Artificial Intelligence as a Driver of Sustainable Organizational Performance: A PLS-SEM Study of Administrative Staff in Educational Institutions in Perú

Artificial intelligence (AI) is transforming organizational processes and workforce capabilities across multiple sectors, generating important implications for sustainable organizational performance. In educational institutions—an underexplored organizational context—administrative staff represent a critical workforce segment whose competencies, adaptability, productivity, and decision-making capacity directly shape institutional sustainability. Yet empirical evidence on how AI adoption affects these outcomes in emerging economy educational settings remains limited. Addressing this gap, the present study examines the predictive relationships between AI adoption and four organizational sustainability indicators: job competencies (CL), resistance to change (RC), administrative productivity (PA), and decision-making autonomy (ATD) among administrative personnel in educational institutions in Chimbote, Peru. A quantitative, cross-sectional, non-experimental design was employed, using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. Data were collected from 98 administrative staff members across 54 educational institutions. The measurement model confirmed adequate reliability, convergent validity, and discriminant validity across three constructs; however, the Resistance to Change construct exhibited insufficient internal consistency reliability (Cronbach’s alpha below .70) and weak indicator loadings, failing to meet recommended PLS-SEM thresholds [77,81] and precluding its inclusion in the structural model. The structural results indicate that AI adoption exerts a positive and statistically significant predictive association with job competencies (β = 0.627, t = 11.55, p < 0.001), administrative productivity (β = 0.589, t = 9.885, p < 0.001), and decision-making autonomy (β = 0.398, t = 5.267, p < 0.001). The three empirically testable hypotheses (H1, H2, H3) are supported; H4 (Resistance to Change) could not be tested due to measurement reliability constraints. These findings position AI as a substantive driver of sustainable organizational performance in resource-constrained educational contexts, offering empirical evidence from a Latin American emerging economy perspective in alignment with Sustainable Development Goals 4, 8, and 9.