People will get fired due to hallucinated bad reports, won't they.
Like, we get it - you resent your coworkers & consider them beneath you.
Hopefully, your unique, elite output keeps you employable for a few quarters longer than them.
I’m routinely asked to make reports, but things stall when I ask, “so what do you actually need to see on the report, and where should the data come from”. A lot of people out there are kind of cargo cutting their way through things.
Cargo culting. Though cargo cutting would be interesting to watch :-)
Your response was both snarky and personal which I think comes across much worse.
You would be amazed how many issued we identified so far and our mgmt uncharacteristically wants us to bring up issues. Some companies kill their messengers.
So yeah.. I absolutely buy that a portion of business reports are of low quality.
But if I was making mistakes, say, in 5% of my facts and figures...I probably would have been reassigned to other work. 5% is way too high for an Excel sheet: it makes the model completely useless. It often takes more effort to audit and fix 5% of busted Excel cells than it does to just rebuild the model from scratch. GPT-4/Copilot/Gemini/etc almost certainly make more than 5% errors for any real use case.
I am sure there are many bad organizations with lower standards than the organizations I worked at. My concern is that good organizations will adopt technology like GPT, inadvertently lowering their standards because they trusted a machine they didn't understand. It's very worrying that good people are rationalizing this choice by saying "well humans are also unreliable" without putting specific numbers to it. 5% errors in document summarization is not good enough for human workers, and it certainly shouldn't be good enough for software.
Its customer data, and it can technically be anything. Imagine making a system that is expected to be reliably excellent at generating things within that domain. It is already a very hard problem that I can confidently say is unsolved, and probably not even as good as a human can sort things. Even in a good dataset that is unstructured and unlabelled you will face issues in just a single domain (e.g. vision tasks trying to classify a variety of vehicles), imagine that being extended to a wide variety of subjects that can mix and match.
I imagine at most, it'll be a reputation hit.
The not so fine print is Copilot may return garbage and it's up to you to confirm the results.