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.

Imagine a follow-up call of a customer. They are referring to earlier calls and the call center agents needs to check what it was about. So they can skim/read the transcripts while talking to the customer. I guess it's really hard to listen to transcripts while you're on the phone.

you want to be able to search over summaries so you need to generate them right away

Also entrenches plausible deniability and makes legal contests way more cumbersome for plantiffs to resolve.

You also will have saved them all the cost of the AI summaries that are incorrect as well.

The parent states:

>Not only are the summaries better than those produced by our human agents...

Now, since they have not mentioned what it took to actually verify that the AI summaries were in fact better than the human agents, I'm sceptical they did the necessary due dillengence.

Why do I think this? Because I have actually tried to do such a verification. In order to verify that the AI summary is actually correct you have to engage in the incredibly tedious task of listening to original recording literally second by second and make sure that what is said does not conflict with the AI summary in question. Not only did the AI summary fail at this test, it failed in the first recording I tested.

The AI summary stated that "Feature x was going to be in Release 3, not 4" whereas the in the recording it is stated that the feature will be in Release 4 not 3, literally the opposite of what the AI said.

I'm sorry but the fact that the AI summary is nicely formatted and has not missed a major topic of conversation means fuck all if the details that are are discussed are spectacularly wrong from a decision tracking perspective, as in literally the opposite of what is stated.

And I know "why" the Ai summary fucked up, because in that instance the topic of conversation was about how there was some confusion about which release that feature was going to be in, that's why the issue was a major item of the meeting agenda in the first place. Predicably, the AI failed to follow the convoluted discussion and "came to" the opposite conclusion.

In short, no fucking thanks.

This works unless you want to automate something with the transcripts, stats, feedback.

The summaries can help automate performance evaluation. If the employee disputes it, I imagine they pull up the audio to confirm.

>Store the call audio at 24Kb/s Opus - that's 180KB per minute

Why OPUS though? There's dedicated audio codecs in the VoiP/telecom industry that are specifically designed for the best size/quality for voice call encoding.

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Same here. It's frustrating that it doesn't seem to have contextual awareness of who we are and the things we work on so things like names of our products, names of big clients, that we use repeatedly in meetings, are often butchered.

That's the difference between having real AI guys and your average linkedIn "AI guys." The other post is a perfect example for a case where you could take a large but still manageable, cutting-edge transcription model like Whisper and fine-tune it using existing hand made transcriptions as ground truth. A match made in heaven for AI engineers. Of course this is going to work way, way better for specific corporate settings than slapping a random closed source general purpose model like Gemini on your task and hoping for the best, just because it achieves X% on random benchmark Y.

I don’t know how it can confuse because input on mic is relatively straight forward to get. I use fathom and others and they are accurate, better than manual taken. Interesting take, that I don’t memorize 100% on the calls anymore since I rely on note takers, I only remember the major points but when I read the notes, everything comes clear.

Oh, that's what happening. I thought my English is just terrible :(

I found that if you have people with accents and they emphasize certain words then it becomes very difficult to read. One example, I find is "th" is often D because how people pronounce it. Apart from that it is a hit or miss.

I also use Gemini notes for all my meetings and find them quite helpful. The key insight is: they don’t have to be particularly accurate. Their primary purpose is to remind me (or the other participants) of what was discussed, what considerations were brought up, and what the eventual decision was. If it inverts the conclusion and forgets a “not”, we’re going to catch that, because we were all in the meeting too. It’s their to jog our memory of what was said, because it’s much easier to recognize correct information than recall it, it’s not the authoritative source of truth on the meeting.

This gets to a common misconception when it comes to GenAI uses: it functions best as “augmented intelligence” rather than “artificial intelligence”. Meaning that it’s at its best when there’s still a human in the loop and the AI supplements the parts the person are bad at rather than replacing the person entirely. We see this with coding, where AI is very good at writing scaffolding, large-scale refactoring, picking decent libraries, reading API docs and generating code that calls it appropriately, etc but still needs a human to give it very specific directions for anything subtle, and someone to review carefully for bugs and security holes.

Part of me also wonders if people may agree that its better simply because they don't actually have to do the summarization anymore. Even if it is worse by some %, that is an annoying task you are no longer responsible for; if anything goes wrong down the line, "ah the AI must've screwed up" is your way out.

I built a little solution to record and transcribe all of my own meetings. I have many meetings (30hr week+) and I can't keep pace with adequate note-taking while participating in them all.

I'm finding that the summarization of individual meetings very useful, I'm also finding that the ability to send in transcripts across meetings, departments, initiatives whatever to be very effective at surfacing subtexts and common pain points much more effectively than I can.

