Training Employees For Successful Digital Adoption

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.

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗶𝗻 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Technology is evolving fast, and businesses must adapt to stay competitive. But rolling out new tools and systems isn’t enough—employees and customers need the skills to use them effectively. This is where training plays a critical role in digital transformation. Without proper training, digital adoption stalls, productivity drops, and frustration grows. Here’s how training helps organizations successfully embrace new technologies. 1️⃣ Training Reduces Resistance to Change People resist what they don’t understand. Employees often fear that new technology will complicate their jobs, and customers may struggle to adopt unfamiliar tools. ✅ Early training builds confidence, showing users the benefits of new systems. ✅ Step-by-step learning helps ease the transition, turning uncertainty into mastery. 2️⃣ Faster Digital Adoption = Faster ROI Investing in new technology is costly, but the real value comes from how quickly employees and customers start using it effectively. ✅ Interactive training programs ensure teams can integrate new tools into their workflows immediately. ✅ Customers who understand new features are more likely to use them, increasing retention and satisfaction. 3️⃣ Preventing Productivity Loss During Transitions When employees don’t receive proper training, productivity takes a hit. Confusion leads to errors, and support teams get overwhelmed with questions. ✅ On-demand learning resources allow employees to learn at their own pace without disrupting workflows. ✅ AI-driven training solutions deliver personalized learning paths, ensuring employees get the information they need when they need it. 4️⃣ Creating a Culture of Continuous Learning Digital transformation isn’t a one-time event—it’s an ongoing process. Companies that embed training into their culture keep their teams agile and adaptable. ✅ Regular training updates help employees stay current as technology evolves. ✅ Microlearning modules provide bite-sized lessons that fit into daily work schedules. 5️⃣ Ensuring Customers Can Leverage New Features Customers don’t always explore new features on their own, meaning valuable tools go unused. Training ensures they get the most out of your product, leading to better satisfaction and retention. ✅ Tutorials, webinars, and AI-powered guides make digital tools easier to navigate. ✅ Proactive customer education prevents churn and drives deeper product engagement. Training is the Key to Digital Success Technology alone doesn’t drive transformation—people do. When businesses prioritize training, they unlock the full potential of their digital investments. Want your digital transformation to succeed? Invest in training. Your employees and customers will thank you. 🚀 #training #digitaltransformation #education #innovation #technology #analytics

How to train your team to adopt #AI – without creating fear or resistance Let's be honest: when you introduce AI to your team, some people get excited. Others get anxious. As a CEO, I've seen both. And I've learned that AI adoption is not just a tech rollout. It's a change management process. Here's what worked for my team: 🔹 Start with education, not disruption Before replacing anything, we ran team-wide sessions to explain what AI is (and what it isn't). No one wants to be replaced by something they don't understand. 🔹 Co-create, don't impose We asked each department: "What's repetitive? What drains your time?" This turned AI from a threat into a tool, because the team helped shape the use cases. 🔹 Train people, not just systems We didn't just introduce AI tools. We created learning tracks, internal demos, and buddy support. Adoption happens when confidence grows. 🔹 Make AI a habit, not a hype At our regular opening & closing weekly meetings, we discuss the latest AI tools, share how we use them, and learn from each other. This ongoing rhythm turns curiosity into capability and builds a culture where innovation is continuous. 🔹 Celebrate time saved, not headcount reduced The message was always clear: this is about making your work easier, not replacing it. When the team saw that the goal was empowerment, not elimination, resistance faded. AI can accelerate your #business. But it can also backfire if people feel left behind. The real secret? Adopt AI through empathy, not just efficiency. When people feel involved, supported, and safe – they become your biggest innovators. #AI #AIAdoption #TeamDevelopment #CEOLife

