Ndubuisi Ugwuanyi, Phd Candidate in Management, The Wharton School
Abstract: Entrepreneurial firms operate in environments characterized by rapid technological change, shifting demand, and heightened uncertainty. While prior research has identified factors associated with startup growth and survival, much of this work relies on static, ex post measures and offers limited insight into how firms adapt strategically in real time. This project proposes artificial intelligence as a strategic lens for studying entrepreneurial adaptation and scaling, examining whether AI-based methods can detect early signals of organizational change that predict subsequent growth.
Leveraging large-scale, longitudinal data on startups’ digital footprints, including website content, hiring patterns, and organizational roles, this study develops machine-learning models to capture how firms update their strategies, capabilities, and market positioning over time. By analyzing patterns of textual evolution and structural change, the project seeks to identify whether adaptive behaviors can be measured ex ante and linked to future scaling outcomes such as sustained growth, continued funding and favorable exits.
The project contributes to research on entrepreneurship and strategy. Substantively, it advances understanding of how startup firms respond to changing environments and which forms of adaptation are most strongly associated with scalable and sustainable growth. Methodologically, it demonstrates how AI can be used to extract strategic signals at scale. More broadly, the findings have implications for investors, policymakers, and founders seeking to identify and support high-potential ventures before performance outcomes are fully realized.