A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

• Published in: Decision analytics journal Vol. 12; p. 100504
• Main Authors: Sun, Ying-Chih, Cosgun, Ozlem, Sharman, Raj, Mulgund, Pavankumar, Delen, Dursun
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2024
• Subjects: Artificial intelligence; Cobb–Douglas function; Innovation; Production efficiency; Stochastic production frontier; Production efficiency; Innovation; Cobb–Douglas function; Stochastic production frontier; Artificial intelligence
• ISSN: 2772-6622; 2772-6622
• DOI: 10.1016/j.dajour.2024.100504

As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study. •Explore Artificial Intelligence (AI) innovation’s impact on production efficiency globally.•Show AI innovation significantly influences economic output.•Demonstrate strong digital infrastructure is crucial for effective AI use.•Show that high-skilled labor is needed for AI integration, but its effect lessens as AI develops.•Exhibit the United States leads in AI innovation, the United Kingdom in production efficiency, and China lags in efficiency despite being fourth in innovation.