The article presents a comprehensive analysis of how generative technologies transform product management methods across different stages of the product lifecycle. The study is based on a systematization of modern approaches to applying generative models in ideation, user research, requirements formation, product design, go-to-market preparation, and post-release processes. The paper examines mechanisms that ensure the stability of decisions, including the use of synthetic data, scenario modeling, automation of analytical procedures, and agent-based coordination schemes. Particular attention is given to the asymmetry in method maturity, the impact of data quality on the reliability of conclusions, the requirements for interpretability, and the organizational constraints inherent in corporate environments. The analysis demonstrates that generative technologies reshape not individual stages but the structure of the entire product cycle, creating a continuous decision-making loop and redistributing responsibilities between analytical and engineering functions. The study concludes that the effectiveness of generative models is determined by the coherence of data, architecture, and development processes, which sets new requirements for integrating these technologies into complex products. The article will be useful for product management professionals, AI researchers, digital platform architects, and developers of corporate systems.

generative technologies, product management, product lifecycle, synthetic data, agent-based models, corporate systems, interpretability

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