AI and the Future of Work: Decomposing Displacement, Demand, and Labor-Market Stratification Toward 2030

Published

18 June 2026

Venue

IJIRT

DOI

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Abstract

Artificial intelligence (AI) is restructuring the global labor market through simultaneous displacement of routine-cognitive tasks and the creation of high-skill occupational demand. This study employs a cross-sectional dataset of 3,000 occupational records spanning 10 countries, 10 industries, and 20 distinct job titles to investigate the structural determinants of AI replacement risk and near-term hiring trajectories. Using a combination of descriptive statistics, Spearman rank correlation, one-way ANOVA, Mann-Whitney U, Kruskal-Wallis tests, Ridge regression, and Random Forest modeling, we find that the observed dataset exhibits a notably uniform distribution of AI replacement risk (x̄ = 0.503, σ = 0.261), with no statistically significant differences across automation levels, education strata, upskilling engagement, or AI tool usage in the as-generated synthetic cross-section. Future demand score is near-orthogonal to replacement risk (ρ = 0.008, p = 0.677), suggesting the dataset reflects a balanced design rather than a population drawn from actual occupational risk distributions. Predictive models (Random Forest) are expected to achieve high held-out R² on this dataset given its tabular structure, and multinomial classifiers achieve balanced accuracy for three-class hiring trend prediction. These results are interpreted within a theoretical framework of labor-market polarization, and we draw direct implications for workforce policy, organizational upskilling investment, and occupational re-design strategies. We also document where the synthetic dataset departs from empirical expectations and identify avenues for replication with real-world data.