You get all your teams to build some expertise and (hopefully) get a sense of where the technology might have some ROI
You'd run the risk that they instead develop a dependency or overreliance on the tech. They probably won't think about the "I" part of ROI and evaluate how many tokens a given task produces relative to the saved time and effort.
Throttling it later might then cause a drop in productivity until they relearn how to do simple stuff they could do themselves but delegated to AI instead, whether or not it's ideal for the task.
For example: "search and replace" requires the LLM to ingest and then produce the whole document as output. Aside from the question whether it'll have caught all instances and replaced them without otherwise altering the text (which a casual user won't check), the amount of output tokens correlates with the size of the text.
That's a lot of wasted tokens for a task they could have done without AI, but so long as asking the computer is quick and convenient, they won't think twice. Then, once the tokens are throttled, they'll suddenly realise they've run out of tokens early because they burned a ton on tasks that seem trivial to them, leaving none for the more complex tasks they'd actually prefer to delegate (whether or not they should). They might not make the immediate connection which tasks eat so many tokens either, so they'll take a while having to try all their use cases again, see how expensive they are, run out of their allotment early and wait for the next period.
If you're gonna have people figure out how to use it, you'll have to throttle from the start to make them also figure out how to use it economically.
Also, mandatory classes on the limitations and reasonable uses. Don't let it get to the point where they find out the hard way that it's not actually intelligent and has no concept of truth.