Why the excitement about AI learning is justified
Everyone is competing to make AI a reality. But in our rush to act fast, are we actually helping people learn, or are we just helping them feel like they have learned? Five years ago, if someone had asked me to explain machine learning, I would have confidently opened three browser tabs, speed-read them, and still silently hoped that no one would ask follow-up questions. Now I can not only understand the basics, but also have my voice heard in real conversations about incorporating AI into learning experiences. The “Personalization Engine” does not run every 5 minutes by default. That change is important to me. a lot. AI has made complex ideas more accessible, more democratic, and far less intimidating for people in a variety of roles, including L&D professionals, facilitators, and business leaders. And I love it. But along with that excitement, I noticed something else. It’s rush. And not necessarily in a thoughtful way.
The AI learning gold rush is real and happening fast
McKinsey & Company reports that AI adoption has more than doubled in recent years. LinkedIn’s Workplace Learning Report highlights AI literacy as one of the most in-demand skill areas around the world. And you can feel it in the field. Every learning deck has an “AI-enabled” slide, where suddenly all tools are “AI-powered” and all teams are encouraged to “learn AI fast.” It’s very exciting. it’s necessary. It’s also a bit chaotic.
When “AI learning” becomes a checkbox
I would like to take a break here. It doesn’t stop, it just pauses. Because somewhere along the line in making AI available to everyone, there is a danger that learning itself becomes an hour-long webinar that everyone attends but few people apply for, or a demo of a tool that pretends to build skills, or the addition of flashy features with no real use case. I’ve seen this pattern before with another buzzword. The intent is correct. Execution hastened. And when that happens, we’re not really building capacity. We become accustomed to the feeling of learning. Knowledge and ability are not the same. And exposure is not the same as application.
What really helped me learn AI
It wasn’t the speed that worked for me. It was the context. Understand where AI really fits into your job. We are conducting experiments using small-scale, low-pressure methods. Look at real examples rather than abstract frameworks. No one handed me a “complete AI learning path” and expected me to follow it linearly. It’s tedious, repetitive, and honestly could have been much more effective. This is exactly why I worry when learning is designed backwards: tools first, context later.
Distinctions that really matter
The World Economic Forum puts it well: The real challenge is not to implement AI concepts at scale, but to meaningfully retrain people at scale. This word does a lot of the heavy lifting in its meaningful sense. Awareness is not an ability. Exposure is not application. Access is not recruitment. These are not only semantic differences. This is the gap between teams that say they’ve done AI training and those that have actually changed the way they work.
So what should we do instead?
Don’t slow down. Don’t shy away from AI. Absolutely not. But perhaps you should reframe your first question. Start with the problem, not the tool. Before deploying AI capabilities, ask yourself, “What are we actually trying to solve?” Tools are the answer, not the starting point. Design with relevance in mind. Customer support executives and learning designers don’t need the same AI training. One size rarely fits everyone perfectly. Keep it human. Ironically, the more human the learning experience feels, the more likely AI adoption will actually stick. Just because there’s a compelling demo doesn’t mean people will change the way they work. They change because it makes sense to them. And finally, create space for experimentation. Learning AI shouldn’t feel like passing an exam. With enough psychological safety, you should be willing to try something, fail a little, and try again.
where you landed
I’m still learning more towards AI. If anything, more so than ever. Because we’ve seen what happens when it’s done well, when someone says, “Do you think machine learning is something that uses data?” “Here’s how you can actually use this in your learning strategy.” It’s not perfect. But seriously. That’s the point. Not everyone needs to become an AI expert overnight. All we need is for them to become thoughtful and confident users of it.
It’s not a bad thing for AI to learn Gold Rush. It means people care. It means we are moving forward. But if you’re not careful, you can end up with too much activity and not enough actual skills. So perhaps the question is not, “How fast can we scale AI learning?” It’s “How well are we helping people actually use it?”
