Developing Training for New Technologies

During our #AIAdoption workshop this week, we completed a real-time #ADKAR Force Field Analysis. Here are the top forces restraining #Awareness of the need “to competently and confidently integrate AI to do my work more efficiently and effectively.” 🚧 Awareness Restraining Forces: 1. Lack of understanding & misconceptions – Limited awareness and mixed messages about AI leave people unsure what it is or why it matters, stalling engagement. 2. Unclear personal value & use cases – When individuals can’t see “what’s in it for me,” new tools feel irrelevant, risky, or not worth the effort. 3. Fear & uncertainty – Anxiety about job impact, mistakes, or the unknown discourages exploration and experimentation. 4. Competing priorities & time pressure – Attention battles and overloaded calendars crowd out bandwidth to learn something new. 5. Scattered focus – General distraction (“What should I pay attention to today?”) that dilutes momentum. 6. Data & security concerns – Worries about data misuse or weak enterprise safeguards make people pump the brakes. 7. Pace of change & where to start – Rapid evolution leaves learners paralyzed by “too much, too fast” and unclear first steps. 8. Lack of strategy & direction – Perceived “dabbling” without a clear plan erodes trust and signals low commitment. 🚧 Which of these restraining forces are impeding your AI progress? 🧗♂️ What can you do about it? Well, here are three suggestions from Prosci's AI Assistant #Kaiya on better next steps: 1. Develop Targeted Educational Campaigns Create workshops or webinars that demystify AI, emphasizing its relevance and benefits to employees. Tailor content to address misconceptions and present relatable use cases that highlight personal value. This approach tackles the lack of understanding (1) and unclear personal value & use cases (2) by providing concrete examples of how AI can enhance daily tasks and improve job performance. 2. Establish a Structured Learning Pathway Design a structured learning pathway with bite-sized training modules and clear first steps for AI integration. Incorporate flexible scheduling to accommodate busy calendars and competing priorities. This roadmap addresses the pace of change & where to start (Point 7) and competing priorities & time pressure (Point 4), easing anxiety about AI adoption by breaking down the learning process into manageable parts. 3. Foster a Culture of Open Communication and Support Create forums for employees to voice concerns about data security, job impact, and other fears. Pair this with transparent communication from leadership regarding the AI integration strategy and safeguards for data and roles. This initiative addresses fear & uncertainty (Point 3) and data & security concerns (Point 6), fostering trust and support. Open communication clarifies the organization’s commitment to a thoughtful approach to AI, enhancing engagement and reducing resistance. Contact Prosci for AI Adoption support.

35 people. 3 workshops. 90% adoption. We just wrapped up a training series for a new customer. They wanted to go beyond demos and get their hands dirty. To take them further, we adapted our Bootcamp Series and ran a custom workshop track for their team of 35. Here’s what we did: Workshop 1: Demystify AI Set the foundation. How AI works, what it’s good at, where it’s risky. Workshop 2: Prompting & Bot Building Merged our prompting and GPT builder sessions. Everyone built bots. Workshop 3: Automation & Agents New session focused on AI at work...building automations to scale AI. That was just part of it. As part of our engagement model, we also met weekly with their leadership team. Talked trends. Showed live demos. Explored practical use cases. Results? 4.4/5 --> confidence using tools like ChatGPT 4.4/5 --> better understanding of how to apply AI at work 90% --> of the team using AI in new ways just weeks after training What they told us mattered most: - "Building GPTs together, having something we could use at the end." - "Learning how to prompt properly. The lightbulb finally went off." - "Understanding personas and tone really helps get quality outputs." - "Automation tied it all together with actual process improvements." And here’s how we can improve: - Break the sessions into shorter blocks. - Give more structure around automation tools. - Add more “what to do when it doesn’t work”. - Create more space for small group hands-on time. This was one of our favorite engagements to date. When coming to us, this company had a clear appetite to learn AI. Now, they have real momentum. If you're looking to do something similar, then get in touch! [This post was Human Generated, Human Approved]

𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗬𝗼𝘂𝗿 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 📚 Creating a training program is just the beginning—measuring its effectiveness is what drives real business value. Whether you’re training employees, customers, or partners, tracking key performance indicators (KPIs) ensures your efforts deliver tangible results. Here’s how to evaluate and improve your training initiatives: 1️⃣ Define Clear Training Goals 🎯 Before measuring, ask: ✅ What is the expected outcome? (Increased productivity, higher retention, reduced support tickets?) ✅ How does training align with business objectives? ✅ Who are you training, and what impact should it have on them? 2️⃣ Track Key Training Metrics 📈 ✔️ Employee Performance Improvements Are employees applying new skills? Has productivity or accuracy increased? Compare pre- and post-training performance reviews. ✔️ Customer Satisfaction & Engagement Are customers using your product more effectively? Measure support ticket volume—a drop indicates better self-sufficiency. Use Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT) to gauge satisfaction. ✔️ Training Completion & Engagement Rates Track how many learners start and finish courses. Identify drop-off points to refine content. Analyze engagement with interactive elements (quizzes, discussions). ✔️ Retention & Revenue Impact 💰 Higher engagement often leads to lower churn rates. Measure whether trained customers renew subscriptions or buy additional products. Compare team retention rates before and after implementing training programs. 3️⃣ Use AI & Analytics for Deeper Insights 🤖 ✅ AI-driven learning platforms can track learner behavior and recommend improvements. ✅ Dashboards with real-time analytics help pinpoint what’s working (and what’s not). ✅ Personalized adaptive training keeps learners engaged based on their progress. 4️⃣ Continuously Optimize & Iterate 🔄 Regularly collect feedback through surveys and learner assessments. Conduct A/B testing on different training formats. Update content based on business and industry changes. 🚀 A data-driven approach to training leads to better learning experiences, higher engagement, and stronger business impact. 💡 How do you measure your training program’s success? Let’s discuss! #TrainingAnalytics #AI #BusinessGrowth #LupoAI #LearningandDevelopment #Innovation

Then the CEO said to me, “Why the hell didn't we do this sooner?” I was working with a company on their AI strategy and noticed some weird vibes across the board with senior leadership. Something wasn’t being said… Turns out there was still massive apprehension about whether AI would actually be useful. They had read all the headlines... We had skeptics everywhere except one person willing to drive things forward and they were banging their head against the wall. They'd started with a smart knowledge agent on top of their documents for one department. It was going well, but they wanted more adoption and to help people see other opportunities. So we said, "Let's run a workshop to educate and demystify the tech. A safe space for the execs." First thing we learned? Tons of people tried AI once or twice in their personal lives but abandoned it. It never became part of daily usage. Our entire workshop was done using ChatGPT. We did brainstorming, synthesis, analysis—all of it within the tool. Eventually, an executive stood up and said, "This thing's incredible. I think it's going to take my job!" They were half-joking, but the message was clear: "This is WAY better than I thought." Next, we expanded their knowledge agent’s capabilities, giving them access to company data through AI. Suddenly, I'm getting emails about how much time it's saving them. "Game changer," they said. The conversation shifted instantly from "This seems risky" to "How do we go faster?" I've seen this pattern repeatedly with individuals, departments, and entire companies. There's something powerful about play when new technologies emerge. When you give people permission to just play with it—see what it can do—their minds open to possibilities they couldn't see before. This wasn't formal training on prompt engineering. This was opening minds. Once that opening happens, your job is to use that momentum to craft vision, strategy, and plans for organizational impact. The challenge goes far beyond tech. For many employees, AI feels threatening to their identity and self-worth. Anything that seems like it can do their job creates immediate resistance. Most people don't automatically think, "Wow, look at all this cool stuff I could be doing instead!" They think about what could go wrong. It's through play—through freedom of exploration—that technology starts to feel less threatening and more full of possibility. Create these environments first, then add formal training and evangelism. The most successful programs aren't pushed onto people. They're pulled through by people who've discovered the possibilities themselves. My advice to all of you out there who want to unlock the power of AI in your organization? Let them play. Image credit: Midjourney, "A funny hyper realistic photo of a workshop with executives playing like little kids"

