How to Train Employees on AI Tools

Somebody has to say it: some AI tools are causing more harm than good. Not because the technology is bad. Not because people are resisting change. But because we keep rolling out tools without guidance, training, or context and calling it “innovation.” When employees are expected to figure it out on their own, confusion replaces confidence. Work slows down. Trust erodes. AI at work doesn’t fail loudly. It quietly creates friction when enablement is missing. If we want better outcomes, we have to design for adoption, not just deployment. If you’re rolling out AI at work and want it to actually help, here’s a simple place to start: 1. Start with the “why,” not the tool ✅ Be clear about the problem AI is meant to solve. Productivity, quality, speed, decision-making. If people don’t understand the purpose, they won’t trust the tool. 2. Define when and when not to use it ✅ Ambiguity creates hesitation. Give real examples of appropriate use cases and clear boundaries so employees aren’t guessing. 3. Train for workflows, not features ✅ Skip the generic demos. Show how the tool fits into existing day-to-day work, step by step. 4. Equip managers first ✅ If managers can’t explain or model usage, adoption stalls. Enable leaders before expecting teams to follow. 5. Build feedback loops early ✅ Create space for questions, friction, and adjustments. Early feedback prevents quiet frustration from turning into resistance. 6. Treat adoption as ongoing, not a launch event ✅ AI enablement isn’t a one-time rollout. It’s reinforcement, iteration, and support over time. AI works best when people feel prepared, not pressured. ——— ✦ ——— 🌱 More on AI + Workforce Development → Janet Perez

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.

Everyone’s talking about AI adoption. But not enough are asking: “Are people actually ready to use it—well?” 72% of employees now say they’re using AI regularly. But when you zoom in on the frontline? Just 51%. That's what BCG calls the "Silicon Ceiling"— Interest is high, tools exist… But value? Still stuck at the surface. Here’s why AI often flops after launch: 🧱 36% feel untrained 🧰 37% don’t have the right tools 🧭 Only 25% feel supported by leadership What happens next? Over half turn to unauthorized AI tools (54%) to get their work done. That’s a compliance nightmare waiting to happen. What actually works? ✅ Coaching. ✅ Real training. ✅ Leadership that leads. Just 5 hours of focused support drives real ROI: 📈 +19pp in AI adoption 📈 +14pp with access to a coach 📈 +12pp from in-person learning And the biggest unlock? When leaders actively back AI: Confidence jumps from 15% → 55% If you're a leader, this is your roadmap: 1. Measure what matters – Track AI impact on productivity, quality, satisfaction. 2. Don’t DIY your training – Build formal pathways, not one-off workshops. 3. Support your people – Tools are useless without workflows and skills. 4. Experiment fast, fail safe – A/B test AI agents to learn quicker, with less risk. Here’s the real truth: AI won’t transform your business. Your people will. But only if they’re trained, guided, and empowered. If you’re done chasing AI hype and ready to build real capability— Let’s talk. 👉 This post is inspired by recent insights from BCG, June 2025. ♻️ Share this with your network if it resonates. ☝️ And follow Stuart Andrews for more insights like this.

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.

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.

Stop Treating AI Like a Tool, Start Onboarding It Like a Teammate! 🚀 Are you struggling to get real value from AI in your team? The problem might not be the technology, but how you're integrating it. Just like a new hire, AI needs clear roles, training, and ongoing feedback to truly thrive. : * Define clear responsibilities: What specific tasks will the AI handle? * Invest in "AI literacy": Everyone on the team needs to understand AI's capabilities and limitations. * Establish communication protocols: How will the AI share its insights and when will it need help? * Provide continuous training and feedback: Help the AI learn and improve, just like you would with any team member. * Foster collaboration and trust: Encourage teamwork between humans and AI. * Iterate and adapt: Be flexible and adjust your approach as the AI evolves. * Address ethical considerations: Be mindful of bias and ensure fairness. The key takeaway? Treat AI as a partner, not just a tool. Build a collaborative environment where AI can flourish, and you'll unlock its true potential.

