Journal of Global Economics, Management and Business Research

Adaptive AI-driven reskilling functions as a bridge between digital transformation and competency development primarily through three mechanisms: data-driven skill-gap identification, personalized learning pathways, and real-time learning feedback and adjustment. Recent evidence from 2024–2026 strengthens this interpretation, although pooled estimates and adequate longitudinal evidence remain unavailable. In an Indian IT study, all seven AI-enabled learning and development features were positively and significantly associated with skill development. Engagement through AI systems was the strongest predictor (β = 0.371), whereas platform accessibility had the lowest coefficient (β = 0.082), and the model explained 92.4% of the variance in perceived skill development (Vetrivel et al., 2026). Another study reported that adaptive learning had the strongest influence on reskilling effectiveness compared with content personalization (Juman et al., 2025). In web-based vocational training, dynamically generated learning pathways increased completion by 8–14% and satisfaction by 0.7–1.2 points compared with the baseline (Zhou & Shu, 2026). Recent reviews and implementation studies similarly identify personalized pathways, automated skill-gap analysis, real-time feedback, and AI-enabled coaching as central mechanisms, while emphasizing persistent concerns about long-term transfer, algorithmic bias, privacy, and unequal access (Chen, 2024; Malge et al., 2026; Prabhakar et al., 2026).

However, this bridge does not emerge automatically. Personalization must be aligned with business strategy, role requirements, human engagement, ethical governance, and equitable access. Full automation during training reduced autonomy, motivation, engagement, and skill acquisition, whereas partial automation produced better outcomes (Passalacqua et al., 2024). Major gaps include limited evidence of long-term learning transfer, dependence on self-reported measures, inadequate representation of vulnerable and marginalized workers, incomplete data concerning workforce inclusivity, and insufficient testing of algorithmic bias across demographic and occupational groups. Accordingly, adaptive AI is best understood not as a replacement for human development, but as connective infrastructure that translates digital transformation agendas into relevant, continuous, measurable, and socially responsible learning.