This article presents an economic framework for optimizing data acquisition costs in organizations, highlighting its foundational role in enterprise AI implementation.
Data acquisition represents the foundational economic challenge of enterprise AI implementation, often consuming 40-80% of total project budgets before a single model is trained. This article presents a comprehensive economic framework for understanding, planning, and optimizing data acquisition costs across different organizational contexts.
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Oleh Ivchenko (2026) studied this question.
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