You must use the tool. We must justify it. These aren't the words companies use, but that's what it comes down to. When you buy an expensive solution before you've clearly identified the problem, you have to use the tool to justify its line item in the spreadsheet. At most companies I've worked in, there's always some application I've used once or twice. The third time I want to use it, it's gone. If a tool isn't popular, the finance team has no trouble dropping it.
But for some reason, it's different with AI. If nobody's using it, the assumption is that employees aren't being productive enough. So we got signed up for mandatory training. There was limited space, but I moved fast and got a spot. I'd been using ChatGPT since it came out, so I was hoping the training would turn me from a casual user into a pro.
Instead, we were shown how to copy and paste data from Excel and other sources into the chat interface. We worked with sample data, and there was always someone in class who'd say "mine didn't work." The developers in the room asked about Codex. The OpenAI-certified instructor replied that she wasn't a developer. There was an awkward moment when someone asked if we could just use Copilot, since it's already integrated into Excel.
I got my certificate. But I don't think I learned anything I couldn't have picked up with a free account on my own time.
What comes with a certificate, though, is the obligation to use the tool. I had a hammer, and everything looked like a nail. Whenever I ran into dense information, I'd copy, paste, extract insight. The great thing is that the result always looks neatly presented. I didn't have time to read the dense information in the first place, so of course I wasn't going to verify whether it was accurately referencing the source material. Besides, everyone was doing the same thing. I hate it when people use AI to write a Slack message, but at this point it's inevitable.
One time, after a meeting, I pulled the transcript and asked ChatGPT to organize it into action items and generate a Q&A based on the content. It gave me something very elaborate. Or at least it looked elaborate. I skimmed it and passed it along to the next person, who actually had to use it to research a project. What I failed to notice was that a large part of the document was hallucinated.
Some of the action items sounded technical enough to fool me. But to someone who actually had to work on them, they made no sense at all. I'm guilty of this, and it's become the norm. The same way we use large language models to write code as software engineers, the planning and architecting behind that code is increasingly written by LLMs too.
In my household, AI slop videos are strictly banned, to the point that my kids police their friends who watch that stuff. So when my son saw ChatGPT open on my computer, he asked if I was making BI Slop.
"BI Slop? What's that?"
"It's Business Intelligence Slop!"
I was so proud of him for recognizing exactly what I was doing. And I was so disappointed in myself, because that's exactly what I was doing. The same way we scrutinize code and refuse to merge PRs that are entirely AI-generated, we need to pay attention to the other ways we use these tools. Business decisions and plans produced by LLMs need to pass the smell test before they're accepted.
The issue is the same one. We merge large PRs because we don't have time to read them, then suffer the consequences later in unexpected ways. With business intelligence, we don't read the output because of its sheer volume, and we approve it because it looks good on the surface. Then, again, we suffer the consequences later in unexpected ways.
This is a problem created only because we were handed a mandatory solution: use AI to justify the line item in the spreadsheet. It only saves you time in the sense that you skip reading the output, and you do that because it looks good on the surface.