AI systems excel at syntax but struggle with semantics and business context. Most enterprise value lies in specific practices and definitions unique to each organization, which AI cannot comprehend without guidance. Benchmarks such as Spider 2.0 show AI's limitations, achieving only 59% accuracy in translating natural language into SQL for realistic enterprise databases. Many developers find AI unreliable, as it often produces outputs that require substantial debugging and fact-checking, primarily due to its inability to grasp unique business contexts which are not found in public training data.
AI systems struggle with business context, leading to significant challenges in translating enterprise-specific knowledge into actionable insights, as shown by limited accuracy in translating natural language to SQL.
The Spider 2.0 benchmarks illustrate the difficulty AI has with real enterprise databases, achieving only 59% accuracy on exact matches and 40% when faced with transformation complexities.
Most enterprise value resides in contextual knowledge that AI cannot easily access, highlighting the necessity of human guidance to bridge gaps in understanding.
Developers often express frustration not at AI's coding abilities, but at their inability to trust AI outputs due to limitations in context comprehension.
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