What limits the usefulness of a complex AI prediction model currently?
Flawed input data or unknown underlying physical chemistry.
Even with the computational power of modern Artificial Intelligence and Machine Learning, the effectiveness of any predictive model remains fundamentally dependent on the quality and relevance of the information it is fed. A highly sophisticated algorithm cannot compensate for fundamentally poor data quality or a lack of understanding regarding the basic physical chemistry governing the product's behavior. If the input data describing raw material variability or storage conditions is inaccurate, or if a critical degradation pathway has not been identified by physical chemistry principles, the resulting data-driven forecast produced by the AI will be unreliable, regardless of its complexity.
