Dynamic Pricing 2.0: How AI Is Revolutionizing Real-Time Pricing Strategies

Abstract

Dynamic pricing has progressed to very sophisticated AI based systems that can make real time, data informed decisions. In this paper, the author explores how Artificial Intelligence (AI) is changing the game of pricing in various industries such as retail, e-commerce, ride-hailing, and B2B services. Using a secondary quantitative research design, we study peer-reviewed research studies and industry reports whose accuracy has been verified to measure the performance of AI methods like reinforcement learning, deep neural networks, and Bayesian optimization. The findings indicate that AI-based pricing systems are much more effective than the traditional ones, which increase revenues by up to 22% and operational excellence indicators, including fleet utilization and inventory turnover. Nevertheless, a positive or neutral customer sentiment was observed in such industries as retail, e-commerce, but negative or mixed in B2B SaaS and ride-hailing, where the issues of transparency and fairness in pricing are also raised. The study also highlighted the significance of algorithmic responsiveness where AI models can update prices in seconds, which is much more responsive than legacy systems. Although these benefits are high, there are still ethical issues regarding algorithmic discrimination, explainability, and regulatory compliance that are not adequately covered in the literature. The paper will end with a recommendation of responsible implementation frameworks that will ensure a balance between profitability, transparency, and accountability. The observations are used to develop future AI-based pricing ecosystems that would not only be efficient but also ethical and user-oriented.