🤖 AI Summary
This study addresses the growing misalignment between conventional product management frameworks and agentic AI—systems characterized by autonomy, goal-directed behavior, and multi-agent collaboration. Drawing on systems theory, coevolution theory, and human–AI interaction theory, we conduct an integrative analysis of over 70 scholarly works and industry case studies from leading technology firms to develop the first holistic “human–AI coevolution” framework spanning the entire product lifecycle (from discovery to launch). The framework explicates bidirectional adaptation mechanisms between product managers and AI agents. It reconceptualizes the product manager as a coordinator of sociotechnical ecosystems, delineating novel responsibilities in AI collaboration, oversight, and strategic alignment. We identify three core competencies: AI literacy, governance capability, and systems thinking. This work bridges critical theoretical gaps in AI-augmented governance, role evolution, and dynamic integration, offering both foundational theory and actionable pathways for organizations pursuing responsible, high-efficiency human–AI collaboration.
📝 Abstract
This study explores agentic AI's transformative role in product management, proposing a conceptual co-evolutionary framework to guide its integration across the product lifecycle. Agentic AI, characterized by autonomy, goal-driven behavior, and multi-agent collaboration, redefines product managers (PMs) as orchestrators of socio-technical ecosystems. Using systems theory, co-evolutionary theory, and human-AI interaction theory, the framework maps agentic AI capabilities in discovery, scoping, business case development, development, testing, and launch. An integrative review of 70+ sources, including case studies from leading tech firms, highlights PMs' evolving roles in AI orchestration, supervision, and strategic alignment. Findings emphasize mutual adaptation between PMs and AI, requiring skills in AI literacy, governance, and systems thinking. Addressing gaps in traditional frameworks, this study provides a foundation for future research and practical implementation to ensure responsible, effective agentic AI integration in software organizations.