Episode Details

This story was originally published on HackerNoon at: https://hackernoon.com/when-ai-fails-in-the-real-world-the-model-is-rarely-to-blame.
AI failures in production aren’t about models. Learn how infrastructure, data pipelines, and system design determine success in real-world AI systems.
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At IEEE ICAIC 2026, Tejas Pravinbhai Patel argued that AI failures stem from infrastructure—not models. Key issues include training-serving skew, duplicate data from streaming errors, and model staleness. Success in enterprise AI depends on strong architecture, observability, and disciplined deployment—not just better models.