[doorhammer]

Sentiment analysis, nuanced categorization by issue, detecting new issues, tracking trends, etc, are the bread and butter of any data team at a f500 call center.

I'm not going to say every project born out of that data makes good business sense (big enough companies have fluff everywhere), but ime anyway, projects grounded to that kind of data are typically some of the most straight-forward to concretely tie to a dollar value outcome.

[la_fayette]

Yes that sound like important and useful use cases. However, these are solved by boring old school ML models since years...

[williamdclt]

I think what they're saying is that you need the summaries to do these things

[esafak]

It's easier and simpler to use an LLM service than to maintain those ad hoc models. Many replaced their old NLP pipelines with LLMs.

[aaomidi]

Sentiment analysis was not solved and companies were paying analyst firms shit tons of money to do that for them manually.

[doorhammer]

So, I wouldn't be surprised if someone in charge of a QA/ops department chose LLMs over similarly effective existing ML models in part because the AI hype is hitting so hard right now.

Two things _would_ surprise me, though:

- That they'd integrate it into any meaningful process without having done actual analysis of the LLM based perf vs their existing tech

- That they'd integrate the LLM into a core process their department is judged on knowing it was substantially worse when they could find a less impactful place to sneak it in

I'm not saying those are impossible realities. I've certainly known call center senior management to make more hairbrained decisions than that, but barring more insight I personally default to assuming OP isn't among the hairbrained.

[adrr]

Those have been done for 10+ years. We were running sentiment analysis on email support to determine prioritization back in 2013. Also ran bayesian categorization to offer support reps quick responses/actions. Don't need expensive LLMs it.

[doorhammer]

Yeah, I was a QA data analyst supporting three multi-thousand agent call-centers for an F500 in 2012 and we were using phoneme matching for transcript categorization. It was definitely good enough for pretty nuanced analysis.

I'm not saying any given department should, by some objective measure, switch to LLMs and I actually default to a certain level of skepticism whenever my department talks about applications.

I'm just saying I can imagine plausible realities where an intelligent and competent person would choose to switch toward using LLMs in a call center context.

There are also a ton of plausible realities where someone is just riding the hype train gunning for the next promotion.

I think it's useful to talk about alternate strategies and how they might compare, but I'm personally just defaulting to assuming the OP made a reasonable decision and didn't want to write a novel to justify it (a trait I don't suffer from, apparently), vs assuming they just have no idea what they're doing.

Everyone is free to decide which assumed reality they want to respond to. I just have a different default.