When does artificial intelligence innovation pay in servitization? A combined organizational learning and socio-technical systems perspective

Artificial intelligence (AI) innovation is often celebrated as a powerful catalyst for servitization, yet its revenue implications remain paradoxical and underexplored. Building on organizational learning theory and socio-technical systems theory, this study theorizes and tests the nonlinear influence of AI innovation on firms’ service revenue, using panel data from 796 Chinese manufacturing firms between 2015 and 2022. It reveals an inverted U-shaped relationship, suggesting that while AI innovation initially enhances revenue through exploratory learning and customer value creation, excessive reliance may induce organizational rigidity and misaligned priorities that erode service outcomes. Moreover, coordination capability stabilizes AI innovation by moderating both its benefits and risks, while human capital amplifies these dual effects. These findings transcend the prevailing view of AI as a uniformly positive enabler, offering a more nuanced understanding of how organizational and technical subsystems interact to shape the trajectory of service-led growth.