Your AI pilots are stuck at 20-30% adoption. Here's how to triple that in 30 days. I sat down with Cat Valverde, founder of Enterprise AI Group, on #Unchurned and she shared a deceptively simple framework that's transforming how enterprises roll out AI tools. The problem isn't what most leaders think. It's not just fear of job loss. Cat's research across 250 enterprise employees found the real adoption killer: cognitive overload mixed with fear of looking incompetent during training. Her solution? The 15-Minute Rule. Four weeks. 15 minutes per week. That's it. Here's the framework: ➡️ Week 1: Micro-commitment. Just click around for 15 minutes. No pressure, no follow-up. ➡️ Week 2: Choice architecture. Pick one feature that interests you and explore it for 15 minutes. This builds autonomy while reducing overwhelm. ➡️ Week 3: Social proof. Volunteers share 30–60 second takeaways in team meetings. This creates peer-driven momentum instead of top-down mandates. ➡️ Week 4: Real-world application. Use your chosen feature for one actual task — summarizing a client call, triaging emails, whatever aligns with your workflow. The results across their post-mortem study:  → Adoption jumped from 20-30% to 60-80%  → Training costs cut in half (from $1,000 to $500 per user annually)  → Performance improved 2–3X across enterprises and mid-market orgs Cat's background in organizational psychology informed this approach. She's applying principles from habit formation — micro-commitments, proximate objectives, collaborative learning — to AI adoption. The genius is that it works with human psychology, not against it. 💡 For CS leaders rolling out AI agents, this framework addresses the real barrier: making adoption feel manageable rather than mandatory. Listen to the full conversation on [Un]Churned to hear Cat break down each week in detail. (Links in comments.)

Lots of organisations are trialling Microsoft Copilot, but few share the results. Vendors provide glowing case studies, but what about the mixed ones? That’s why I was excited to see a public study from the Office of Digital Government Western Australia. It was more nuanced than the usual rose-tinted vendor stories, offering valuable insights into AI adoption, raising questions about implementation strategies the rest of us can learn from 5,765 licenses deployed: solid sample size for a robust trial 33% adoption rate: Decent for a new, little-understood workplace technology, but hardly transformative The primary use? Summarising meetings & drafting—important but isolated tasks that lack the integration needed for broader impact. Copilot is doing work that might otherwise not get done, but it’s not yet the game-changer AI could be Observations: Limited integration: Meeting summaries and drafts are isolated activities. Without connecting tools to broader workflows, the potential for transformative value is lost Lack of process analysis: A comprehensive process review was recommended but appears not to have been done. Dropping tools into workflows without context limits ROI Adoption gaps: Why did only 33% adopt when meetings are universal? Barriers—technical, cultural, or support-related—likely played a role Training matters: People who undertook more than one type of training (eg workshops, peer learning, self-paced modules) showed much higher adoption rates. Varied, ongoing training is essential to building confidence and capability Technical limitations: Issues with Excel & Outlook and inaccuracies hurt productivity. Familiarity bias toward enterprise platforms like Microsoft might not always serve users best Prompt engineering struggles: Challenges with prompts suggest gaps in training or change management rather than tool design Over-reliance risks: Concerns about losing deep knowledge are valid. Organisations must balance efficiency with accountability and critical thinking Early adopter bias: Early users were perceived as more productive, which may foster resistance or fear—a common hurdle in change management If you’re planning a trial: Invest in varied training: Training shouldn’t be a one-off. Use diverse formats and reinforce adoption over time Choose fit-for-purpose tools: Don’t default to familiar vendors. Smaller, more agile tools can often deliver better results Conduct a discovery phase: A thorough process review ensures tools align with organisational needs, reducing risks and maximising ROI Set clear metrics: Measure costs, benefits, and adoption outcomes to guide experimentation and ensure accountability If your organisation is running a Copilot trial, or considering one, these steps can help you maximise success. And of course, you can always come talk to us at Lithos Partners. You knew that, right? Have you worked with AI tools like Copilot? I’d love to hear your experiences or tips for successful adoption.

