Product development can be understood as a dynamic socio-technical ecosystem comprising interdependent elements such as products, people, processes, methods, tools, ICT environments and their contexts. This ecosystem perspective emphasizes the evolutionary and adaptive nature of product development environments, where change is not isolated but propagated through complex networks of actors, processes, tools and infrastructures. Influences from regulatory transformations, sustainability demands, and advances in digital technologies continuously reshape these ecosystems, requiring an integrated, systemic understanding to navigate and manage their evolution effectively. Within this framework, methods and tools are not static instruments but co-evolving entities embedded in a larger system of development practices. Their successful implementation depends on coherence with existing processes and compatibility with the knowledge and competencies of stakeholders. New tools often emerge from technological innovation—particularly in artificial intelligence and data analytics—and in turn reshape the methods that support development activities. When these changes are not aligned or are poorly integrated, they can generate disruptions analogous to ecological imbalances, leading to inefficiencies or resistance to adoption. The increasing complexity of products, including cyber-physical and product-service systems, demands interdisciplinary collaboration and context-specific integration of practices. Disciplines such as mechanical engineering, computer science, and service design bring distinct approaches and expectations, making harmonization of processes and terminology essential. Organizational challenges, such as varying digital competencies and generational shifts in work habits, further contribute to the dynamic character of development ecosystems. Artificial intelligence represents a potentially disruptive force within these ecosystems. While current applications primarily automate repetitive or data-intensive tasks, future systems are expected to support decision-making, contribute creatively, and operate as learning entities within development teams. Such transformation requires rethinking roles, workflows, and even the tacit knowledge that underpins engineering expertise. The interplay between human and machine intelligence is poised to redefine not only how products are developed but also how innovation processes are conceived and managed. Effective adaptation to these changes depends on maintaining the internal coherence of the product development ecosystem. Tools and methods must evolve in concert, and their interdependencies must be understood and respected. Changes in one element often necessitate adjustments in others; otherwise, benefits may not materialise, or unintended consequences may arise. Ecosystem resilience depends on recognizing these interconnections and managing change systematically rather than through isolated interventions. Product development is undergoing a paradigm shift that calls for continuous reassessment of practices, tools, and frameworks. Understanding the co-evolution of tools and methods within evolving ecosystems is critical to supporting innovation, improving resilience, and sustaining competitiveness in a rapidly changing environment. A robust response to the ongoing transformation requires interdisciplinary research capable of addressing the socio-technical, organizational, and technological dimensions of change.