A speaker breaks down classical AI, generative AI, and RPA, then explains why complex prompts fail with tools like GPT-4. He recommends a workshop where teams sort their use cases into the right bucket, and warns that AI adoption works best as part of a broader strategy, not in isolation.
So one thing I believe for everybody to— it's important for everybody to understand, how do we differentiate classical AI, generative AI? And actually, what is also interesting, RPA is robot process automation, or just process automation. In the workshops that I do, I normally ask the people to bring in their ideas about AI use cases, and then it's very interesting for them to understand, okay, we have those 3 buckets, and it might not be AI at all, it might be process automation for the time being. Then if you look at AI agents, that's going to be the way forward potentially, but not yet. Yeah, and also to differentiate what is classical AI, what is generative AI. So, so this, these 3 buckets, um, I, I believe companies and people need to work on, on differentiating those, uh, to really have a better understanding when and how to use AI. That's one. Then basically start using it. That's definitely the second one. I mean, I have it always open in a browser window in terms of when I have questions, when I try to generate new ideas. It's so helpful already, and you kind of start exploring what it can do and what it cannot do. Sometimes I do too complicated requests and it actually breaks down. GPT-4, for example. It's interesting that there are some limits in terms of what it can do also from a complexity point of view, and then you need to formulate it more simple, break it down into different steps. So I think this is just an experience everybody should have. And then as a third item, do it in an ecosystem.
