Software release management coordinates planning, gating, deployment and monitoring in CI/CD-intensive environments where rapid iteration compresses decision windows and increases risk. This paper examines how Analytical AI and Generative AI support those decisions. Based on qualitative interviews with industry experts, we identify key benefits of AI integration, such as increased automation of repetitive tasks, more efficient resource allocation, enhanced risk forecasting, and improved over-all decision-making. These advances support faster, more reliable software deployments by streamlining processes and enabling data-driven insights. However, the study also highlights significant hurdles to effective AI/ML adoption, including integration complexity, the opacity of AI models, and organizational resistance. Addressing these challenges proves essential for realizing the full potential of AI in software release management. By examining both the practical advantages and limitations, this research offers actionable guidance for industry practitioners and contributes to broader discussions on the responsible application of AI in contemporary software product management.