There's a lot of conversation right now about what AI can do. It can automate tasks, summarize information, generate content, analyze data, and help people get work done faster.
But there's another side of AI adoption that I think deserves just as much attention: the people who are expected to use it. You can introduce the best technology in the world, but that doesn't mean people will automatically know what to do with it. They still have to understand why it's being introduced, how it fits into their work, what's expected of them, and maybe most importantly, feel comfortable enough to actually use it. That's why I think having a background in change management and training is especially valuable right now.
AI is still a change
AI may feel different from other technology transformations, but many of the human reactions to it are familiar. Whenever the way we work changes, people have questions. What does this mean for my job? How is my day-to-day work going to change? What am I responsible for now? What happens if I use the tool incorrectly? Can I trust the information it gives me?
With AI, there's another layer to those questions. People are hearing everything from "AI will make your job easier" to "AI will replace your job." It makes sense that employees might approach it with a mix of curiosity, excitement, skepticism, and concern. That's where change management matters.
Successful adoption isn't just about sending an announcement, hosting a demo, and hoping everyone gets on board. People need context and clear communication. They need leaders who can explain what is changing, why it matters, and what it means for their teams. They also need a way to ask questions and provide feedback as they figure out what AI means for their work. When people are part of the change instead of simply being told that change is happening, they have a better opportunity to understand it and adapt.
Knowing that AI exists isn't the same as knowing how to use it
This is where my training background shapes how I think about AI adoption. There's a big difference between showing someone what a tool can do and helping them become comfortable using it on their own. A demonstration can introduce the possibilities, but it doesn't necessarily prepare someone to use AI effectively in their day-to-day work.
Think about something as simple as prompting an AI tool. You could give someone a list of prompt-writing tips, and that might be helpful, but the real learning happens when they start applying those ideas to their actual work. What information should I give the AI? How specific should I be? How do I evaluate the response? What should I do when the answer isn't quite right? What information should I not put into the tool?
Those aren't questions people learn to answer from a single presentation. They learn through practice, examples, feedback, and support. That's why I think AI training has to go beyond teaching features. We have to help people build confidence and judgment so they understand not only how to use the technology, but how to make good decisions while using it.
Adoption doesn't happen on launch day
One of the biggest lessons I've learned from working in change and training is that launching something is not the same as adopting it. You can have a successful rollout with great communications, strong attendance at training, and plenty of resources, and still discover a few months later that people have gone back to their old ways of working.
AI is no different. In fact, because AI tools are changing so quickly, I think reinforcement becomes even more important. People may need new examples, updated guidance, office hours, manager support, communities of practice, or simply opportunities to share what is and isn't working. The support shouldn't disappear just because the initial rollout is complete.
Organizations also have to listen to what employees are telling them. If people aren't using an AI tool, the answer shouldn't automatically be, "They need more training." Maybe the tool doesn't fit naturally into the workflow. Maybe expectations aren't clear. Maybe people don't trust the output. Maybe managers aren't reinforcing the change. Or maybe employees have found a better way to accomplish the task. Understanding the difference is part of good change management because not every adoption problem is a training problem.
Where change and training professionals fit
I don't see AI reducing the need for change management or training. I see it changing the kind of work we need to do. Instead of simply teaching people where to click, we may be helping them develop judgment. Instead of communicating a one-time system launch, we may be supporting a workplace where tools and processes continue to evolve. And instead of measuring success primarily by training completion, we may need to look more closely at behavior. Are people using the tool? Are they using it appropriately? Is it actually improving performance? Where are people getting stuck?
Those are questions change and learning professionals are already used to asking, and I think that experience gives us something valuable to contribute to the AI conversation. We understand that adoption takes more than access to a tool. It takes communication, learning, reinforcement, feedback, and a willingness to adjust when something isn't working.
Ultimately, AI transformation isn't just about what the technology is capable of doing. It's about whether people understand it, trust it, know how to work with it, and can adapt as it changes. Organizations that recognize the human side of AI adoption will be better positioned to turn new technology into meaningful changes in how work gets done.
