Companies are now beginning to see that the problem lies not in the model's size, but in whether the AI can actually understand the data it's supposed to interpret. AtScale has identified the cause of these AI-powered inefficiencies: ambiguous definitions, inconsistent metrics, and data forcing the model to guess at meaning.
