Implementing a Learning Management System

Change management when a company replaces its existing product with a new one! This transition is not about the software - it is about the people, processes, and mindset. When a company is accustomed to using a particular system, they develop a workflow, which leads to challenges in changing to a new one. The key to successful adoption lies in a structured approach: Awareness, Training, Support, and Feedback. 1️⃣Awareness. Users must understand why the change is happening. Communicate the benefits—whether it’s efficiency, cost savings, or compliance. 2️⃣Training ensures users are comfortable with the new system before they fully migrate. This involves hands on sessions, quick reference guides, and scenario based learning. 3️⃣Support - No matter how intuitive a system is, users will face challenges. A dedicated support structure—live chat, email assistance, etc. ensures a smooth transition. 4️⃣Feedback loops are essential. Gathering user concerns and addressing them refines the process and increases user confidence. The goal should be to make the users feel empowered, not burdened, by the shift. #changemanagement #shipsandshipping #maritimeindustry #management #training

The decision that defined our AI transformation. Train everyone at once or go dept by dept? Our CFO wanted speed. “Rip the band-aid off. One massive rollout.” Our COO wanted precision. “Pilot with marketing. Learn. Iterate.” We chose speed. We rolled out AI training to 400 people. Some became power users. Most went back to their old workflows in weeks. Here’s what that experience taught me. What Company-Wide AI Training Gets Wrong 1/ One-Size-Fits-All Content → Marketing needed generative AI for campaigns. → Finance needed forecasting and analysis. → HR needed screening and engagement workflows. Instead, everyone got the same generic training. 2/ Bottlenecked Support 400 people. 2 trainers. 1 IT channel. → Questions sat unanswered for days → Frustration replaced curiosity → Momentum died quietly 3/ Change Fatigue Everywhere at Once Every department was disrupted simultaneously. → No coverage while others learned → Client deliverables slipped → Managers absorbed pressure from all sides 4/ No Champions, Just Casualties Fast learners had no peer group. Slower learners had no safe place to ask questions. → No internal community formed or shared wins Six months later, we tried again. This time: one department at a time. We started with Marketing. Within two weeks: → Campaign briefs were automated → Content cycles shortened → Prompt libraries were shared internally Success became visible. Then we moved to Finance. Then HR. Each group had: → Training built around real workflows → Examples from active projects → Dedicated support → Early adopters turned into internal coaches Adoption spread peer-to-peer instead of top-down. The Cost Reality The biggest lesson wasn’t about training design. It was about cost structure. When we compared the two approaches, directionally: Company-Wide Rollout → Higher upfront training spend → 3–4x more productivity drag → Significant license waste from low usage → Adoption under 25% Department-by-Department Rollout → Lower overall training investment → License spend aligned with active users → No revenue impact → Adoption close to 90% Same 400 people. Very different outcomes. The Framework I Use Now If you go company-wide: → Build role-specific tracks → Train managers first → Stagger start dates → Staff support aggressively If you go department-wide: → Start with the most change-ready team → Document use cases and playbooks → Turn early adopters into internal trainers → Let success stories travel Company-wide training feels faster. Department-wide training becomes faster → When you measure sustained usage → When workflows actually change → When ROI shows up AI transformation isn’t a training problem. It’s a behavior change problem. Training your marketing team to use AI saves you time and money. Use my GPT to find out how much: https://lnkd.in/gdMizmRH Save this for when you start training your team.

