12d (edited) • General Discussion 💬
Most AI setups don’t fail because the tools are bad.
They fail because people try to automate everything too early.
What usually goes wrong:
- No clear real world problem being solved
- Too many tools stacked together
- Over automation before understanding the workflow
- No human checkpoint in the process
- Building complexity instead of clarity
In my experience, the simplest setups tend to work the best in real environments.
Start with one problem. One workflow. One outcome.
Then expand only when there’s actual proof it works.
𝗠𝘆 𝘁𝗮𝗸𝗲: 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆 𝗯𝗲𝗮𝘁𝘀 “𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱” 𝘀𝗲𝘁𝘂𝗽𝘀 𝗮𝗹𝗺𝗼𝘀𝘁 𝗲𝘃𝗲𝗿𝘆 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀.
What do you think is the biggest reason AI setups fail in practice?
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Most AI setups don’t fail because the tools are bad.
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