I'm also using it to look at my own participation in meetings to help me see how I interact with others a (little) bit more objectively and it has helped me find ways to improve. (I don't take its advice directly lol, just think about observations and determine myself if it's something that's important and worth thinking about)

Have you tried having it summarize the meetings you have?

AI definitely summarizes meetings better than me and _almost_ anyone else I've seen do it (there is one exception -- one guy was a meeting note taker god. He was so good that he set up a mailing list because so many people wanted to read his meeting notes.) I could probably do better than AI if I really tried, but I've only ever done that a few times.

As a customer of many companies who has also worked in call centers, I can't tell you how frustrating it is when I, as a customer, have to call back and the person I speak with has no record or an insufficient record of my last call. This has required me to repeat myself, resend emails, and wait all over again.

It was equally frustrating when I, as a call center worker, had to ask the custmer to tell me what should already have been noted. This has required me to apologize and to do someone else's work in addition to my own.

Summarizing calls is not a waste, it's just good business.

We stopped after it kept transcribing a particular phrase of domain jargon as “child p*rn”, again and again.

Unless a case goes down the legal road, nobody is ever bothering to read old call summaries in a giant call center.

When was the last time you called a large company and the person answering was already across all the past history without you giving them a case number first?

My guess is that the summaries are never actually read, so accuracy doesn't actually matter and the AI could equally be replaced with /dev/null

We tried Otter.ai, someone complained and asked: "Could you f-ing not? I don't trust them" and now Otter is accused of training their models on recorded meetings without permission. Yeah, I don't even care if it works, I don't trust any of these companies.

I have seen ai summaries of calls get people into trouble because the ai hallucinated prices and features that didn't exist

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?

It's such a useless platitude. The "higher value work" is answer more calls so we can have less staff on queue.

Because call centers are cost centers - nobody pays a dime more than they have to in these situations and its all commodity work.

Could you broadly describe the AI projects you have built?

Thank you, I came to say this too. You're mushing your humans harder, and they'll break. Those 5 mins of downtime post-call aren't 100% note taking - it's catching their breath, trying to re-compose after dealing with a nasty customer, trying to re-energise after a deep technical session etc.

I think AI in general is just being misused to optimise local minima in detriment to the overall system.

"Mechanically, more efficiency means less people required for the same output."

Why can't it mean more output with the same number of people? If I pay 100 people for 8 hours of labor a day, and after making some changes to our processes, the volume of work completed is up 10% per day, what is that if not an efficiency gain? What would you call it?

It really depends on the amount of work. If the demand for your labor is infinite, or at least always more than you can do in a days work, efficiency gains won't result in layoffs, just more work completed per shift. If the demand for the work is limited, efficiency gains will likely result in layoffs because there's no point in paying someone who was freed up by your new processes to sit around twirling a pen all day.

You're not accounting for teams already being understaffed and overtasked in many situations, with some AI tools allowing people to get back to doing their jobs. We aren't expecting significant headcount changes, but we are expecting significant performance and quality improvements for the resources we have.

But... that is the actual job. A clear medical history is very important, and I'm not ready yet to cut out my doctor from that process.

This reminds me of the way juniors tend to think about things. That is, writing code is "the actual job" and commit messages, documentation, project tracking, code review, etc. are tedious chores that get in the way. Of course, there is no end to the complaints of legacy code bases not having any of those things and being difficult to work with.

Competent, yes. But the current ones are likely to transcribe "recommend amputating the left foot" as "recommend amputating the right foot". Still want it?

Until it hallucinates and the AI has written something wrong about you in your official medical record

they weren't

its likely a checkbox for compliance or some policy a middle manager put in place that is now tied to a kpi

I'm under the impression that one of the most critical responsibilities a lead has is to establish and maintain a good working culture. Properly vetting new additions feeds directly into that. Why offload it to AI?

Yeah the standard "this call may be recorded for quality or training purposes" preamble shouldn't cover for slurping your voiceprint to further the butchering of client service that this call centre is here for.

You would be hard-pressed to find a call center that _doesn't_ start every phone call with a warning that the conversation may be recorded.

Typical call center call is recorded and you are told so by the start of the conversation. I had quite a few of those.

You could get a transcript, not a summary

Only if your audio was crystal clear, you spoke like a robot very slowly, and each customer has to do a 30-minute “please read this text slowly to train the speech recognition software” preamble before talking to the actual human.

Having a human read a summary is way faster than getting them to write it. If they want to edit it, they can.

AI today is terrible at replacing humans, but OK at enhancing them.

Everyone who gets that is going to find gains - real gains, and fast - and everyone who doesn't, is going to end up spending a lot of money getting into an almost irreversible mistake.

I presume they're not using these notes for anything mission or life critical, so anything less than 100% accuracy is OK.