Bert And The Strategic Reinvention Of Morocco’s Agri-Food Industry

Abstract

Abstract

This article explores how the BERT learning model in artificial intelligence improves the personalization of products and services in the industry by leveraging customer data to better adjust the offerings and increase consumer satisfaction. The literature review shows that BERT goes beyond traditional methods by detecting weak signals and analyzing natural language more precisely. Nevertheless, its concrete application in the industrial sector remains underexplored. To fill this gap, our study is based on seven semi-structured interviews conducted with marketing, supply chain, production, data, and quality experts. We conducted this study in the Casablanca-Settat region, part of the agri-food sector. The results highlight four key levers through which BERT optimizes personalization: early trend detection, facilitating the anticipation of customer expectations; better analysis of consumer feedback, offering finer and more relevant segmentation; improved demand forecasting, allowing for the limitation of unnecessary stocks and the avoidance of shortages; and more tailored and flexible recommendations, aligning the offer with the real needs of different customer segments. However, this study has limitations regarding the generalization of the results due to the small sample size. Future research should delve into the economic and operational impact of BERT and identify best practices for its gradual integration.

Keywords:  Morocco, Strategy, Industry, AI, Qualitative Study, Algorithm.