Xiangdian Emma Chen

The rapid advancement of the Internet of Things (IoT) and artificial intelligence (AI) is reshaping business landscapes, with intelligent manufacturing at the forefront of the new industrial revolution. While these technologies optimize operations, their potential to drive business model transformation toward sustainable growth remains underexplored. Managers often struggle to design adaptable business models that can respond to increasingly complex and dynamic environments. Existing tools—such as the Business Model Canvas and the St. Gallen Business Model Navigator—outline structures but do not explain how business models evolve, particularly in Industry 4.0 ecosystems. This dissertation investigates how AI can enhance business model innovation and support tool development for sustainable growth in manufacturers using intelligent work machines (IWM). Drawing on system-related theories, including general systems theory, business ecosystems, and complex systems, it addresses key challenges in developing an AI-enhanced business modeling tool. Employing a mixed-methods approach—combining a theoretical paper, multiple case studies, and tool development—the research examines how business models operate and evolve as open systems across multiple ecosystem levels and how AI agents influence their transformation through decision-making in the IWM context. The resulting tool embeds sustainability into decision-making and operations while tracking business model evolution to achieve long term growth strategy, contributing to the intersection of AI, business model innovation, system dynamics, and sustainability, and offering practical insights for managers, policymakers, and researchers seeking sustainable competitive advantage in intelligent manufacturing.