<![CDATA[In today’s data-driven economy, pricing strategies have become increasingly critical amid rapidly evolving market conditions. The integration of artificial intelligence (AI) offers new opportunities to optimize pricing decisions and strengthen competitive advantage. This study investigates the use of AI algorithms in optimizing product pricing within microeconomic contexts. Using a qualitative method and systematic literature review, it draws on publications from the past decade indexed in Scopus, DOAJ, and Google Scholar. The findings highlight that AI-based price optimization is shaped by several key factors: data availability, algorithm complexity, and the alignment of AI systems with existing business models. However, major challenges such as data bias, limited computational resources, and insufficient organizational readiness often hinder successful implementation. Despite these barriers, AI shows great promise in enhancing pricing accuracy, efficiency, and adaptability to market fluctuations. This research offers a comprehensive overview of the limitations and potential of AI in price optimization, emphasizing the importance of addressing technical and organizational challenges. It contributes to a deeper understanding of how AI can transform traditional pricing strategies and encourages further empirical research to explore its real-world applications within dynamic microeconomic settings.Keywords - Artificial Intelligence, Microeconomics, Pricing Algorithms, Price Optimization.]]>
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