New tech rarely dies in testing. It dies when real people have to use it. The pilot works. The demo lands. The use case makes sense. And still, it never scales. Why? Adoption measures behavior. And behavior is where the brain gets involved. Here’s the neural map to getting past the pilot phase: 👇 1️⃣ Don’t assume a successful pilot means people are ready Do this: ↳ Design for behavior change, not just proof of concept The science: ↳ The brain can like an idea and still resist changing routines ↳ The basal ganglia prefers familiar patterns over new effort 2️⃣ Don’t lead with technical performance Do this: ↳ Lead with what gets easier, safer, or faster for the user The science: ↳ The brain scans for personal relevance first ↳ If value doesn’t feel immediate, attention drops 3️⃣ Don’t ignore the fear underneath adoption Do this: ↳ Surface and reduce the emotional risk of using the tech The science: ↳ New tools can trigger fear of failure, exposure, or replacement ↳ People protect status before they embrace change 4️⃣ Don’t make the new workflow feel too different Do this: ↳ Anchor adoption to behaviors users already know The science: ↳ The brain prefers familiarity and predictability ↳ High perceived effort creates resistance fast 5️⃣ Don’t treat training like a side task Do this: ↳ Make training simple, repeated, and tied to real use moments The science: ↳ The brain learns through repetition and reward ↳ Memory strengthens when learning is applied in context 6️⃣ Don’t overload users with too much information Do this: ↳ Simplify the message and narrow the actions The science: ↳ Working memory is limited ↳ Cognitive overload reduces confidence and follow-through 7️⃣ Don’t assume logic will override politics Do this: ↳ Make adoption feel safe socially and professionally The science: ↳ Social pain lights up many of the same brain regions as physical pain ↳ If adoption feels politically dangerous, scale dies 8️⃣ Don’t make the first experience slow or clunky Do this: ↳ Create a fast first win users can feel The science: ↳ Early wins create dopamine ↳ If the first experience feels frustrating, the brain tags it as costly 9️⃣ Don’t leave the middle managers out Do this: ↳ Equip frontline leaders to reinforce the change daily The science: ↳ The brain looks to authority and peer behavior for safety cues ↳ Local managers shape whether a new behavior feels normal 🔟 Don’t stop at proving the tech works Do this: ↳ Prove people can adopt it consistently under real conditions The science: ↳ The brain trusts repeatability more than novelty ↳ Scale requires lower friction, lower threat, and clearer reward P.S. What's the last pilot you saw fail? ➡️ If your new tech is getting interest but still not making it past pilot, try this --> https://lnkd.in/gvZNBKq9 -------------------------------------------------------------------- ♻️ Share this with a founder building new tech ➕ Follow Shannon for more brain-based GTM tactics

The Rollout Is Just the Starting Line. Now Listen, Learn, and Adapt Rolling out new technology isn’t a finish line; it’s where the real work begins. The first few weeks post-launch are critical. That’s when friction points surface, shortcuts emerge, and usage patterns reveal what’s working (and what’s not). That’s why smart leaders build robust feedback loops from day one, not as an afterthought. 📢 Create clear, no-hassle ways for employees to share real-time feedback (on usability, integration gaps, or where they’re getting stuck). 🔁 Commit to action: Based on that input, adjust workflows, refine dashboards, or tweak configurations. Even small changes show you’re listening. 🎯 Provide targeted follow-up training, focused on what people need help with, not what the vendor’s onboarding assumed. This isn’t about perfection on day one, it’s about building a system that adapts quickly and aligns with real user experience. Because when employees feel heard and supported, adoption doesn’t just stick, it accelerates. How are you closing the loop between user feedback and system evolution? If you need help, you can always talk to Digital Transformation Strategist.

Even when you think something is easy to use, like a chatbot, adoption doesn't happen by accident. Adoption is success you need to plan for, and you need a strategy around it. The strategy I use is called the 3 E's: Education, Engagement, and Execution 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧 should be your first adoption planning step, and it should also continue to run throughout the project. A lot of people don't think about education as related to driving engagement, but it's critical. If people don't know 𝐰𝐡𝐲 it makes sense for them to start working in new ways, they have no motivation to do so. Operating in the dark is scary, a compounded feeling since change is already scary. If your adoption program starts with training at the end of a project, you've already failed. 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 is the next step, and it's multi-stage. Engaging end users in every step of the project is vital. Understand their needs by listening to them, engage them in process improvement, engage them in pilots and iterative roll-outs, engage them through training. This project is for them and they should be involved so that by the time roll-out approaches, they're excited about it! It should not come as a surprise to them that there's a new platform for them to use, or what the platform is - they should be eagerly awaiting it and feel ownership already. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 is the final step. As you roll-out and train, you'll need to capture pockets of success and amplify them. Put channels in place and ensure cross-pollination so that peer-to-peer word of mouth can spread. Capture metrics and tell everyone, capture quotes of success and happy use and spread these. This is a human-centered adoption program with enough flexibility to suit any environment and any project. Because adoption is all about your people: success means you've met their needs. They need to be at the center of your project the whole way through. #changemanagement #adoption #legaltech #AI

