Opportunities and Barriers in Global AI Adoption

The diffusion of AI across national economies and industry sectors does not follow a uniform trajectory. Unlike previous waves of technological change, whose adoption patterns were primarily constrained by physical infrastructure and capital investment, AI diffusion is shaped by a more complex configuration of factors: the availability and quality of data, the depth of digital ecosystems, the institutional capacity to govern emerging technologies, and the stock of human capital capable of developing and deploying intelligent systems. These factors are distributed unevenly across countries and regions, producing adoption trajectories that diverge substantially in speed, scope, and organizational form. Understanding this heterogeneity is not merely a descriptive exercise: it has direct implications for the competitive positioning of firms operating internationally, for the strategies of multinational enterprises seeking to leverage AI across diverse institutional contexts, and for the policy frameworks through which governments seek to shape national AI capabilities. This chapter examines the opportunities and barriers associated with global AI adoption through the lens of international business theory and institutional economics. It draws on technology diffusion theory, the institutional perspective in international business, and the emerging literature on digital globalization to develop an analytical framework for understanding how adoption trajectories differ across contexts and what this means for firms competing in the global AI economy.