<p>The integration of Artificial Intelligence (AI) into various industries and domains is a pertinent and contemporary topic extensively discussed in the scientific literature. One such domain is product management, particularly focusing on New Product Development (NPD). The intersection of AI and product management presents substantial opportunities for research into the application of AI solutions to support product management activities. Drawing on a structured literature review of 190 publications and interviews with five experts in AI and product management, we map out AI applications across different phases of the new product development process. The findings indicate that significant results can be achieved only by disaggregating AI and product management into specific methods and phases and reveal a predominance of AI methods such as sentiment analysis, knowledge extraction, and demand forecasting in early-phase activities, with fewer studies examining AI’s application in later stages such as product testing, validation, and post-launch optimization. The study identifies several research gaps, including the underutilization of AI in concept testing, the limited focus on integrative AI solutions that span multiple NPD phases, and the absence of systematic frameworks for AI-driven product management. We propose a set of guidelines to facilitate the development of a cohesive, end-to-end framework. By situating AI within a broader managerial and organizational context, this paper offers both theoretical insights into AI’s evolving role in product development and practical recommendations for stakeholders seeking to leverage AI’s transformative potential.</p>