Artificial Intelligence Adoption, Digital Infrastructure, and Business Productivity: A Cross-Country Empirical Analysis Using Secondary Data

Artificial intelligence (AI) has emerged as a transformative technology reshaping business operations and economic productivity across industries and countries. As organizations increasingly integrate AI-driven systems into operational processes, understanding the relationship between AI adoption and productivity outcomes has become a critical research concern. This study employs a cross-country quantitative secondary data analysis using harmonized datasets from the OECD Artificial Intelligence Policy Observatory, the Stanford Artificial Intelligence Index, the World Bank World Development Indicators, and the McKinsey & Company. The sample includes five economies representing varying levels of digital development. Descriptive statistics, correlation analysis, and multivariate regression modeling are applied to examine relationships among AI adoption, digital infrastructure, and business productivity. Findings reveal a positive and statistically significant effect of AI adoption on productivity, with digital infrastructure strengthening this relationship. Industries characterized by higher data intensity demonstrate greater AI adoption rates. The findings contribute a structured cross-country empirical framework linking artificial intelligence adoption, digital infrastructure, and productivity performance across developed and emerging economies. The results emphasize the importance of technological readiness, innovation ecosystems, and data-driven capabilities in supporting productivity growth within digitally transforming economies. The findings provide insights for policymakers, leaders, and digital transformation strategists seeking to strengthen economic competitiveness.