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

(thedailyadda.com)
418 points speckx | 4 comments | 21 Aug 25 15:36 UTC | HN request time: 0.661s | source
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jawns ◴[21 Aug 25 16:36 UTC] No.44974805[source]▶
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 using AI to transcribe and write these summaries. Not only are the summaries better than those produced by our human agents, they also free up the human agents to do higher-value work.

It's not sexy. It's not going to replace anyone's job. But it's a huge, measurable efficiency gain.

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1. Shank ◴[21 Aug 25 16:43 UTC] No.44974907[source]▶
Who reads the summaries? Are they even useful to begin with? Or did this just save everyone 3-5 minutes of meaningless work?
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2. vosper ◴[21 Aug 25 16:47 UTC] No.44974979[source]▶
AI reads them and identifies trends and patterns, or answers questions from PMs or others?
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3. doorhammer ◴[21 Aug 25 16:56 UTC] No.44975119[source]▶
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.

4. cube00 ◴[21 Aug 25 18:47 UTC] No.44976538[source]▶
AI writes inaccurate summaries and then consumes its own slop so it can hallucinate the answer to the PM's questions after misreading said slop.

Much like dubbing a video tape multiple times, it's going to get worse as you add more layers text predictors.