Purpose This study examines how AI-enabled remote work influences productivity, employability and long-term career sustainability among Indian professionals. It develops and empirically operationalizes the AI–remote work career sustainability model (ARW-CSM) to explain how individual, organizational and technological factors shape AI adoption and its downstream career outcomes. Design/methodology/approach Using a cross-sectional dataset of 350 employees across multiple industries, the study employs descriptive analysis, two-way ANOVA, multiple regression, moderated regression and Random Forest predictive modelling. Composite scores capture determinants, AI adoption behaviour, productivity, employability and career sustainability. Feature importance analysis identifies the strongest empirical drivers of sustainable career trajectories. Findings Group-level differences across AI usage and work modes were minimal, indicating broad normalization of AI-supported work environments. None of the determinant clusters significantly predicted AI adoption, suggesting that adoption behaviours may be shaped more by job design or organizational mandates than by personal or contextual readiness. Productivity outcomes varied significantly by career stage, with mid- and senior-career employees deriving greater benefits from AI adoption. Predictive modelling revealed AI engagement, training intensity and human-capital variables as the most influential predictors of career sustainability. Practical implications The findings highlight the importance of targeted digital upskilling, structured AI engagement practices, and supportive organizational environments in strengthening career sustainability. Career-stage-specific developmental strategies are essential to maximize productivity outcomes in AI-enhanced settings. Originality/value This study offers a structured, empirically grounded framework for understanding sustainable careers in AI-mediated remote work environments. By integrating explanatory and predictive analytics, it advances career development research and provides data-driven insights into the evolving nature of technology-enabled work.
Pasi et al. (Wed,) studied this question.