Human–AI Collaboration in Product Management: A Systematic Review of AI-Augmented Decision-Making Across the Product Lifecycle

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Artificial Intelligence (AI) is gradually changing the landscape of product management by improving how PMs gather, process, and evaluate data and options to manage products across their lifecycle. This review explores the new paradigm of Human-AI collaboration in the product management domain and looks at how AI functions effectively on the side of the product manager. It merges a variety of machine learning, predictive analytics, NLP, recommender systems, generative AI, and emerging agentic AI applications in customer and market discovery, product ideation, strategy and prioritization, road mapping, product development, product launch, and post-launch optimization. Specific focus is placed on task distribution, responsibilities and decision-making power between product manager and AI system. The study suggests that AI can offer many benefits in areas such as data analysis, pattern recognition, prediction, information synthesis, content creation, recommendations, and automation, while human involvement is still crucial for aspects of context interpretation, strategic decision-making, creativity, empathy, ethical considerations, and accountability. Yet, collaboration is hindered by issues such as algorithmic bias, hallucination, opacity, automation bias, over-reliance, privacy issues, deskilling and unclear accountability. The review thus suggests a framework of integration between product lifecycle activities, AI capabilities, human expertise, collaboration mechanisms, governance and organizational outcomes. The framework also emphasizes how product managers will be moving away from AI-enabled product management and toward agentic systems where product managers will take on more supervisory, orchestrating, evaluating and governing roles. Lastly, the review pinpoints research priorities related to human-AI task allocation, trust calibration, autonomous agents, measurement of collaboration, responsible AI, and end-to-end empirical evaluation.