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95% of Companies See 'Zero Return' on $30B Generative AI Spend

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Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#301

Post by jmkni »

b8 wrote: post #171
IgorPartola wrote: post #34

What are the actual use cases that can generate revenue or at least save costs today? I can think of: 1. Generate content to create online influence. This is at this point probably way oversaturated and I think more sophisticated models will not make it better. 2. Replace junior developers with Claude Code or similar. Only sort of works. After all, you can only babysit one of these at a time no matter how senior you…

AI Customer Service is very frustrating to work with as a end user.

Yep.

The thing is, you aren't contacting customer services because everything is going well, you are contacting them because you have a problem.

The last thing you need is to be gaslit by an AI.

The worst ones are the ones where you don't realise right away you aren't talking to a person, you get that initial hope that you've actually gotten through to someone who can help you (and really quickly too) only to have it dawn on you that you are talking to a ChatGPT wrapper who can't help you at all.

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#302

Post by williamdclt »

la_fayette wrote: post #276
doorhammer wrote: post #205

Earlier quoted context omitted.

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 dol…

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

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

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#303

Post by rpcope1 »

jampa wrote: post #42

The biggest mistake people are making is treating AI as a product instead of a feature. While people are doing their work, they don't think, "Oh man, I am really excited to talk with AI today, and I can't wait to talk with a chatbot." People want to do their jobs without being too bored and overwhelmed, and that's where AI comes in. But of course, we cannot hype features; we sell products after all, so that's the sta…

All true, but then there goes your stratospheric valuations and all the crazy hype. This come to jesus moment may very well deflate one of the few remaining hot areas around software engineering..I could see people being reluctant to stop the hype train as then we'd really have to come to terms with the fact that the "industry" as a whole is kind of in the shitter and it's a less good time to be a software engineer across the board than 5 or 10 years ago.

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#304

Post by gmd63 »

herval wrote: post #277
gmd63 wrote: post #91

Earlier quoted context omitted.

Smartest people will be working against that. You're thinking of opportunistic people with myopia.

Pretty much the entire tech industry has bent the knee by now - they even gifted the new ruler with golden statues. It's not just a handful of people...

Few people have the balls to do the right thing when the risks pass a certain limit. And yet it's most important to do the right thing at the largest scale.

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#305

Post by thisisit »

jordanb wrote: post #140
jawns wrote: post #56

Full disclosure: I'm currently in a leadership role on an AI engineering team, so it's in my best interest for AI to be perceived as driving value. Here's a relatively straightforward application of AI that is set to save my company millions of dollars annually. We operate large call centers, and agents were previously spending 3-5 minutes after each call writing manual summaries of the calls. We recently switched to…

We use Google meet and it has Gemini transcriptions of our meetings. They are hilariously inaccurate. They confuse who said what. They often invert the meaning "Joe said we should go with approach x" where Joe actually said we should not do X. It also lacks context causing it to "mishear" all of our internal jargon to "shit my iPhone said" levels.

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.

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#306

Post by chriskanan »

Where is the actual paper that makes these claims? I'm seeing this story repeated all over today, but the link doesn't actually seem to go to the study.

I am not going to trust it without actually going over the paper.

Even then, if it isn't peer-reviewed and properly vetted, I still wouldn't necessarily trust it. The MIT study on AI's impact on scientific discovery that made a big splash a year ago was fraudulent even though it was peer reviewed (so I'd really like to know about the veracity of the data): https://www.ndtv.com/science/mit-retracts-popular-study-clai...

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#307

Post by andrepd »

FirmwareBurner wrote: post #239
dsr_ wrote: post #86

Earlier quoted context omitted.

Pro-tip: don't write the summary at all until you need it for evidence. Store the call audio at 24Kb/s Opus - that's 180KB per minute. After a year or whatever, delete the oldest audio. There, I've saved you more millions.

>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.

Opus pretty much blows all those codecs out of the water, in every conceivable metric. It's actually pretty impressive that a media codec is able to universally exceed (or match) every previous one in every axis.

Still, it's based on ideas from those earlier codecs of course :)

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#308

Post by croes »

jawns wrote: post #56

Full disclosure: I'm currently in a leadership role on an AI engineering team, so it's in my best interest for AI to be perceived as driving value. Here's a relatively straightforward application of AI that is set to save my company millions of dollars annually. We operate large call centers, and agents were previously spending 3-5 minutes after each call writing manual summaries of the calls. We recently switched to…

But I doubt it justifies the billions of dollars getting burned for training language models and building power plants.

And are full transcriptions not the better option?

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#309

Post by esafak »

la_fayette wrote: post #276
doorhammer wrote: post #205

Earlier quoted context omitted.

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 dol…

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

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.

Re: 95% of Companies See 'Zero Return' on $30B Generative AI Spend

#310

Post by doorhammer »

la_fayette wrote: post #276
doorhammer wrote: post #205

Earlier quoted context omitted.

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 dol…

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

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.

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