10d • General Discussion 💬
I Doesn’t Fail in Deployment — It Fails in Definition
Most AI problems don’t appear during implementation.They’re baked in long before, during the definition stage no one pays enough attention to.
Vague goals like “optimize operations” or “automate customer service” sound impressive,but they hide the real danger: there is no measurable success condition.
In AI Advisory, I’ve learned one rule:If you can’t define the success metric in one sentence, the project is already drifting.
A proper AI Audit forces clarity:What exactly are we improving?By how much?For whom?Measured how?And what decision changes when we succeed?
Once these are clear, the tech almost feels trivial.Because AI is not about modeling — it’s about meaning.
Define the win.Then build the path.
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I Doesn’t Fail in Deployment — It Fails in Definition
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