Buying technology is easy. Getting people to use it? That’s the hard part. Too often, companies invest in new software expecting it to transform operations overnight—only to hit major roadblocks with operational alignment and adoption. The system gets underutilized, workarounds emerge, and the promised efficiencies never materialize. Sound familiar? Here’s why technology adoption stalls: ❌ Poor process alignment – If tech doesn’t fit how people actually work, they won’t use it. ❌ Lack of user buy-in – People resist change when they don’t see the value. ❌ Insufficient training – A one-time demo isn’t enough. Users need hands-on learning and job aids aligned to their day-to-day activities. ❌ No accountability – Without clear expectations and leadership support, adoption suffers. A successful implementation isn’t just about turning the system on—it’s about making sure people actually use it. That’s why a change management strategy is essential to drive adoption and long-term success. When we help clients select and implement new vendor management systems, we focus on more than just system setup—we develop a change strategy to drive adoption. This includes: ✅ Setting clear adoption goals and success metrics to measure impact and progress. ✅ Engaging users early to gather requirements and build buy-in from the start. ✅ Optimizing workflows to ensure processes align with and fully leverage the technology. ✅ Designing tailored training, support, and feedback mechanisms to reinforce adoption. ✅ Ensuring leadership actively supports and champions the change to drive accountability. Technology alone doesn’t drive change—people do. Investing in adoption strategy is just as important as investing in the software itself. What’s been your biggest challenge with technology adoption? Drop a comment below! ⬇️

If your goal is scaled adoption, putting training inside your product won’t get you there. It sounds right in theory: meet users where they work, deliver learning in context, and watch adoption grow. But here’s the reality — outside of Outlook, most people don’t live in your software. They’re busy. They’re task-driven. They log in to do something, not to learn something. And what about your dark users — the ones who’ve never even logged in? They’ll never see your in-app guides, walkthroughs, or pop-ups. In-product training solves for accessibility, not adoption. Real adoption happens when users: ·      Understand why the change matters ·      Feel confident applying it in their day-to-day flow ·      Experience consistent reinforcement across channels and peers Scaled adoption requires more than product-embedded content. It requires an orchestrated experience that reaches users before they ever open your app — and keeps them engaged long after. If you want true scale, stop thinking in-product training and start thinking ecosystem enablement.

The AI adoption gap isn't a tooling problem. It's a habit problem. Stop training. Start embedding. Every company I talk to right now is rolling out (or wants to roll out……or has rolled out unsuccessfully) an AI training of some form. Decks and LMS links. Maybe a lunch-and-learn if you're lucky. Then everyone goes back to their desk… and nothing changes. Why? Because training alone doesn't shift behavior when the stakes are high and the work gets real. What to do/change/blend into the mix? Embedded execution partners. Another webinar won’t help! However, add in a calm, low-ego, no-judgement operator sitting in the second chair. Game changer. Someone who shows up where the work actually happens and supports you in the tools, in the flow, is available when you need them, dealing with the messy day-to-day, and stays until the new habit sticks. Bingo! Research backs this up too: training gives you a modest bump. Training PLUS hands-on coaching? It multiplies adoption. People learn by doing (70%) and being coached (20%), not by watching a video (10%). Rollout idea? Pick one team. One painful workflow step/change. Assign a coach who sits with them for X days for Y mins/day. Observe, co-pilot, hand back, audit. Track what matters: time to first use, 7-day repeat rate, reversion. If people slip back into old habits, run it again. Progress over perfection. The takeaway: If you want the behavior, change the work while it's being done. Stop hoping adoption happens. Embed the people who make it happen. Phew, that felt good…been meaning to write this post for a while! Hope it's helpful. Would love to hear your thoughts/experiences. #AIAdoption #AITraining #EmployeeTraining #EmployeeDevelopment

