This research uses machine learning, data mining, and optimization techniques to identify hidden relationships between products in retail shopping baskets. By analyzing over two million transactions, it predicts how promotions affect demand across products and helps retailers design smarter discount strategies, improve inventory planning, increase profits, and enhance customer satisfaction.

This research investigates why many organizations fail to implement AI effectively, focusing on readiness rather than technology. In automotive after-sales services, it identifies gaps between systems and AI ambitions. The study develops a framework aligning people, processes, and capabilities, helping organizations achieve sustainable and successful AI adoption.