Most AI projects don't fail because of the model.
They fail because nobody trusts them enough to use them.
Teams spend weeks comparing:
GPT vs Claude
Agent frameworks
Prompt strategies
Benchmarks
Then the project quietly dies.
Not because the AI was bad.
Because nobody solved the boring stuff.
Things like:
Validation
Monitoring
Human approval flows
Error handling
Accountability
In my experience, improving the model usually gives small gains.
Improving trust changes everything.
A 90% accurate agent that people trust creates value.
A 99% accurate agent that nobody trusts gets ignored.
The biggest challenge in AI isn't intelligence.
It's adoption.
Curious if others have seen the same thing.
What actually killed the AI projects you've worked on?
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