Digital Transformation Induced by Exogenous Forces: Demographics, Educational Mismatch, and Monetary Policy as Structural Accelerators of Labor Automation

Abstract Labor automation has been predominantly explained as an endogenous phenomenon driven by corporate competitive strategies. This article proposes an alternative analytical framework: the contemporary wave of automation increasingly responds to structural exogenous forces that transcend the decision-making domain of individual economic agents. Using a sequential‐ explanatory mixed‐methods design (Creswell & Plano Clark, 2018), three vectors are examined: (i) population aging and contraction of the working‐age population (WAP); (ii) the structural mismatch between educational competencies and labor market demands; and (iii) the distributive effects of unconventional monetary policies on the relative capital‐labor price. The analysis integrates demographic projections from the U.S. Census Bureau and UN DESA (2024‐2100), the systematic coding of 1,507 occupational tasks (O*NET 27.2; Cohen’s Kappa = 0.81), and an exploratory analysis of central bank macroeconomic time series (2008‐2025). Results indicate that, in the Business and Finance sector, only 33.9% of tasks exhibit high automatability, while 50.2% require human‐AI synergy, evidencing a reconfiguration process rather than massive substitution. An inverse correlation between wage levels and automatability is confirmed (30.1% in the top quartile vs. 37.8% in the bottom quartile), along with a significant negative correlation (r = –0.63; p < .01) between relative capital‐labor price variation and robot density across 18 OECD countries (2008‐2024). Demographic projections indicate WAP contractions exceeding 30% in Europe and South America by 2100. The study’s central contribution is the integration of these vectors into a model of preventive structural acceleration, with direct implications for public policy design in professional retraining and social protection. Keywords: digital transformation; exogenous forces; labor automation; demographic aging; educational mismatch; quantitative easing; labor market; public policy. JEL Codes: J11, J21, J24, O33, E52, E24, I21