💼 AI Business

85% of AI Projects Fail Because ML Teams Won't Ditch Accuracy

Gartner pegs AI project failures at 85%. The culprit? Blind faith in accuracy amid skewed datasets. Time for precision, recall, and smarter metrics.

James Kowalski Mar 29, 2026 3 min read 5 views
Line graph showing accuracy misleading vs F1 score rising on imbalanced fraud dataset

⚡ Key Takeaways

  • Accuracy crumbles on imbalanced data — 85% AI fails prove it.
  • Pick precision/recall/F1 by business costs, not habit.
  • AUC and log loss future-proof models for production wins.
James Kowalski
Written by

James Kowalski

Investigative tech reporter focused on AI ethics, regulation, and societal impact.

#AUC ROC #F1 score #ML metrics #accuracy pitfalls #precision recall

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Originally reported by Towards AI

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