AI: A Tool That Provides Solutions for Micro Businesses

Small grocery stores in Buenos Aires face ongoing challenges in managing inventory and meeting customer demand, particularly when preferred products are unavailable. This research, a collaboration between ITBA and MIT LIFT Lab, examined purchasing behaviors and substitution patterns using Natural Language Processing (NLP) and Generative AI. Data was collected through consumer and storekeeper surveys across seven grocery stores, analyzing key factors that influenced substitution choices, such as product availability, store characteristics, and demographic trends. The study concluded with an analysis of key findings derived from the surveys and the development of a predictive model. This model forecasts customer decisions when a desired product is unavailable, considering variables such as store, age, product and gender. The findings of the research highlighted the role of AI-driven insights in optimizing inventory management and improving decision-making for small grocery stores.