Did you know that 92% of learning leaders struggle to demonstrate the business impact of their training programs? After a decade of understanding learning analytics solutions at Continu, I've discovered a concerning pattern: Most organizations are investing millions in L&D while measuring almost nothing that matters to executive leadership. The problem isn't a lack of data. Most modern LMSs capture thousands of data points from every learning interaction. The real challenge is transforming that data into meaningful business insights. Completion rates and satisfaction scores might look good in quarterly reports, but they fail to answer the fundamental question: "How did this learning program impact our business outcomes?" Effective measurement requires establishing a clear line of sight between learning activities and business metrics that matter. Start by defining your desired business outcomes before designing your learning program. Is it reducing customer churn? Increasing sales conversion? Decreasing safety incidents? Then build measurement frameworks that track progress against these specific objectives. The most successful organizations we work with have combined traditional learning metrics with business impact metrics. They measure reduced time-to-proficiency in dollar amounts. They quantify the relationship between training completions and error reduction. They correlate leadership development with retention improvements. Modern learning platforms with robust analytics capabilities make this possible at scale. With advanced BI integrations and AI-powered analysis, you can now automatically detect correlations between learning activities and performance outcomes that would have taken months to uncover manually. What business metric would most powerfully demonstrate your learning program's value to your executive team? And what's stopping you from measuring it today? #LearningAnalytics #BusinessImpact #TrainingROI #DataDrivenLearning

⏰ AI Governance – A Time for Change⏰ Implementing and maintaining compliance with an Artificial Intelligence Management System (#AIMS) is transformative. It reshapes workflows, accountability, and decision-making, but challenges can extend beyond deployment. Sustaining compliance requires consistent employee engagement, skill development, and adaptation to evolving standards. The #ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement) is a proven framework for managing individual transitions. Combined with #ISO10020, which provides structured change management practices, these tools guide organizations through both building and sustaining adherence to an AIMS. ➡️ Challenges in AIMS Implementation and Compliance 🧱 Employee Resistance: Teams may distrust AI systems or resist workflow changes required for compliance. 🛑 Skill Gaps: Maintaining compliance demands ongoing proficiency in monitoring and improving AIMS operations. ⚙️ Process Overhaul: Adherence often requires rethinking workflows and embedding accountability structures. ⚖️ Accountability and Ethics: Sustained compliance requires transparency and alignment with organizational values. These issues necessitate strategies addressing both human and operational challenges. ➡️ How ADKAR and ISO10020 Facilitate Compliance 1️⃣ Awareness: Establishing the Why ISO10020 highlights the importance of clear communication, while ADKAR ensures individuals understand the need for change. ⚠️ Challenge: Employees may question the effort required for AIMS compliance. 🏆 Solution: Communicate how compliance is both a safeguard and a foundation for ethical AI. 2️⃣ Desire: Encouraging Engagement Long-term compliance requires sustained commitment. ⚠️Challenge: Employees may disengage if they see compliance as burdensome. 🏆 Solution: Highlight how compliance simplifies workflows, builds trust, and safeguards integrity. Share success stories to inspire buy-in. 3️⃣ Knowledge: Building Competency ISO10020 emphasizes training plans, while ADKAR focuses on equipping individuals with role-specific skills. ⚠️Challenge: Teams may lack expertise to manage compliance or respond to audits. 🏆 Solution: Offer ongoing training tailored to roles, covering regulatory updates and compliance practices. 4️⃣ Ability: Supporting Skill Application ADKAR emphasizes practice, and ISO10020 focuses on interventions to remove barriers. ⚠️Challenge: Teams may struggle with consistent application of compliance requirements. 🏆 Solution: Establish actionable workflows and assign compliance champions to provide guidance. 5️⃣ Reinforcement: Sustaining Compliance Both frameworks stress the importance of monitoring and iterative improvement. ⚠️Challenge: Without follow-up, teams may lapse in compliance adherence. 🏆 Solution: Use tools like dashboards and change matrices to track progress. Celebrate successes and refine processes based on feedback. A-LIGN Prosci Tim Creasey #TheBusinessofCompliance Harm Ellens

