Sorry I didn't see your comment until now, apologies for the late reply.
A classic messy judgement call would be:
1. Input information includes some word of mouth info that I have no reason to doubt, but also absolutely no way of verifying against field data
2. A single piece of equipment is not functioning - the plant is reasonably safe to operate with the failure, but how safe? Are the other relevant protective layers in place and effective in the relevant scenarios?
3. If I decide to implement a really robust and good-quality solution that'll stand the test of time, will it actually take so long to implement that I would have been better off with something simpler but less robust?
4. Is my decision making process clearly communicated enough for the decision makers involved? Which installation manager is on shift?
5. If the regulator audited my decision making process would they raise a recommendation? So what if there's a 0.1% risk that they'll raise a recommendation as long as people are safe?
These kinds of thought processes are where I add value as an engineer in a way that's irreplaceable by LLMs. I just hope that LLMs can really improve how quickly I can access data to make my human decision-making better based in fact.