20+ interviews with learning & enablement leaders - this came up every single time: No initiative succeeds without a change management plan. New software doesn’t fail because it’s hard to use. It fails because change is hard to manage. Yet training is still too often treated as a box to check. A set of guides. A kickoff workshop. A quick LMS course. And then we wonder why adoption stalls. The strongest teams treat training as part of change management, not just knowledge transfer. Here’s what that looks like in practice: - Identify Champions. Bring in power users early. They model behavior and influence peers in ways no slide deck ever could. - Communicate the “Why”. Tie training back to business outcomes leaders care about — faster onboarding, fewer errors, stronger customer outcomes. - Stage the Rollout. Don’t overwhelm on day one. Layer learning in phases and let people build confidence step by step. - Reinforce Over Time. Change sticks with repetition. Use reminders, refreshers, and in-app nudges to keep habits alive long after launch. One leader put it perfectly: “Our job isn’t to train on features. It’s to guide people through the discomfort of change.” That’s the difference between checking the training box and driving true adoption. Because training isn’t just about content. It’s about culture, momentum, and helping people embrace change.

🔎 𝗧𝗵𝗲 𝗜𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗦𝗸𝗶𝗹𝗹 𝗧𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗼𝗿 𝗕𝗿𝗲𝗮𝗸𝘀 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 Most companies think adoption is about the software. It’s not. It’s all about people. The 𝙞𝙣𝙫𝙞𝙨𝙞𝙗𝙡𝙚 𝙨𝙠𝙞𝙡𝙡? It’s the 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝙏𝙀𝘼𝘾𝙃 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆. Too often, SMEs and internal teams are asked to train others but have never been taught how. I've seen both sides of this: helping SaaS teams onboard customers and supporting businesses wrestling with tools nobody wants to use. 𝗧𝗵𝗲 𝘂𝗴𝗹𝘆 𝘁𝗿𝘂𝘁𝗵? The core adoption barrier is human—most SMEs and internal teams aren’t taught how to train. ❗ Here’s what I see daily: 🚫 New tools rolled out, but teams left guessing ⏳ Hours wasted searching for answers—or worse, doing it wrong 😤 Frustration leads to disengagement (and in employees, turnover; in customers, churn) 💸 Smart investments in software end up costing more than they return 🙅♂️ Leadership wonders why “nobody uses our solutions” Sound familiar? But there’s good news: These are 𝗽𝗲𝗼𝗽𝗹𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀, and they’re fixable. ✅ 𝙏𝙝𝙧𝙚𝙚 𝙝𝙪𝙢𝙖𝙣-𝙛𝙞𝙧𝙨𝙩 𝙩𝙖𝙘𝙩𝙞𝙘𝙨 we use to make adoption fast, sticky, and scalable: ✔️ 𝗘𝗺𝗽𝗼𝘄𝗲𝗿 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗰𝗵𝗮𝗺𝗽𝗶𝗼𝗻𝘀: Identify natural influencers and upskill them as trainers, not just users. ✔️ 𝗠𝗮𝗸𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗿𝗶𝗱𝗶𝗰𝘂𝗹𝗼𝘂𝘀𝗹𝘆 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁: Ditch generic demos. Share real, daily workflows—tailored to each team’s needs. ✔️ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀: Adopt a culture where questions are easy, and every answer gets reused (documentation, quick videos). When real adoption happens, your teams (and customers) become your best advocates. Productivity goes up, support tickets down, and your software investment finally pays off. ✋🏼Do you have a team of SMEs that need help delivering effective training? Let’s tackle the people side, together. 🤝🏼Let’s connect. Picture by Gemini #Training, #Onboarding, #Enablement, #LearningAndDevelopment, #ChangeManagement