So let's go to that topic next and say, okay, where is the main differences between the embedded AI and the bolt-on? Where is it that you really either get into trouble or get a lot of benefits in terms of the embedded AI?

Yeah.

So you could say one thing is how we instruct it.

So when you open up and you start a question, if you do it in a general model like OpenAI or Cloud, etc., you'll need to prompt it to say, what is it I want to talk about today? Because it doesn't know whether you want to write an email about postponing a meeting or whether you want to explore a new market opportunity or something like that.

So the first part is how it's instructed and what is the context.

And having it embedded in the IPP solution, for example, if you are in a forecast, you're working with your forecast, it's quite obvious that then we can instruct the LLM to know that, okay, what you probably want to know when you're looking at your forecast overview is you want to understand what is the new forecast, how is it different from the forecast we had from last cycle, what are the different market intelligence, the inputs that we have provided, etc.

And what are the different dimensions that would be interesting and relevant to analyze? What have you previously found interesting? And where could we find those highlights? And there you can see you're a lot more likely in the embedded version here to get a good answer to your question than if you start completely from scratch in prompting your AI.

When is that the right choice? Yeah,