Enthusiast, Optimist or Skeptic? Mapping AI Readiness Profiles for Workforce Upskilling

Are you an Enthusiast, an Optimist, or a Skeptic regarding AI adoption? The question is not merely rhetorical: as artificial intelligence reshapes labor markets at an accelerating pace, workers’ orientations toward AI-driven change vary substantially across attitudinal, institutional, and individual dimensions, and these differences have direct consequences for who seeks upskilling, who benefits from it, and who is left behind. This study develops a three-profile AI readiness framework and investigates which factors predict the intention to pursue AI tools training. Drawing on original survey data collected from 357 respondents in the South-East Development Region of Romania, it has been applied K-Means cluster analysis to identify three distinct workforce profiles: Optimists, characterized by positive AI attitudes and strong organizational preparedness; Skeptics, combining negative attitudes with near-absent institutional support; and Enthusiasts, whose high AI impact perception coexists with only moderate organizational readiness. Chi-square analyses reveal that AI literacy is the only competency priority that differs significantly across profiles, following an inverted gradient, the workers most exposed to displacement risk are least likely to prioritize it, a pattern we term the AI readiness inversion. Binary logistic regression further shows that training intention is predicted by personal preparedness and positive AI attitudes, but not by organizational readiness. These findings challenge the dominant policy assumption that institutional investment in digitalization automatically generates upskilling demand and point instead to attitudinal barriers as the primary constraint on training uptake. The three-profile framework offers a practical tool for designing differentiated upskilling interventions calibrated to the heterogeneous readiness configurations found within any given workforce or region.