A Review on the Implementation of Artificial Intelligence for Real-Time Product Pricing and Demand Forecasting Optimization

Abstract

In the contemporary, fast-paced global market environment, organizations need to respond swiftly and effectively to changing consumer expectations, rival pricing schemes, and fluctuating economic conditions.

A paradigm shift from traditional, heuristic-based approaches to data-driven, adaptive, and scalable solutions is represented by the incorporation of Artificial Intelligence (AI) into pricing and demand forecasting processes. Artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and reinforcement learning (RL), have shown remarkably effective at identifying latent demand patterns, capturing non-linear relationships, and facilitating real-time dynamic pricing decisions. With a focus on their algorithmic underpinnings, implementation designs, real-world applications, and quantifiable effects on business performance, this paper summarizes the most recent state-of-the-art AI techniques used in product pricing and demand forecasting optimization.The paper also describes potential research routes and discusses current issues such data heterogeneity, model interpretability, and computational complexity. Clarifying the strategic role of AI in promoting revenue optimization, raising customer happiness, and boosting overall operational efficiency across a range of industrial areas is the key goal.