In applied AI, “collect more data” is the default response to performance issues — but it’s increasingly wrong. Drawing on real-world deployments, this article explains why data signal quality, not data volume, determines whether AI systems scale reliably.
Training data quality defines the ceiling of AI performance. Drawing from 10,000+ real-world AI projects across healthcare, autonomous systems, and enterprise vision, this article examines why data quality is a systems problem — not a labeling problem. We break down the quality dimensions that matter, modality-specific insights, and why manual QA fails at scale. The result is a practical, engineering-led perspective on building production-ready AI systems.
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