←back to thread

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

(thedailyadda.com)
418 points speckx | 1 comments | 21 Aug 25 15:36 UTC | HN request time: 0s | source
Show context
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.

replies(39): >>44974847 #>>44974853 #>>44974860 #>>44974865 #>>44974867 #>>44974868 #>>44974869 #>>44974874 #>>44974876 #>>44974877 #>>44974901 #>>44974905 #>>44974906 #>>44974907 #>>44974929 #>>44974933 #>>44974951 #>>44974977 #>>44974989 #>>44975016 #>>44975021 #>>44975040 #>>44975093 #>>44975126 #>>44975142 #>>44975193 #>>44975225 #>>44975251 #>>44975268 #>>44975271 #>>44975292 #>>44975458 #>>44975509 #>>44975544 #>>44975548 #>>44975622 #>>44975923 #>>44976668 #>>44977281 #
jordanb ◴[21 Aug 25 16:50 UTC] No.44975016[source]▶
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.

replies(6): >>44975247 #>>44975295 #>>44975356 #>>44975601 #>>44975899 #>>44976157 #
1. sigmoid10 ◴[21 Aug 25 17:54 UTC] No.44975899[source]▶
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.