How to Upskill Your Workforce for AI

I’m excited to be filming my new Udemy course on “AI for People Managers” aimed at folks who aren’t necessarily AI experts but want to help their teams use AI ethically and effectively. The great Allie K. Miller suggests that you encourage your people to experiment with AI for ~10 hours a week. This means you have to do more than offer begrudging permission. You need to demonstrate curiosity and excitement— even if you’re still learning too. Here are ten things people managers should know about AI experimentation: 1. Set clear rules upfront about what data your team can and can’t feed into AI tools, because nothing kills an AI experiment faster than a data privacy violation. 2. Frame AI as your team’s new super-powered assistant, not their replacement, so people get excited about what they can accomplish rather than worried about their jobs. 3. Start small with low-risk experiments like brainstorming or first drafts, because you want people building confidence with AI, not stress-testing it on your most important projects. 4. Make it totally okay for people to share when AI gives them weird or unhelpful results, since learning what doesn’t work is just as valuable as discovering what does. 5. Teach your team that getting good AI results is all about asking good questions, and yes, “prompt engineering” is now a legitimate workplace skill worth investing in. 6. Always have someone double-check AI outputs before they go anywhere important, because even the smartest AI can confidently give you completely wrong information. 7. Keep an eye out for AI responses that might be unfair to certain groups of people, since these tools can accidentally bake in biases that you definitely don’t want in your work. 8. Let AI inform your team’s decisions but never make the final call itself, because human judgment still needs to be the ultimate decision-maker. 9. Stay curious about new AI developments and limitations because this technology changes faster than your smartphone updates, and what’s true today might not be tomorrow. 10. Track more than just “how much time did we save” and also measure whether people are actually doing better, more creative work with AI as their sidekick. Let me know if you’re as excited about this topic as I am (and yes, I am learning alongside you too)! #ai #leadership #managers

If you're a leader looking to leverage AI, this read is for you. I recently discussed AI with a CEO, and this was their dilemma: "We don’t know what we don’t know. Where do we start?" Here was my response: Start soon, and follow this plan: 1 — Educate and Empower Your Team 2 — Identify Use Cases 3 — Prototype Your Best Ideas 4 — Deploy and Integrate I have found this approach to be the most effective starting point. It doesn't guarantee success, but it makes it more likely. A little more about Step 1 — Educate and Empower Your Team I like to break this down into three levels: A. Executives must be able to articulate a vision for how AI will enable the company to succeed. B. Mid-level managers must be able to identify, approve, and manage AI initiatives. C. Front-line players must be proficient in using AI tools. To maximize value and impact, it’s helpful to provide industry context. I like to do this with industry case studies, company projects, news, and insights into competitor products and initiatives. When I’ve tried to bypass "education," I’ve encountered poor results downstream, and the actual value generation from AI solutions was compromised. I believe "education" helps teams a) make informed decisions and b) foster the right long-term investing mindset to make AI work. But that’s just my hypothesis.