Rethinking Competitiveness in the Age of AI: A Comparative Index-Based Approach

Abstract

This study examines the influence of artificial intelligence (AI) capabilities on national competitiveness through a comparative analysis of the IMD World Competitiveness Index and three major AI indices: Oxford AI Readiness, Tortoise AI Index and Stanford AI Index. Utilizing correlation analysis, multiple regression and K-means clustering across samples of 64, 59 and 35 countries, respectively, the research identifies infrastructure and research capacity as key predictors of national competitiveness, with regression models explaining 52.4%–60.8% of IMD variance and Pearson correlations exceeding 75% for predictive validity. Clustering analysis delineates AI-advanced nations (A2 cluster) with superior AI performance relative to national competitiveness and resource-dependent laggards (C2 cluster) at risk of stagnation without AI investment. The study proposes open innovation strategies, inspired by collaborative ecosystems like shared mobility, leveraging government-industry-academia partnerships and digital public infrastructure (DPI) to address gaps in government policy, research capacity and infrastructure, with case studies of the United States and Singapore. For Least Developed Countries (LDCs), a 2 × 2 strategy matrix outlines low-cost, high-impact AI initiatives to enable a bypass strategy, leveraging open innovation ecosystems to circumvent traditional industrial pathways. Findings underscore AI's transformative role in redefining competitiveness, driven by qualitative capabilities like efficiency, innovation and governance, offering actionable pathways for advanced economies and LDCs to close competitiveness gaps through strategic AI integration and DPI investments.