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.

5,800 course completions in 30 days 🥳 Amazing! But... What does that even mean? Did anyone actually learn anything? As an instructional designer, part of your role SHOULD be measuring impact. Did the learning solution you built matter? Did it help someone do their job better, quicker, with more efficiency, empathy, and enthusiasm? In this L&D world, there's endless talk about measuring success. Some say it's impossible... It's not. Enter the Impact Quadrant. With measureable data + time, you CAN track the success of your initiatives. But you've got to have a process in place to do it. Here are some ideas: 1. Quick Wins (Short-Term + Quantitative) → “Immediate Data Wins” How to track: ➡️ Course completion rates ➡️ Pre/post-test scores ➡️ Training attendance records ➡️ Immediate survey ratings (e.g., “Was this training helpful?”) 📣 Why it matters: Provides fast, measurable proof that the initiative is working. 2. Big Wins (Long-Term + Quantitative) → “Sustained Success” How to track: ➡️ Retention rates of trained employees via follow-up knowledge checks ➡️ Compliance scores over time ➡️ Reduction in errors/incidents ➡️ Job performance metrics (e.g., productivity increase, customer satisfaction) 📣 Why it matters: Demonstrates lasting impact with hard data. 3. Early Signals (Short-Term + Qualitative) → “Small Signs of Change” How to track: ➡️ Learner feedback (open-ended survey responses) ➡️ Documented manager observations ➡️ Engagement levels in discussions or forums ➡️ Behavioral changes noticed soon after training 📣 Why it matters: Captures immediate, anecdotal evidence of success. 4. Cultural Shift (Long-Term + Qualitative) → “Lasting Change” Tracking Methods: ➡️ Long-term learner sentiment surveys ➡️ Leadership feedback on workplace culture shifts ➡️ Self-reported confidence and behavior changes ➡️ Adoption of continuous learning mindset (e.g., employees seeking more training) 📣 Why it matters: Proves deep, lasting change that numbers alone can’t capture. If you’re only tracking one type of impact, you’re leaving insights—and results—on the table. The best instructional design hits all four quadrants: quick wins, sustained success, early signals, and lasting change. Which ones are you measuring? #PerformanceImprovement #InstructionalDesign #Data #Science #DataScience #LearningandDevelopment

In VR enterprise, we need workforce aligned simulations that actually change how people show up at the bedside. Here’s what separates VR that “wows” from VR that works: ⇉ Start with the strategy, not the headset. Effective simulation training is not about using the latest tech. It is about solving real clinical problems. When the learning objectives are clear, VR becomes a tool for transformation, not just engagement. ⇉ Design for real-world decision-making. Great VR does not just look like a hospital. It feels like one. It replicates pressure, uncertainty, and workforce reasoning in a safe-to-practice environment, giving learners the mindset and muscle memory to act with confidence. ⇉ Competency is the outcome, not just completion. It is not about ticking a box. It is about building a workforce who are ready for the field. This is twice as important for healthcare training. Clinical simulations should assess, reinforce, and scale core competencies tied to better patient outcomes. Let’s stop asking, “How do we make this immersive?” Let’s start asking, “How do we make this impactful?” VRpatients #PhysioLogicAI #nursing #nurse #simulation #VR #MR #XR #AI #VRpatients