There is. Companies are anxious to be left behind and want to hire people who are confident in working with AI. And trust me, especially in ageing societies, even your colleagues who are demonstrably tech savvy are not necessarily inclined to working with AI, so you definitely have an advantage there.
I generally outline the problem first that I would like to solve, then ask for best practice approaches and industry standards that apply to situations. Next, I ask to summarize the pros and cons of the various possible solutions and how they apply to my particular situation.
Mainly /goal mode of complex project/prompts. Example: https://doxygen.rpnx.net/cortado/ was nearly a one-shot for GPT 5.6 sol and took well over 1 hour for the first prompt before finishing.
But anything to do with compiler development tends to leave the AI spinning its wheels for quite a while.
Edit: Checked the time on that one, I confused it with Compress taking 3+ hours for 1 prompt and another couple of hours each for the next few prompts... Cortado was still well over 1 hour for the initial prompt though...
Time Zones factor into that. They want to be able to operate 24/7 "follow the sun"-style, so they want to get people half way around the globe - who obviously can't come to the office.
Few companies these days actually offer full remote work in their own time zone - the allure of being able to "control" your employees by making them come into the office is just too strong and "productivity" is not something that can be easily measured, let alone discerned (not by the people who make the decisions, anyway).
Huh, now THAT adresses a real need: An off-work day between Saturday (aka "catch up with chores you had to neglect because you were working") and "have to dread/mentally prepare for the upcoming work week"-Sunday!
I'd call it "Funday", actually: A day for pure, unalloyed relaxation. Everyone would have time to visit friends and hang out with family.
In my line of work (IT Operations for a large international bank), we came up with a classification framework that tried to gauge information along two axes: Importance and Frequency of Use.
Importance is the more obvious one - there is critical info you absolutely need as a basis to understand the system, then there is also minor details. The difference is in an emergency, lack of the former will break your attempt to fix the system, lack of the latter will merely inconvenience you.
The Frequence of Use axis is the one that brought value to our operations when we introduced it. That's the question "How often will I need this info?". Regularly re-occuring, detailled procedures need to be accessible easily, so they go to a prominent Confluence page. But then there is information that you will hardly ever need, some arcanae about the system that only the old timers knew but that needed to be fixed in writing.
The most vexing bits of info are those that are highly important but are rarely ever needed. Do you plonk them into Confluence? Might not be the best idea because then you don't know how to quickly find it when need be. So we had an extra category for just that sort of thing which became our go-to info hub for all sorts of highly urgent but at first intractable issues with our system.
Part of the answer to this conundrum might be the "lightswitch effect": Lightswitches are ubiquitous and easy to use, so you stop thinking about them, take them completely for granted and miss the bigger picture that even 150 years ago, this would have been considered not a small technical miracle!
LLMs seem to fall squarely into that category - the threshold to using them is incredibly low because you can "just talk to them" and they give back meaningful answers loaded with the sum total of human knowledge from the interwebs.
I don't think I take LLMs for granted - but I've misclassified them as something else.
The reason why it's so cool now, is because we can run good models on our own machines now. That's really what makes it a hackable technology in my head. Calling an API is just another product.. Does that make sense to you?
