Startups should prioritize one high-impact use case, ship quickly, and iterate against clear metrics rather than pursuing broad AI transformation from day one.
A common sequence is discovery, data readiness checks, prototype delivery, production hardening, and ongoing optimization. This keeps momentum high while managing risk.
Strong implementation combines speed with governance: versioned prompts, usage monitoring, and fallback logic.
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