This is (basically) how voice assistants are structured today, and the results are... lacking. The core issue here is that you're still modeling this like a RPG

This is (basically) how voice assistants are structured today, and the results are... lacking. The core issue here is that you're still modeling this like a RPG conversation tree - which is inherently limiting. You can use AI to help beef up the breadth of possible scenarios supported, but ultimately it's still a conversation tree. Take two very plausible real-life conversations that can be had. We'll start with one that's a bit more mechanical and domain-limited: A: "Can I get two tickets to the 8:30 showing of Avatar?" B: "I'm sorry, but that showing is fully sold out. Would you like the 9:30 showing?" A: "No that's too late, what else is showing around that time?" B: "There is a showing of M3gan that starts at 8:40, is that ok?" A: "Yeah, I'll have two tickets for that." So in this case the customer went off-script. Your chatbot may be designed to predict intent and walk through a script - a well-edited and wide script, but the user can walk off the script at any time - in this case making a wholly separate query unrelated to the original ticket purchase. Importantly also that the results of the query are then used to re-contextualize the original intent. This is a very normal conversation that practically no humans would have trouble with - but in a conversation tree-type model breaks very badly. Now let's take it up another level: A: "Can I get two tickets to the 8:30 showing of Avatar?" B: "I'm sorry, but that showing is fully sold out. Would you like the 9:30 showing?" A: "No that's too late, what else is showing around that time?" B: "There is a showing of M3gan that starts at 8:40, is that ok?" A: "I need something kid-friendly unfortunately. What do you have on that front?" B: "Puss in Boots would be good if your kid like animated movies, there's a showing a little before, at 8:20. Is that ok?" A: "Yes, two tickets please." So in this case the user is expecting some type of situational intelligence that merges two areas (ticket buying, movie information and recommendations) and where references to each area must flow smoothly back and forth and inform each other. This level of dynamism is practically impossible to model using conversation tree-type approaches (even AI-assisted conversation trees, which have wider coverage but the same rigid structure). This is a good example of where current approaches to the problem space produce very rigid, mechanical conversation agents that require the user to have deep knowledge of the (expected) conversation structure, which inhibits discovery of new capabilities and also is intimidating for novices who have not yet internalized the expected scripts.