provide clear answers. Hold regular Q&A sessions where leadership can engage directly with employees about AI’s benefits and challenges. Share success stories of AI adoption in similar companies or teams to demonstrate its positive impact on people’s roles. 📌Train and upskill: Implement a comprehensive AI training program that goes beyond just using the technology. Focus on how to integrate AI into daily tasks, with special emphasis on making employees feel confident in using these tools. Offer ongoing support through AI mentoring sessions or dedicated helpdesks. It’s time to shift the focus from just tech to people. When you lead with empathy, AI adoption isn’t just successful, it’s transformational. What’s your approach to human-centered AI adoption?

After training 100+ finance teams, working with 2,700+ CFOs and finance leaders inside my AI Finance Club community, and training 50,000+ finance professionals live… Here is what I would do if I had only 2 weeks to double AI adoption in a finance team. Not with a big transformation project Not with a 40-slide AI strategy deck Not with random experimentation Because having access to AI is not the same as having adoption. I would do this: 1. Give everyone a corporate AI license If only a few people have access, AI becomes a side project (especially if it’s given to only managers who anyway have already too much to do) If everyone has access, AI becomes a team capability. Choose one approved tool: ChatGPT Business / Enterprise Claude Team / Enterprise Microsoft Copilot Business Google Gemini Pro The tool matters less than the rule: Everyone uses the same approved environment. This reduces confusion, avoids shadow IT, and gives people permission to start. 2. Run one “eye-opener” session People don’t use AI correctly because they have not seen what is possible in their own work. So show them finance use cases: AI Dashboard AI in Excel AI in Powerpoint Scenario analysis The goal is simple: Make people say: “I did not know AI could help me with that.” I’ve seen that this changes significantly adoption 3. Create an AI use cases / wins channel Open one Teams or Slack channel (finance-ai-wins / ai-use-cases / finance-ai-help) Then ask your champions to share examples every week. Use a simple format: Problem Prompt Output Time saved What was checked before using it This channel has two jobs: Champions share what works. Slower adopters ask questions without feeling stupid. AI adoption will not happen only in training It happens when people see their colleagues use it 4. Run a weekly 1h Lunch & Learn 30 minutes: one person shares a real use case 30 minutes: Q&A and discussion. Examples: How I used AI to write my variance commentary How I used AI in Excel How I cleaned a messy file faster This continues the momentum 5. Document AI workflows as SOPs This is where most companies fail. They celebrate one good AI use case… But they don’t turn it into a process. The SOP should include: When to use it Which tool to use What data can be used The prompt Expected output Verification steps Human review required This is how you move from “cool prompt” to repeatable workflow and make sure everybody can use it 6. Build AI assistants based on the SOPs Once your SOPs are clear, you can turn them into AI assistants. Custom GPTs Copilot Agents Gemini Gems Claude Skills This is how a finance team moves from: “I tried ChatGPT once.” To: “We have AI workflows we use every week.” —— What i’ve seen and taught: adoption starts with access, examples, champions, repetition, documentation, and then assistants. In 2 weeks, you can really start the momentum Feel free to share your examples below of what worked for you!

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.

𝐀𝐈 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐧 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬: 𝐅𝐨𝐮𝐫 𝐋𝐞𝐯𝐞𝐥𝐬 𝐟𝐫𝐨𝐦 𝐂𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲 𝐭𝐨 𝐀𝐈-𝐍𝐚𝐭𝐢𝐯𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 Most companies think they are further along in AI adoption than they actually are.  This framework maps four distinct levels and being honest about where you sit is the first step to moving up. LEVEL 1: INDIVIDUAL USAGE (Curiosity-Driven) Goal: Individuals experiment with AI to save time. • Quick Tasks: Used for emails, brainstorming, and summaries • No AI Strategy: No formal company policy or direction • Personal Tools: Employees use different AI tools individually • Manual Workflows: Outputs are copied manually between tools • Early Exploration: High curiosity but inconsistent results • No Data Governance: Sensitive data may be shared without safeguards LEVEL 2: TEAM-LEVEL EXPERIMENTATION (Process Exploration) Goal: Teams begin applying AI to real work processes. • AI Content Creation: Used for emails, posts, reports, and documents • Meeting Automation: AI summarizes meetings and extracts action items • Workflow Automation: Simple AI chains automate repetitive tasks • AI Research Support: Helps analyze competitors and summarize reports • Tool Consolidation: Teams narrow down to a few preferred AI tools • Manager-Driven Adoption: Leaders encourage AI adoption LEVEL 3: DEPARTMENTAL AI INTEGRATION (Structured + Scalable) Goal: AI use becomes standardized across teams. • AI Playbooks: Defined workflows for each department • Data Pipelines: Clean, structured data feeds AI systems • Prompt Libraries: Shared prompts ensure consistent results • AI Team Champions: Each team has someone responsible for AI adoption • Security Controls: Data protection policies and tool vetting in place • ROI Tracking: Teams measure productivity gains and cost savings LEVEL 4: AI-NATIVE OPERATIONS (Autonomous + Self-Improving) Goal: AI is embedded in every workflow and continuously improves. • AI-Driven Decisions: AI guides strategy, hiring, pricing, forecasting • Connected AI: AI systems across teams work together automatically • Self-Learning: Models improve continuously using new data • AI Governance: Policies ensure ethical and secure AI use • Custom Models: Internal data trains specialized AI models • Revenue from AI: AI creates new products and services MY RECOMMENDATION At Level 1: Establish an AI strategy and basic data governance immediately. At Level 2: Consolidate tools and appoint AI champions per team. At Level 3: Build data pipelines and prompt libraries before scaling further. At Level 4: Focus on connected AI systems and self-learning loops. Which level best describes your organization right now? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #EnterpriseAI #AgenticAI #AIGovernance

