This article explores the synergistic integration of generative artificial intelligence (GAI) and autonomous digital agents (DAs) in modern logistics systems, an area of increasing strategic importance. By exploring the convergence of analytical sophistication and operational automation, the study shows how these technologies are redefining the management of resource flows in logistics orchestration, transportation optimization, and intelligent allocation of warehouse space, considering interdependencies from production networks to end-consumer delivery ecosystems. The research systematically explores GAI-DA applications in logistics service innovation and demonstrates its ability to enhance collaboration between organizations and adaptive decision-making in complex multi-stakeholder environments. Through a dual lens of theoretical exploration and empirical analysis, the article advances strategic imperatives for management by highlighting how these technologies catalyze value chain optimization, stakeholder engagement paradigms, and interdisciplinary innovation at the intersection of corporate marketing, operational agility, and technological adoption. Joining the literature on AI-driven organizational transformation, this work goes beyond descriptive analysis by suggesting a holistic business model realignment. It introduces a novel framework that conceptualizes GAI-DA implementations as interdependent systems that require synchronized evolution across four fundamental pillars: precision-engineered processes, highly skilled people capital, strategic partner ecosystems, and purpose-driven technological platforms (the 4Ps). The ‘four Ps’ framework is a comprehensive approach to GAI-DA implementation, emphasizing the importance of aligning processes, people, partners, and purpose-driven technological architecture for successful integration. The study concludes that successful implementation requires more than just the integration of algorithms—it requires redesigning organizational structures to adapt to dynamic logistics ecosystems. As a pragmatic contribution, the work proposes actionable implementation guidelines for embedding GAI-DA solutions into core logistics functions. These include protocols for data management in multi-agent environments, adaptive workflow redesign for human-AI collaboration, and metrics for evaluating performance improvement across the ecosystem. The research advances the scientific approach to smart logistics systems by linking theoretical insights with operational designs. It provides practitioners with a roadmap to exploit GAI-DA organizations at the forefront of innovation while meeting the effective, ethical, economic, and efficiency requirements in an era of hyper-connected logistics.