Synchronizing AI investment and leveraging strategy in entrepreneurship: a machine learning approach

Purpose Artificial intelligence (AI) has become a critical driver of entrepreneurial development, yet entrepreneurial ventures differ markedly in how much they invest in AI relative to rivals and in how effectively they convert such investments into performance gains. Drawing on resource orchestration theory, this study aims to examines how AI investment relative to rivals influences venture performance and how this effect depends on an innovation-focused leveraging strategy. Design/methodology/approach Using a sample of 299 ventures listed on China’s Science and Technology Innovation Board (STAR Market) from 2019 to 2023, their benchmark each firm’s AI investment intensity against its industry peers. To address high dimensionality and improve estimation reliability, they use multiple machine learning techniques, including standard least absolute shrinkage and selection operator regression (LASSO), adaptive LASSO, Elastic Net and Partialing-Out LASSO. Findings The results show that deviating from rivals’ AI investment levels, whether above or below the industry mean, is associated with lower venture performance, whereas conforming more closely to rivals’ AI investment norms is associated with higher performance. However, the performance penalty of investing more heavily in AI than rivals becomes weaker when such investment is aligned with an innovation-focused leveraging strategy. By contrast, the performance penalty of investing less in AI than rivals becomes stronger when a venture simultaneously adopts an innovation-focused leveraging strategy. Practical implications These findings suggest that AI investment does not generate superior outcomes in isolation. Entrepreneurial ventures need to align the scale of AI investment with an appropriate leveraging strategy to avoid misallocation and improve value creation. Managers should therefore evaluate not only whether to invest more or less in AI than rivals, but also whether their post-investment strategy is capable of effectively leveraging such commitments. Originality/value This study extends research on AI in entrepreneurship by shifting attention from the absolute level of AI investment to AI investment relative to rivals. It also contributes to resource orchestration research by showing that the performance consequences of AI investment depend on its synchronization with leveraging strategy, thereby offering a more nuanced understanding of when AI investment can become a source of resource-based advantage.