The implementation of Artificial Intelligence (AI) in retail promotional strategies marks a paradigm shift from mass marketing to granular, stock-keeping unit (SKU) level customization. AI-driven SKU promotion can stimulate consumer interest through contextual, geo-fenced and inventory-friendly offers, while real-time data streams allow retailers to surface high-conversion SKUs, optimize stock rotation and minimize holding costs. AI recommendations also reduce search fatigue and encourage exploration of new product categories. However, privacy concerns, algorithmic bias and ethical risks, particularly when behavioral patterns are inferred without the direct consent of the user, constrain large-scale adoption. This paper examines how AI systems use customer behavior data, transaction history, demographic segmentation and contextual triggers to promote SKUs to particular groups of consumers. A quantitative, cross-sectional design was adopted, surveying 150 retail consumers exposed to AI-based solutions such as virtual try-ons, smart shelves and recommendation engines. Findings indicate that perceived AI accuracy, transparency and reduced cognitive load have a significant effect on trust and purchase intention, while omnichannel integration enhances satisfaction. The privacy problem remains but is mitigated by perceived usefulness and transparency. The paper underscores the need to align AI potential with data governance and ethical execution in order to gain sustainable competitive advantage in the retail business.