𝐒𝐜𝐚𝐥𝐞 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 𝐰𝐡𝐲 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐦𝐚𝐧𝐝𝐚𝐭𝐞 𝐢𝐬 𝐧𝐨𝐭 𝐟𝐚𝐬𝐭 𝐞𝐧𝐨𝐮𝐠𝐡. Every scale software company I talk to is doing something with AI. Workshops, pilots, hackathons, tool rollouts. But very few are seeing the acceleration they expected. The question I keep getting from CEOs: "How do we actually bring AI into the org in full force?" I see four actions that companies must tackle: 𝐁𝐮𝐢𝐥𝐝 𝐜𝐨𝐧𝐯𝐢𝐜𝐭𝐢𝐨𝐧 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐞𝐟𝐨𝐫𝐞 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞. Mandating AI adoption top-down does not always work. One of our portfolio companies tried it — adoption was mixed after a month. What worked: make tools available, celebrate early adopters, let teams experiment without an ROI case upfront. Identify your AI enthusiasts and have them work across departments — not to lecture, but to show what is possible. Champions spread adoption faster than any mandate. As conviction grows, increase compliance. 𝐆𝐫𝐞𝐞𝐧𝐟𝐢𝐞𝐥𝐝 𝐟𝐢𝐫𝐬𝐭, 𝐭𝐡𝐞𝐧 𝐛𝐫𝐨𝐰𝐧𝐟𝐢𝐞𝐥𝐝. Start with new AI-native features, prototypes, or internal tools. Run hackathons — fastest way to surface prototypes and get the team excited. Faster to show value, no interference with current goals. Then move to the existing codebase. Touching a live product requires the AI muscle that greenfield builds first. 𝐑𝐞𝐢𝐦𝐚𝐠𝐢𝐧𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐮𝐬𝐞-𝐜𝐚𝐬𝐞𝐬 𝐭𝐡𝐚𝐭 𝐰𝐞𝐫𝐞 𝐧𝐨𝐭 𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐛𝐞𝐟𝐨𝐫𝐞. Every scale software company has customer problems they shelved because they were too expensive, too complex, or too manual to solve. AI changes that math. The best companies are enabling use-cases their customers never thought to ask for. Your domain knowledge tells you where the pain is. Your data tells you what is possible. And you already have trust and relationships with your customers — that is a significant advantage no startup can replicate on day one. 𝐂𝐗𝐎𝐬: 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐩𝐫𝐞𝐚𝐜𝐡. One of our portfolio CEOs took a week off, sat with one engineer, and built the entire company website using Claude Code. Took it live. Things drastically changed after this and the team made more progress than what two quarters of pushing AI-first thinking from the top had achieved. Preaching from a slide deck does not work. Getting your hands dirty does. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: Build conviction before mandating. Start greenfield, then tackle brownfield. Reimagine your product using your core capabilities. And make sure your CXOs are building with AI, not just sponsoring it. The companies moving fastest are doing all four — and with extreme velocity. This is not a transformation over quarters. The window to build AI muscle before your competitors do is measured in months. Treat it with that urgency. What has worked in your company to get AI adoption moving fast? Would love to hear.

AI Adoption is Stalling in Your Org—Here’s Why (And How to Fix It) AI isn’t the future. It’s now. And yet, in too many organizations, ambitious AI initiatives hit an invisible wall—cultural stall. A client of mine, a fast-moving, high-change-tolerance exec, recently found himself in this very situation. He saw AI as a catalyst for transformation. His company? More like a fortress of tradition. The result? A slow crawl instead of a sprint. So, why do even the smartest AI strategies grind to a halt? Three core reasons: 1. Fear: “Will AI Replace Me?” AI doesn’t just change workflows—it challenges identity. Employees fear obsolescence. Leaders fear looking uninformed. Unchecked, fear turns into passive resistance. 🔹 What smart leaders do: Flip the narrative. AI isn’t a job taker; it’s a value amplifier. Show—not tell—how AI makes work more strategic, not less human. Make AI upskilling a leadership priority, so people feel empowered, not endangered. 2. The Status Quo Stranglehold Big companies have institutional memory. “This is how we’ve always done it” isn’t just a mindset—it’s a roadblock. AI disrupts deeply ingrained habits, and people default to what’s familiar. 🔹 What smart leaders do: Instead of forcing AI as a hard pivot, position it as an acceleration of what already works. Connect AI adoption to existing business priorities, not as a standalone experiment. Find internal champions—people with credibility who can shift the narrative from the inside. 3. No Quick Wins = No Buy-In AI often feels abstract—too complex, too long-term, too risky. If employees can’t see immediate benefits, skepticism spreads. 🔹 What smart leaders do: Deploy fast, visible wins. Start with low-friction, high-value applications (automating reports, enhancing decision-making). Make results tangible and celebrated. Small victories create momentum—and momentum is everything. Bottom Line? AI Adoption Is a Mindset Shift, Not Just a Tech Shift. Your strategy isn’t enough. Your culture has to move at the same speed. The leaders who win with AI aren’t just tech adopters—they’re behavior shapers. So, if your AI initiative is stalling, ask yourself: Are you implementing AI, or are you leading AI adoption? The latter makes all the difference. 🔹 In my next post, I’ll share real-world success strategies from leaders who’ve cracked the code on AI adoption—so their teams aren’t just accepting AI, but accelerating with it. Stay tuned.