Publication Details
Abstract
The rapid advancement of Artificial Intelligence (AI) has traditional business analytics into a descriptive reporting apparatus into predictive intelligence systems. There is a major disparity between the predictive analytical output and the strategic action taken. This study proposes a combined approach of Generative Artificial Intelligence (GenAI) and advanced business analytics to facilitate strategic insight creation with the help of automatic means. The study uses the dataset of Instacart Market Basket Analysis that has over three million actual grocery transactions to develop a predictive classification model of whether a product will be reordered in case of past customer purchase history. Machine learning techniques are employed to analyze repeat buying behavior, shopping cart sequences and product-product affinity in retail transactions. In addition to predictive modeling, this study also presents a Generative AI layer that is meant to analyze the output of the analytics and provide automatic executive-level strategic recommendations in retail decision-making. The system converts predictive results into context business suggestions including inventory optimization plans, customized promotional campaigns, and cross-selling suggestions instead of providing numerical probabilities on their own. This approach represents a transition from traditional decision-support systems to autonomous strategic intelligence systems. The proposed framework is measured based on predictive performance measures such as F1-score and ROC-AUC, and qualitative measures of strategic relevance and managerial interpretability. The results suggest that the application of Generative AI to business analytics brings a high level of efficiency to decision-making, less managerial cognitive load, and better applicability of the analytical results in practice. This study contributes to the emerging field of AI-enhanced business analytics as it offers a scalable framework connecting predictive modeling and strategic reasoning. This study also has theoretical and managerial implications for retail organizations that are interested in using Generative AI to create intelligent and automated decision ecosystems.