Training Employees On New AI Technologies

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

🚫 STOP saying: “AI won’t replace you. A person using AI will.” It sounds more like a threat than a strategy. It shuts down the conversation instead of opening it. Because when employees express fear about AI, they don’t need clichés. They need a plan. Show you’re investing in them, not replacing them. Upskilling isn’t just about training. It’s about trust. So don’t just quote the internet. Show them where they fit in and how to grow. Here are 7 ways leaders can actually do that: 1. Start with listening ↳ Let them voice fears and skepticism ↳ Don’t respond with a TED Talk 2. Audit current roles ↳ Identify tasks that could be enhanced (not replaced) ↳ Talk openly about what AI can actually do 3. Invest in AI literacy ↳ Offer bite-sized, low-pressure workshops ↳ Demystify AI without overwhelming your team 4. Create low-stakes practice zones ↳ Let employees test tools with no deadlines ↳ Make it okay to play, learn, and even mess up 5. Celebrate progress, not perfection ↳ Highlight effort, experimentation, and curiosity ↳ Focus less on mastery, more on momentum 6. Pair learning with real work ↳ Show how AI can solve actual small problems ↳ Build skills while building solutions 7. Repeat the message ↳ “You’re part of the future.” ↳ “And we’re building it together.” No trust, no transformation. AI adoption isn’t just strategy, it’s a trust fall. 💬 What’s one step you’ll try with your team? ♻️ Repost if you’re investing in people, not just tech. 👣 Follow Janet Perez for more like this.

ChatGPT is the new Excel. Here’s your first step toward AI. Many companies are racing to adopt AI, but the biggest opportunity often goes unnoticed: empowering your team with AI tools. It’s not just about building new AI products; it’s about integrating AI into the daily workflow of your employees. The tools—like ChatGPT, Claude, and Perplexity—are available, but the knowledge gap is significant. While people experiment with these tools, few companies provide the right training to maximize their value. A well-trained workforce using AI effectively is a game changer. This skill set not only accelerates daily tasks but also builds the foundation for larger AI initiatives. Companies that fail to build this muscle now are not only leaving productivity gains on the table but also signaling to their most innovative employees that they’re not serious about AI. The wrong step? Banning AI tools like ChatGPT. The right step? Training employees on their effective use. Here’s what you need to be doing: 1 — Align on an AI Assistant (ChatGPT, Perplexity, Claude, etc.) Start by choosing one of the key AI assistants—whether it’s ChatGPT, Claude, or Perplexity—or a combination of them. The great news is that all of these now offer enterprise-grade plans that help you manage your teams efficiently. Plus, they come with major certifications like SOC 2, GDPR, CCPA, and CSA Star, ensuring compliance and security for your business. 2 — Make AI Part of Your Team’s Daily Toolkit Make it clear across your company: just as everyone uses a computer, email, PowerPoint, or Excel daily, AI assistants are going to be a prerequisite for everyday work. Part of becoming an AI-powered organization is ensuring these tools are integrated into everyone’s daily routine. 3 — Organize Structured Training Set up a comprehensive training program that teaches your employees how to work effectively with these tools. Focus on prompt engineering, real use cases, and practical examples. Just as important, provide clear guidelines on what not to do—such as entering sensitive IP or customer/employee information—to ensure proper usage and avoid risks. There’s a lot of FOMO out there, and many companies are rushing to figure out how to implement AI projects. But a prerequisite to all of this is having your workforce turbocharged and powered by AI assistants. Whether or not you end up building your own AI-powered features, this will help boost your team’s overall productivity. It will also build the familiarity and intuition your team will need for working with AI-powered services—or vendors who are leveraging this technology. All in all, it’s a win-win: a low-effort, low-cost, easy way to get started with AI adoption and transformation.

𝗪𝗵𝗲𝗻 𝗜 𝘁𝗮𝗹𝗸 𝘁𝗼 𝗖-𝗦𝘂𝗶𝘁𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀, 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝘂𝗻𝗹𝗶𝗸𝗲 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗵𝗲𝗮𝗿 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗔𝗜 𝗯𝘂𝗯𝗯𝗹𝗲. Most companies are just starting to make AI training available to their employees. While some are already a bit further ahead, the majority of leaders are wondering where to start. Here are my two cents in a recent InformationWeek article: Andreas Welsch advocates starting with a “Community of Multipliers”—early tech adopters who are eager to learn about the latest technology and how to make it useful. These multipliers can teach others in their departments, helping leadership scale the training. Next, he suggests piloting training formats in one business area, gathering feedback, and iterating on the concept and delivery. Then, roll it out to the entire organization to maximize utility and impact. “Despite ChatGPT being available for two years, Generative AI tools are still a new type of application for most business users,” says Welsch. “Prompt engineering training should inspire learners to think and dream big.” He also believes different kinds of learning environments benefit different types of users. For example, cohort-based online sessions have proven successful for introductory levels of AI literacy, while executive training expands the scope from basic prompting to GenAI products. Advanced training is best conducted in a workshop because the content requires more context and interaction, and the value comes from networking with others and having access to an expert trainer. Advanced training goes deeper into the fundamentals, including LLMs, retrieval-augmented generation, vector databases, and security risks, for example. From tailored workshops to customizable curriculums, workforce enablement needs to fit your needs. Get in touch to help your workforce use AI tools effectively! #ArtificialIntelligence #GenerativeAI #IntelligenceBriefing

𝗜'𝗺 𝗵𝗲𝗮𝗿𝗶𝗻𝗴 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗯𝗼𝘂𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀. Employees are NOT using it - they don't see the value or don't know how to. And I know exactly why... All fancy AI licenses are worthless because you are: 🚫 Throwing licenses at employees 🚫 Forcing top-down adoption 🚫 Assuming people will "figure it out" 🚫 Focusing only on technology The truth? Having AI isn't enough; effective adoption is key. Here's what successful companies do differently (5Es): ✅ Educate: Show AI capabilities w/ use cases & benefits ✅ Empower: Provide proper training and support ✅ Enable: Create space for experimentation ✅ Engage: Address concerns openly ✅ Execute: Implement clear adoption strategies Here's a 3-step framework that transformed our AI/RPA Automation adoption rates 👇 Start with WHY - Connect AI/Automation to business objectives - Show Organizational & personal benefits - Address replacement fears head-on Enable through HOW - Structured training programs - Hands-on workshops - Real-world use cases Support with WHAT - Clear implementation roadmap - Regular feedback sessions - Celebration of small wins Remember: Having AI isn't enough. Success lies in your people adopting it. What do you think? ---- 🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX #AI #innovation #technology #automation