Managing User Adoption Of A New Learning Management System

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.

Of course your onboarding program failed. You built it to serve you — not your customer. I see it all the time. Companies over-engineer onboarding to hit internal milestones, check off boxes, and declare victory when they say it’s “done.” But here’s the truth: Just because you wrapped onboarding doesn’t mean your customer is ready. They’re still dealing with: Misalignment Confusion Internal resistance A tool they don’t fully understand how to use, let alone adopt. Here’s what’s actually going wrong: 1️⃣ You treat onboarding like a training event, not a change process 2️⃣ You deliver the same training to every user, regardless of role 3️⃣ You never define what success actually looks like 4️⃣ You don’t empower internal champions 5️⃣ You abandon them the second onboarding is “over” 6️⃣ You think “Go-Live” means “Mission Accomplished” 7️⃣ You ignore resistance to change 8️⃣ You don’t communicate enough (or clearly) 9️⃣ You overload them with info 🔟 You never got executive buy-in Want to fix it? Here’s where to start — tomorrow: ✅ Build a post-onboarding success plan Pre-populate it with the customer’s goals and share it before onboarding ends. ✅ Identify and empower champions early Find them at kickoff. Equip them to lead. Keep them close. ✅ Reinforce the WHY Stop talking about features. Start connecting usage to business impact. ✅ Monitor early signals and take action Don’t just measure adoption. Share it. Explain it. Adjust as needed. ✅ Keep the learning going Enablement isn’t one-and-done. Build ongoing learning paths and resources that scale. Let’s stop designing onboarding for our own convenience. And start designing it for customer success. Put them back in the driver’s seat. ____________________ 📣 If you liked my post, you’ll love my newsletter. Every week I share learnings, advice and strategies from my experience going from CSM to CCO. Join 12k+ subscribers of The Journey and turn insights into action. Sign up on my profile.

Blending IO psychology with digital innovation flipped the results of our last tech rollout. Most teams never connect these dots—here's why it changes everything ↓ Tech implementations often fail not because of the technology, but due to human factors. The deployment to a large international pharma company was heading for disaster until we brought in IO psychologists. They helped us understand: - How different personality types interact with new systems - The impact of change on team dynamics - Ways to reduce resistance and boost adoption We tailored our approach based on these insights: - Customized training for different learning styles - Change champions selected based on influence networks - Communication strategies aligned with team cultures The results were staggering: - 94% adoption rate within 3 months - 40% increase in user satisfaction scores - 25% boost in productivity post-implementation Key takeaway: Technology and human behavior are deeply intertwined. By considering both, we unlocked synergies we never thought possible. Next time you're planning a tech rollout, remember: The most powerful integration isn't between systems, but between tech and human psychology. Embrace this approach to transform your digital initiatives. PS - and if you know this story, you also know how it set me on the path for my PhD in IO Psychology.

What do most Customer Success teams get wrong about user adoption? It takes more than communication and training to change people’s behaviors. Damon Centola, a noted social network scientist, says there’s a big difference between hearing about something and creating new norms. Well-connected influencers can quickly get the word out, a phenomenon Centola calls a simple contagion. Like COVID-19 in the early days, news can travel exponentially fast. Add some resistance, however, and Centola says the contagion becomes complex—spreading it takes multiple exposures. For example, if you see a neighbor put their yard waste to the curb, you’d do nothing. But if three neighbors did the same thing, you’d ask what’s happening. After learning that there’s a special waste pick-up the next day, you’d clean up your backyard, too. It’s the same thing for technology. Once peers adopt, the social pressure’s on to adopt, too. In successful transformations, the wave starts small, progresses to adjacent groups, and after a tipping point, mass communication accelerates the change everywhere. The difference in this approach is astounding: 90% adoption versus only 3% relying on mass communications alone. The lesson? If your solution depends on changing the habits of many users, don’t rely exclusively on training and communication. Use social network analysis tools like the one pictured from Polinode to reveal the hidden change agents. Then work with your sponsor to target them, score an early win, and expand along the perimeter before converting the masses. You’ll get substantially higher adoption and your economic buyer will get maximum ROI. Integrate change management into your customer journey with scientifically proven methods. Contact me directly or see the Comments below. #customersuccess #customersuccessmanagement #cx #saas #revenueoperations #changemanagement

Most PMs obsess over technical milestones while elite PMs are laser-focused on end users. The difference? Forget Gantt charts or status reports. It's about understanding who actually determines your success. Let me be blunt: No matter how perfectly you execute the technical aspects, if end users hate your solution on the first day of go-live, you've failed. Enterprise IT projects live or die by user adoption. Period. Yet we spend countless hours on technical requirements while treating user readiness as an afterthought. Take Courtney, for example—a hypothetical ICU nurse who is going to be using a new system, which you will be implementing. Here's the uncomfortable truth: Courtney doesn't care about your project plan. She cares about whether she can do her job on Monday morning. Elite PMs know this. They reverse-engineer her success from day one. Here's what that looks like for Courtney for this 4-month enterprise IT rollout: 𝗧-𝟬: Courtney logs in successfully and can work effectively. (The actual moment of truth.) 𝗧-𝟮: Courtney completes training with confidence, not confusion. 𝗧-𝟭𝟰: Courtney registers for training because she understands its value. 𝗧-𝟯𝟬: Courtney's training schedule is released with enough notice for her to plan around it. 𝗧-𝟰𝟱: Courtney's training is developed based on real workflows, not technical specs. 𝗧-𝟲𝟬: Courtney is matched to her future role with clarity about what will change. 𝗧-𝟵𝟬: Courtney sees her future workflows in a non-production environment and gives you accurate (and, if necessary, brutal...) feedback. 𝗧-𝟭𝟭𝟬: Courtney's future workflows are designed for efficiency, not just technical functionality. 𝗧-𝟭𝟮𝟬: Courtney's current workflows are understood deeply, including pain points and workarounds. Two critical insights here: 1. Everything STARTS with Courtney, not the technology. 2. User readiness begins on day one of your project, not the final sprint. Average PMs celebrate when the system passes QA. Elite PMs celebrate when users can perform their jobs without calling the help desk. The technical aspects matter, of course. But they're just table stakes. The real differentiator is whether your end users feel prepared, supported, and empowered when they first log in. If you're not planning for end-user success from day one of your project, you're not just behind. You're playing the wrong game entirely. ~~~ What's your strategy for ensuring users are smiling, not cursing, on go-live day? Or if you're outside IT, how do you apply this end-user obsession in your domain? Drop a comment below. ⬇️

You're not just delivering a project You're delivering a behavior shift. A new system, process, or tool means nothing if no one uses it. Except most project plans stop at launch. Not adoption. If you're a PM, you're also a change manager. Here's 3 tips to build for behavior AND delivery: ☝ Define what's changing for the end user Every project introduces friction. New steps. New tools. New habits. Map the real impact. Not just the shift in duties, but the human change. ✌ Bring people in early Change lands smoother when people see themselves in the solution. Co-design communications + plans with users. This will make them champions rather than critics. 🤟 Reinforce even after launch The project isn't done at go-live. Change management doesn't just happen at the end either. It's a living process, so plan for training, support, feedback loops, and follow-ups. That's where real adoption happens. Deliverables don't manage change. People do. Make sure to build behavior change into your projects so they're successful. 🤙