In a rapidly changing geopolitical climate, the global automotive industry faces significant disruptions, particularly due to the reintroduction of tariffs, which have impacted production cycles and model-year transitions. This has resulted in a notable decrease in new-model vehicle availability and overall inventory, pressuring consumer pricing and inventory management. Traditionally, inventory classification relied on ABC analysis, which segments items by value, but this method is criticized for its limited criteria. A more comprehensive multi-criteria inventory classification (MCIC) approach, incorporating factors like lead-time and durability, is proposed. However, the importance of unstructured data, such as customer feedback and product reviews, is increasingly recognized. Using large language models (LLMs), insights from these unstructured data sources can be vectorized to enhance inventory classification models, shifting from reactive to predictive management. MongoDB facilitates this by enabling AI-driven inventory classification through vector embeddings and dynamic criteria generation, leveraging structured and unstructured data. This approach aims to provide a more nuanced understanding of product value and demand, adapting to the evolving needs of the automotive industry.