AIs are already trained on huge corpuses of natural human communication, but they still aren't able to reliably analyze human language. Adding a few more gigabytes of chat logs isn't going to do much.
Bot reads "What's the temperature like near me?"
Person calls "$get user-local-temperature"
API responds "{temperature:{f:77},{c:99}}"
Human writes "It's 77 degrees outside!"
Bot reads "What's it like outside?"
Person calls "$get user-local-weather"
API responds "{weather:{now:Sunny},{today:Cold}}"
Human writes "It's sunny now, but will be cold later today."
There's no serious corpus yet for that -- if this is real, it is important work.
It doesn't need to see "What|WP 's|VBZ it|PRP like|IN outside|IN ?|."
Just "What's it like outside?", and know that it will always respond to that with the same call.
Warning: This is mere conjecture.
The data around how users interact will also be important:
Do they prefer a back and forth conversation, or do they want to say everything in one go.
Do they want to start a conversation, drop it, come back to it several hours later, or do they like completing it in one go.
How do they handle switching back and forth between different contexts, if certain requests take time, or do users not switch context.
What data are they happy to share, and what are they not.
What are the typical response times that a user considers acceptable 5 seconds, 1 minute, 5 minutes, 60 minutes? Does it vary depending on scenario.
Is there particular services or information that there is a trend towards, for example local search requests, research/information, particular types of purchase etc.
We've just spent 6 months going through a very similar process to this, which has helped drive the development of our Converse platform, which allows people to build semi or fully automated conversational messaging services, so this is fairly closely related.
(From our point of view, the NLP data we gathered was useful, but it wasn't the most important part)