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The AI projects I've seen fail, rarely fail for technical reasons. They fail because of how they're implemented, and that shapes everything that comes after. Right now, most conversations in this space focus on: Model choice Training data Token costs Self-serve configuration But in practice, implementation matters more than the technology itself. I'm starting a series on how to implement AI successfully. This is part one. I break down the 4 implementation models you need to