Who reads the summaries? Are they even useful to begin with? Or did this just save everyone 3-5 minutes of meaningless work?

Not the op, but I did work supporting three massive call centers for an f500 ecom.

It's 100% plausible it's busy work but it could also be for: - Categorizing calls into broad buckets to see which issues are trending - Sentiment analysis - Identifying surges of some novel/unique issue - Categorizing calls across vendors and doing sentiment analysis that way (looking for upticks in problem calls related to specific TSPs or whatever) - etc

False positives and negatives aren't really a problem once you hit a certain scale because you're just looking for trends. If you find one, you go spot-check it and do a deeper dive to get better accuracy.

Which is also how you end up with some schlepp like me listening to a few hundreds calls in a day at 8x speed (back when I was a QA data analyst) to verify the bucketing. And when I was doing it everything was based on phonetic indexing, which I can't imagine touching llms in terms of accuracy, and it still provided a ton of business value at scale.

AI reads them and identifies trends and patterns, or answers questions from PMs or others?