How Artificial Intelligence Shapes Social Change: Adoption, Power, and Governance

Public and academic debates about Artificial Intelligence (AI) shift between polarized expectations. Optimistic accounts anticipate far-reaching societal transformation, while skeptical perspectives dismiss AI as technological hype with limited real-world consequences beyond marginal efficiency gains and new inequalities. This article argues that both positions obscure how technologies actually shape societies. Drawing on insights from innovation studies, organizational sociology, and theories of social power, it advances a general theory of AI-induced social change that conceptualizes impact as contingent, stepwise, and mediated. The theory models AI's societal effects as a causal chain linking scientific breakthroughs, technological innovation, organizational and individual adoption, uneven gains in relative advantage, shifts in power within and between organizational networks, governance responses, and structural constraints. Central to this framework is the claim that AI alters social outcomes primarily by enabling some actors to pursue their goals more effectively than others, thereby generating localized power shifts. Whether such shifts translate into broader social change depends on subsequent processes, including regulatory interventions, political acceptance, and material, institutional, and societal constraints. By integrating micro-, meso-, and macro-level processes, the framework helps explain why AI can produce significant change in specific domains without leading to immediate or uniform transformation at the societal level. It also clarifies why the absence of macro-level change does not imply the absence of meaningful impact. The theory provides a framework to support cumulative research on AI's societal consequences and to inform more precise and grounded public discussions of AI-enabled social change.