Product roadmapping has historically relied on managerial intuition, customer feedback aggregation, and qualitative prioritization frameworks. While these approaches have supported innovation, they are increasingly insufficient in high-velocity digital markets characterized by data abundance, competitive volatility, and compressed iteration cycles. This paper develops a conceptual framework for AI-augmented product management, positioning artificial intelligence not as a replacement for managerial judgment but as a decision augmentation layer. Drawing from decision theory, bounded rationality, and adaptive systems research, the study proposes predictive roadmapping and adaptive feature prioritization models that integrate behavioral data, market signals, and probabilistic forecasting. It argues that sustainable competitive advantage will increasingly depend on the integration of algorithmic intelligence into product governance systems. The paper advances both theoretical and managerial contributions by reframing AI as a structural component of product decision architecture.