The Role of Artificial Intelligence in Advancing Industrial Productivity and Economic Leadership in the United States

Abstract

This study analyzes how far the U.S. can go in leveraging artificial intelligence (AI) to enhance productivity in the industrial sector and, ultimately, its role as the world's economic leader. The survey was conducted using a cross-sectional survey design with a sample of 250 managers, engineers and executives in U.S. manufacturing, technology, automotive, aerospace and defense, and energy companies. A five-point Likert scale survey was used to assess four constructs: AI adoption, process automation, data-driven decision making, industrial productivity, and economic leadership. The hypothesized relationships were tested using descriptive statistics, Pearson correlation and multiple regression analysis. Results show that process automation, data-driven decision making and the adoption of AI are each important positive predictors of industrial productivity, together accounting for 55.1% of the variance in industrial productivity (R² = .551, F(3, 246) = 100.52, p < .001). The perceived economic leadership, in turn, was the strongest predictor of industrial productivity (β = .276, p < .001), besides the direct effect of AI adoption and data-driven decision making. The results validate an argument that AI is a general-purpose technology that increases the productivity of firms and that, overall, improves national competitiveness. Managerial, policy and future research implications are discussed.