How to Foster Curiosity About AI Technologies

Want to make AI feel less intimidating? Start where humans learn best—through curiosity and play. When I was earning my teaching degree, I spent an entire semester exploring how play drives deeper learning at every age. That lesson is just as relevant today. Instead of rigid training sessions, what if we encouraged our teams to tinker? To have fun as they learn and develop comfort with something new? 👉Ask AI a wild question just to see what it does 👉Rewrite an answer in three tones: serious, sassy, Shakespearean 👉Keep iterating until something clicks - then review what worked Play builds fluency, trust, and confidence—and when leaders play alongside their teams, culture strengthens and innovation accelerates. Aren't these things we should all be seeking? AI isn’t a switch you flip; it’s a sandbox to explore. What’s the most playful AI prompt you’ve tried recently? (My fave is "You are my drunk bestie. Please explain what I do!" This works best if you use your AI as a frequent collaboration partner!)

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

Being a research junkie (especially research on workplace and use of tech), the Microsoft The New Future of Work Report is chocked full of insights about how AI will impact the workplace. I've been following Jaime Teevan who been sharing tidbits from the report on a regular basis. I started at the end of the report, reading the recommendations and one caught my eye. "Lead like Scientist" -- especially the process of developing a hypothesis, doing an experiment to test it - and I would add documenting and reflecting on it. We need to be experimenting with the technology, especially lower risk projects, that help us learn how to integrate into workflow. It is about taking the time to be curious, which can be hard for nonprofits when there is limited time and resources and so much to do. While experimentation may seem time-consuming or extra step, the long-term benefits of integrating AI responsibly and effectively can significantly impact your productivity and ultimately your nonprofit's impact. Getting started is simple, but it requires a mindset change from busyness to learning. My AI for work learning process: 1) Follow key AI researchers who are looking at AI in learning, education, nonprofits, and workplace as it fits with my professional area. 2) Use LLM as provocateur to help think creatively about the practical application to my work 3) Experiments related to my own and typical nonprofit staff workflows: Hypothesis, question, test, reflect and document. [Also doing a lot of testing] 4) Share insights on my blog and/Linked and via trainings and speaking. What is your learning process? Are you ready to lead like scientist #futureofwork #ai #ai4good #nonprofits #leadership https://lnkd.in/gqQVC65M

Get curious! Last staff meeting I gave my team at Duke Innovation and Entrepreneurship an assignment--explore how generative #ai tools can increase our #productivity. Everyone presented their best use case. Here's what we learned... 🤖It's not perfect. Don't expect to turn 100% of a task over to (the current version) of ChatGPT; you still need to apply your knowledge and cross-check the output (hallucinations do happen). But, don't throw the baby out with the bathwater. There's a lot of efficiency that can be gained, so explore and see where there's value add. 🤖Tools can help you stimulate new ideas and serve as a creative partner. For example one of our team members asked for ideas for the name of a program. There were several suggestions that were obvious or overused but there were also a couple no one on our team had ever thought of and we will certainly consider mixing and matching the ideas in our marketing this year. 🤖Tools can help you get 80% of the way there but you'll need to personalize it. For example, one team member used a generative AI tool to the narrative of a new teaching case study while another draft logo ideas for a new program. Both needed additional work and changes, but taking advantage of getting 80% of the way there will make each of us (and collectively our team) more efficient. 🤖One team member even used ChatGPT as a technical consultant, having it write a script that would connect our Airtable database to another database for real-time data integration. Brilliant. Q: What was the reason I devoted an hour of our team time to this exploration? A: I wanted to replace the fear (or avoidance) of AI with #curiosity. One team member even vocalized their concern about the potential of AI to replace their function, but noted that after playing around with it, they found the tool to be a great complement to the work they are doing. Success 💥 What are you doing to push your teams to be curious learners about using AI to drive organizational potential? https://lnkd.in/g9QPBYxc