Xiangdian Emma Chen
Topic
Rethinking Business Models at the Ecosystem Level towards Sustainable Goals: From Theorization to AI-enhanced Tool Development
Currently working on:
Finalizing the revisions of my second journal paper, which reveals the dynamic mechanisms and adaptive patterns of how an ecosystem-based business model (EBM) operates and evolves. It builds on my recently published review paper.
According to Gartner’s 2025 survey, 77% of CEOs believe AI will be the most disruptive and defining force in their industries, yet 66% admit their current business models are not suited for leveraging AI effectively. Alongside digital transformation, sustainability has become an essential strategic priority for managing uncertainty. However, an IBM 2024 global survey reveals that although organizations integrating sustainability throughout their operations achieve stronger financial performance, only 31% significantly embed sustainability data into operations, and a mere 14% do so through innovative initiatives.
These highlight a growing managerial challenge: designing adaptive sustainable business models capable of responding to increasingly complex, data-driven, and dynamic industrial ecosystems. Existing frameworks offer structural guidance but fail to capture the dynamic and evolutionary nature of business models, particularly in Industry 4.0 and sustainability contexts.
My research at LUT University, conducted within the IWM pilot program, addresses these needs by developing a generic AI-enhanced Business Model Innovation (BMI) tool for Industry 4.0, aimed at supporting sustainable growth. Building on my previous DBA research and work experience in the IoT ecosystem strategy domain, this research reveals the underlying logic of business models as open systems interacting dynamically within ecosystems, drawing on General Systems Theory, rooted in biology. The current phase marks a transition from the systemic theorization to empirical exploration, involving a forthcoming case study that applies AI and sustainability data in reshaping ecosystemic business models in industries with Intelligent Work Machines (IWM).
The AI-enhanced BMI tool will enable real-time modeling of ecosystem dynamics through modules such as value proposition alignment, value co-creation mapping, predictive risk management, service configuration generation, key stakeholder simulation, and capacity optimization. This modularized approach reduces design time for “everything as-a-service (XaaS)” models, improving business model viability, and optimizing ecosystem-level resource allocation and collaboration. Ultimately, it contributes to embedding sustainability principles into innovation processes and strategic decision-making.
Existing influential BMI tools, such as the Business Model Canvas (BMC), are good for static structure mapping, but not for dynamic and evolution modeling. Existing BMI frameworks, including several modified versions of BMCs, lack a coherent theoretical foundation. The proposed AI-powered tool, grounded in systems theories, uniquely suits to the needs of data-driven, sustainability-oriented ecosystems characteristic of Industry 4.0, offering both theoretical depth and practical utility.