大多數公司並非在AI上失敗——它們在開始之前就已失敗
Hacker News·
本文認為，AI專案的失敗通常源於忽略了關於決策改進和實際使用的基本商業問題，而非AI模型或工具本身的問題。文章指出，成功與否取決於在深入實施AI之前，是否解決了這些基礎性問題。
In practice, the failures I’ve seen rarely come from bad models or tools. They come from skipping basic questions:
– What decision or task is being improved?
– Are rules still working at their current scale?
– Is there real usage, or just expectations?
I put together a simple breakdown of how AI projects tend to succeed when they do, and where teams usually go wrong early on.
Curious how others here decide when AI is worth the added complexity and when it’s better to wait.
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