AI-Based Dynamic Pricing in Digital Commerce: Implications for Consumers and Retailers

Abstract

Artificial intelligence (AI) is reshaping pricing in digital commerce. Retailers can now combine demand, inventory, competitor prices and customer signals to revise prices quickly. This review examines how AI-based dynamic pricing works and what it means for consumers and retailers. It separates market-responsive pricing from personalized pricing because the two practices create different concerns. Recent studies show that machine learning and reinforcement learning can improve demand estimation, inventory coordination and price decisions. Yet the same systems can also create concerns about fairness, privacy and trust when consumers do not understand why a price has changed. The paper therefore treats pricing as both a technical and a behavioral issue. It proposes an AI Dynamic Pricing-Consumer-Retailer (AI-DPCR) framework linking digital inputs, AI models, price decisions and market outcomes. The review finds that responsible pricing requires more than predictive accuracy. Retailers also need clear rules, data minimization, fairness checks and human oversight. A balanced approach can support revenue and inventory goals while reducing the risk of consumer backlash.