𝗪𝗵𝘆 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗥𝗼𝗹𝗹𝗼𝘂𝘁𝘀 𝗨𝗻𝗱𝗲𝗿𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 As organisations rapidly deploy Generative AI tools across the enterprise, one assumption shows up again and again: → 𝗜𝗳 𝘄𝗲 𝗽𝗿𝗼𝘃𝗶𝗱𝗲 𝘁𝗵𝗲 𝘁𝗼𝗼𝗹𝘀, 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗶𝗹𝗹 𝗳𝗶𝗴𝘂𝗿𝗲 𝗼𝘂𝘁 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗵𝗲𝗺 →↳ 𝗧𝗵𝗮𝘁 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗶𝘀 𝗰𝗼𝘀𝘁𝗹𝘆 Gen AI rarely underdelivers because of the technology. It underdelivers because users are never taught how to communicate with it effectively. Most organisations don’t struggle with access to AI. They struggle with 𝗶𝗻𝗽𝘂𝘁 𝗾𝘂𝗮𝗹𝗶𝘁𝘆. 𝗧𝗵𝗲 𝗨𝗻𝗱𝗲𝗿𝗮𝗽𝗽𝗿𝗲𝗰𝗶𝗮𝘁𝗲𝗱 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Gen AI can: → Automate routine tasks → Support analysis and decision-making → Accelerate content creation and ideation But many rollouts skip a foundational capability: → B𝗮𝘀𝗶𝗰 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘀𝗸𝗶𝗹𝗹𝘀 Employees are expected to: → Know how to frame questions → Provide the proper context → Guide outputs toward business-ready results Without training, they don’t. 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 Prompt engineering is not an advanced or technical niche. It is a 𝗰𝗼𝗿𝗲 𝘄𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲 𝘀𝗸𝗶𝗹𝗹. When prompts are vague, incomplete, or poorly structured: → Outputs are shallow → Results are inconsistent → Trust in the tool erodes In other words: → 𝗴𝗮𝗿𝗯𝗮𝗴𝗲 𝗶𝗻 →↳ 𝗴𝗮𝗿𝗯𝗮𝗴𝗲 𝗼𝘂𝘁 𝗧𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗦𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 When Gen AI is rolled out without prompt literacy: → Employees spend time fixing poor outputs → Teams abandon tools after early frustration → Productivity gains never materialise The result is predictable: → Licensed tools →↳ Limited adoption →↳ Minimal ROI What should be a force multiplier becomes shelfware. 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗪𝗼𝗿𝗸𝘀 Organisations seeing real value take a different approach: 𝗕𝗮𝘀𝗶𝗰 𝗣𝗿𝗼𝗺𝗽𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 → How to structure requests → How to iterate and refine → How to validate outputs 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 → tied to real workflows → not generic demos 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 → AI as a collaborator →↳ not an autopilot 𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 The real question is no longer: → “𝗛𝗮𝘃𝗲 𝘄𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗚𝗲𝗻 𝗔𝗜?” It is: → “𝗛𝗮𝘃𝗲 𝘄𝗲 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝘂𝗿 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁 𝘄𝗲𝗹𝗹?” Gen AI doesn’t create an advantage on its own. Skilled users do. If this resonates, tap 👍, follow for more practical AI adoption insights, and share ♻️ your perspective. #GenerativeAI #AIAdoption #PromptEngineering #FutureOfWork #DigitalTransformation #WorkplaceAI #AITraining #Leadership #EnterpriseAI #Productivity #AIStrategy

In a world where AI announcements seem to drop every 15 minutes (seriously, it’s so hard to keep up), I've been reflecting on what actually matters beyond the hype. As a people leader navigating this landscape, I've learned that the challenge isn't just adopting AI tools quickly—it's adopting them thoughtfully. This is especially important at HubSpot, where helping our employees move faster helps our customers win faster. I'm seeing AI reshape not just what we do, but how we make decisions and prioritize our people. Here are some approaches that have worked well for us as we continue to test and learn: 1. Expedite access to AI tools and encourage experimentation. We're experimenting with the latest versions of Claude, Gemini, ChatGPT, and more—providing teams access within hours of new releases, not weeks. This creates a culture of experimentation and keeps us ahead of the curve. 2. Foster knowledge-sharing. We've created dedicated channels where employees share their AI wins and habits. Our People team sends a weekly "MondAI" digest featuring different employee use cases that inspire others across the organization. 3. Prioritize leader enablement. We've built AI-first resources, starting with People Leaders who then cascade knowledge to their teams. This isn't just about tools—it's about developing judgment for when AI enhances human work and when human expertise should lead. 4. Seek external expertise. We regularly bring in experts from companies like Anthropic and Google to share insights with our teams. We've cultivated a culture of learn-it-alls, not know-it-alls. 5. Integrate AI into existing workflows. We're incorporating AI tools directly into team processes, focusing on high-impact, repetitive tasks first. Our AI support bot now handles over 35% of tickets while maintaining high customer satisfaction. The most exciting part? Watching our teams develop the discernment to make AI work harder for them, not the other way around. When people and technology make each other stronger—that's the sweet spot. Fellow people leaders: How are you balancing rapid AI adoption with thoughtful implementation that truly empowers your people? Other insights we can learn from?

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.

63% of employees say their company’s AI training isn’t good enough, according to TalentLMS. That’s a call to action. As AI adoption accelerates, one of the most impactful steps leaders can take is preparing teams to use these tools with confidence, and those who get this right design training as a catalyst to spark new ways of working. The most effective training programs I’ve seen share three qualities: they’re role-based, hands-on, and ongoing. 1️⃣ Role-based training helps AI adoption stick. When employees leave with three or four clear ways to apply AI immediately, those practices are far more likely to become part of daily work. 2️⃣ Hands-on beats hypothetical. Confidence grows fastest when instruction is concise and paired with time to experiment in low-risk settings. Learning by doing makes adoption real. 3️⃣ Training isn’t one-and-done. Quarterly or biannual sessions, with updates as tools or capabilities evolve, help teams feel supported and ready to keep pace. When training is structured this way, employees feel empowered to use AI, and that’s when it starts to truly transform how work gets done. #AITraining #AIEnablement #LearningAndDevelopment #EmployeeTraining #Upskilling