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This article analyzes annotation costs and outcomes in enterprise AI, suggesting practical frameworks for decision-making.
Data annotation represents one of the most underestimated cost centers in enterprise AI development. This article presents a comprehensive economic framework for annotation decision-making, analyzing the crowdsourcing versus expert labeling dichotomy through total cost of ownership, quality-adjusted returns, and strategic implications. Drawing on case studies from healthcare, autonomous vehicles, financial services, and manufacturing.
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Oleh Ivchenko (2026) studied this question.
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