Why Most Enterprise AI Projects Fail Before They Start: A Practitioner's Honest Assessment
After working on AI/ML deployments across retail, healthcare, fintech, and manufacturing, a pattern is unmistakable. The failures are almost never about the model. They are about the data, the infrastructure decisions made six months earlier, and the absence of a clear definition of success before a single line of training code is written.
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