Rapid changes in the development of artificial intelligence technologies, especially agentic AI systems with the ability to autonomously reason and adapt to new learning, are fundamentally transforming the conceptualization, production, and further development of digital products throughout their performance life cycles. Rigid Software Development Life Cycle designs were created for deterministic software systems that operated on linear or iterative delivery cycles, but struggle to adapt to products powered by AI that learn and optimize after release. This operational and conceptual change from SDLC to a Product Development Life Cycle is enabled by the integration of agentic AI and supported by continuous process optimization mechanisms. The combination of available literature, business experience, and business governance models supports a systematic PDLC model that examines optimization across lifecycle phases. The implications for quality engineering, governance arrangements, and the operating model of the organization are thoroughly discussed in the context of telecommunications, aviation, and pharmaceutical industries. This transformation is not just an improvement in methods but a strategic development of outcome-focused, self-optimizing product ecosystems in which human-AI cooperation balances strategic control with autonomous tactical actions, fundamentally rearranges competitive dynamics and organizational capabilities to provide a lasting benefit.