You can't train just your way to AI fluency. I know - everyone's selling you training, including me! While (effective!) training is important, what you really need is a culture shift that either starts at the top or doesn't happen at all. I talk about this and more during my recent 𝗣𝗲𝗼𝗽𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗣𝗲𝗼𝗽𝗹𝗲 podcast - here are some highlights: 𝗪𝗵𝘆 𝗺𝗼𝘀𝘁 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗮𝗶𝗹 (𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀): Here's what nobody wants to admit - throwing AI tools at your organization won't make it fluent. Training sessions won't either. The real work is changing how people think about their jobs - and that starts with leadership. The gap isn't technical. It's behavioral. 𝗧𝗵𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: When executives say "AI is strategic" but don't use it themselves, you get shelf-ware, not workflow integration. Leadership models behavior. Full stop. If the C-suite isn't demonstrating AI fluency, why would anyone else? 𝗧𝗵𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗿𝗮𝗽: Training ≠ fluency. It's like watching a fitness video versus actually hitting the gym. Real fluency requires repetition, role-relevance, and reinforcement. Give people five specific points in their workflow where AI should be used, with starter prompts. Then teach them to spot their own opportunities. 𝗧𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝘀𝗶𝗱𝗲 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗶𝗴𝗻𝗼𝗿𝗲𝘀: 64% of senior leaders say fear of replacement is stifling AI adoption. Yet only 24% call employee resistance a major barrier. That disconnect? That's why rollouts fail. You can't ignore the psychological reality and expect behavioral change. 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝗹𝗶𝗸𝗲 𝗺𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀: This isn't a gimmick - it's how you actually get results. You wouldn't give a human vague instructions and expect excellence. Same with AI. Context + goals + examples + feedback = better output. And when you distribute AI to thousands of employees, you're effectively doubling your workforce. Who's managing those work resources. 𝗧𝗵𝗲 𝘂𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝘁𝗿𝘂𝘁𝗵 𝗳𝗼𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱 𝘄𝗼𝗿𝗸𝗲𝗿𝘀: Their unconscious competence becomes baggage when paradigms shift. You're asking people who excel at their jobs to regress to "consciously incompetent" and relearn their work. That's hard. It requires psychological safety and permission to experiment without quota pressure breathing down their necks. 𝗪𝗵𝗮𝘁 𝗳𝗹𝘂𝗲𝗻𝗰𝘆 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲: It's not training events. It's habits. Employees redesigning their own workflows. Leaning into AI by default. Talking about "how we use AI" as naturally as they talk about email. That's when you know it's working. The conversation about AI transformation is human, not just technical. Get that part wrong, and the technology doesn't matter. Link to the podcast in the comments. #AI #FutureOfWork

You can spend millions on new tech, but without this one skill, you're part of the 70% that fail. Ever watched a child resist trying new food? That's exactly how most employees feel about new technology at work. I learned this the hard way while leading digital changes in my team. The game changer wasn't fancy software, it was understanding how my team felt. Here's the exact playbook that turned my team's tech fear into enthusiasm: 1. Listen first, act later. When team members worry about losing their jobs to automation, show them how the new tools will make their work easier, not take it away. Schedule dedicated 1:1 sessions to document concerns. 2. Keep talking, keep sharing. Set up structured communication channels, bi-weekly tech updates and anonymous feedback systems. 3. Take baby steps. No one learned to run before walking. Give your team time to learn new tools at their own pace. Break training into short, digestible 15-minute daily modules focusing on immediate-use features. 4. Celebrate small victories. Create a weekly "Tech Win" spotlight in team meetings to recognize progress. 5. Know yourself first. As a leader, if you're stressed about change, your team will feel it too. Use established change management frameworks to assess and manage your own readiness for change. The success of digital initiatives isn't measured by technological efficiency, but by how well teams adapt and thrive in their new environment. What's the biggest challenge you've faced when implementing new technology in your team? #Leadership #Growth #Change #Success

If your AI enablement plan starts and ends with Copilot training… you won’t scale. Yes, people need to learn the tools. But tooling alone doesn’t change organisations. Real adoption happens when you engage people’s heads, hearts, and confidence, not just their keyboards. Before you even think about training on the tools, get these five things in place: 1. Start with the “why.” Be clear about why you’re adopting AI and what problem you’re solving for your sector, organisation, and people. 2. Communicate the roadmap. Share how AI will be introduced over time, including expectations around continuous learning and change. 3. Show leadership commitment. Leaders need to model how they’re using AI and create psychological safety for others to experiment. 4. Make space for the human reaction. Give people time to talk about how they feel about AI; curiosity, concern, scepticism and all. 5. Build a real learning strategy. Teach practical AI skills alongside critical thinking, human judgement, and management capability. Because here’s the tension: You’re asking people to adopt a technology they constantly hear might replace them. Your job isn’t just to deliver AI. Your job is to deliver AI that works for people and performance. Productivity and efficiency matter. But so do motivation, confidence, and job satisfaction. And it’s achievable. We’re collaborating with our clients to make it happen right now. Get in touch to learn more.