Feature prioritization may be one of the most nerve-racking aspects of product management, because it involves balancing stakeholder expectations, customer needs, and available resources. This paper presents an Adaptive Feature Prioritization Framework (AFPF), which is a new methodology that applies AI, machine learning (ML), natural language processing (NLP), and decision theory to make unstructured customer feedback and product information actionable. To make AFPF work, this study relies on data interpretation and processing that is free from human bias, readily adaptable to real-time changes, and performed autonomously. The building blocks of AFPF are effective data preprocessing, AI based feature analysis defines key areas and outputs such as prioritization matrices. Instead of relying on personal biased judgment, which is common with the traditional systems, AFPF enables product teams to resourcefully and intelligently pursue data-based instructions devoid of any advice. This paper discusses the primary characteristics of AFPF, which include the three key factors of success: scalability, adaptability, and accuracy, while explaining how success can be assessed. For large companies, using AI for prioritization of tasks increases products’ market fit, improves decision-making, and keeps pace with market changes.