Not a dev, but learned enough about AI's strengths and weaknesses to know that if a fortune 500 company told me to simply automate their entire business so that no one ever had verify what it's doing, I would chuckle and tell them confidentially that this isn't how AI works.
Then I'd proceed to break down the concept in super simple, glossed over terms by explaining how it's best to see it as a pattern recognition tool that can recognize so many patterns, it's able to mimic a genius that knows all and can do all. However the more deferment you give it, the more choices it has to make. We're talking about trillions of possible right and wrong answers with an infinite variation of both right and wrong answers. It's honestly a miracle that it can get 70-80 percent accuracy on average.
But still. The problem will always remain: What choices does it need to make? The more you ground the context for everything with both backend fail safes and human expertise in operating the models, the more productive value you can gain while being safe. Without that, you're wasting time and money. Worse, you're jeopardizing your company. You can still increase your margins and trim down your workforce. But only to a certain point and you still need at least, someone who knows what's going on and how to fix things quickly.
AI is powerful, but it requires a complete ontological structure layered on top of it to ground the choices it has to make for making our jobs smoother. Otherwise, you get dumb chat GPT garbage and a bunch of employees who think their bosses are all dumbasses for thinking this is going to 20x their growth.
Will this change in the future? Probably not because we'll likely be able to get AI to be exactly right, but it will never be the right choice for you without that context layer built by YOU.
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