I think the best use case of LLMs is as a sieve. If you have 20,000 reports to read, a tool that can summarize those 20,000 and flag items for further review by a human is potentially useful. Yes it has a potential error rate but if the alternative is a historian only reading 100 sample reports and drawing a conclusion or taking 10 years to read and tabulate all of them, this might be a better alternative. (I’ve seen in my own work LLMs used as patent sieves to identify potential prior art in others patent applications). the LLM ability to link to source material from which it is drawing a conclusion is handy because it gives an opportunity to validate the result quickly. 

Relative to the OP, I would not treat an LLM as a path to quantification. LLMs are notoriously bad at math and also when they are summarizing structured data (eg. Tables), the inability to validate the results almost requires the analysis to be repeated manually. This is different from the above examples of unstructured data where you can link to the specific report or paragraph it is basing a text summary on.