The real reason enterprise AI is stuck

# Why Enterprise AI Keeps Disappointing in Real-World Use Companies are still treating AI like magic instead of engineering it like reliable software, which is why impressive demos rarely translate into actual business value. The industry relies on human-like descriptions (AI "dreams," "remembers," "plans") that make these systems sound familiar but don't provide the formal rules needed to make them work consistently at scale. Until businesses stop using metaphors and start building AI with the same rigorous, measurable standards they use for databases or other proven software, enterprise AI will stay stuck between impressive promises and messy reality.
The reason enterprise AI remains stubbornly artisanal is not because models are too weak. It is not because context windows are too short, or agents need better prompts, or companies are resisting adoption. Those are all visible problems. But they are not the deepest one. The deeper problem is
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