Harnessing AI for smart manufacturing: insights from Industry 4.0
D. A. B. Lindberg
Abstract
This paper empirically establishes artificial intelligence (AI) as a distinct factor of production within a macroeconomic Industry 4.0 model of United States manufacturing. Using a flexible translog production function estimated on monthly data, we account for nonlinearities, technological spillovers, and complementarities among labor, hardware, and AI investments. We use Google Trends search data as a proxy for AI adoption, validated against patent activity, to capture cross-sectoral spillover dynamics critical to general-purpose technologies. Our findings indicate that AI now functions as a separate, productivity-enhancing input, though its benefits, like traditional factors, may be subject to diminishing returns. Labor remains a vital input, complementary to IT capital. The analysis pinpoints a structural break in AI's influence on manufacturing output in late 2017, aligning with broader digital transformation trends. Business leaders should be strategic when selecting AI applications, and policymakers should invest in workforce development and training programs.