LEVERAGINGAI FOR DYNAMIC PRICING STRATEGIES: A THEORETICAL FRAMEWORK FOR COMPETITIVE ADVANTAGE

ABSTRACT
Dynamic pricing—the practice of adjusting product or service prices in real time
based on market demand, competition, and other variables—has gained renewed importance
in the age of artificial intelligence (AI). With AI’s advanced predictive analytics, machine
learning algorithms, and big data integration, businesses can implement highly adaptive
pricing models that not only maximize revenue but also sustain competitive advantage. This
conceptual paper develops a theoretical framework that integrates AI-driven dynamic pricing
with strategic competitive positioning. Drawing upon pricing theory, resource-based view
(RBV), and AI adoption models, the study explores how organizations can leverage AI
capabilities to respond to market volatility, enhance customer satisfaction, and optimize
profitability. The proposed framework identifies four key constructs—data acquisition
capability, algorithmic adaptability, customer-centric personalization, and competitive market
intelligence—that jointly drive sustainable competitive advantage through AI-enabled
dynamic pricing. By synthesizing insights from recent literature (2018–2025), this paper
offers both academic and managerial implications, highlights ethical considerations, and
outlines future research opportunities. The framework is intended to guide marketing and
pricing professionals, technology strategists, and policy makers in designing AI-powered
pricing systems that balance profitability with fairness.
Keywords: Artificial Intelligence, Dynamic Pricing, Competitive Advantage,
Predictive Analytics, Machine Learning, Marketing Strategy