Artificial intelligence (AI) and automation are reshaping skill requirements in the knowledge economy, placing increasing pressure on organizations to reconsider how employees acquire and update their competencies. Although research on AI-related workforce development continues to expand, the relationships among technological skills, organizational learning, and sector-specific upskilling strategies remain insufficiently integrated across disciplines. This study examines recent research trends in workforce upskilling and identifies the principal competencies and organizational responses associated with AI-driven transformation. A systematic literature review was conducted using EBSCO Discovery Service (a cross-database discovery platform), covering peer-reviewed publications from January 2022 to December 2025. Publication distributions, keyword frequencies, and recurring research themes were examined to characterize the development of the field. The analysis identified five interconnected areas: foundational digital literacy, AI-specific technical competencies, human-centered skills, organizational learning systems, and sector-specific adaptation strategies. The reviewed literature also showed a growing emphasis on continuous learning, AI-supported training, and the integration of skill development into organizational practices, although approaches differed across industries and institutional contexts. These findings indicate that workforce adaptation to AI depends on the interaction between individual competencies and organizational learning arrangements rather than technical training alone. By bringing together research from management, education, economics, and related fields, this review provides an integrated account of upskilling priorities and identifies directions for research on knowledge development, organizational adaptation, and workforce transformation.
Organizational learning depends on how knowledge is acquired, shared, and integrated across organizational units, yet the allocation of learning capabilities often overlooks differences in how employees interpret information and develop shared understanding. This study develops a mathematical decision-support model for allocating organizational learning capability (OLC) dimensions across organizational units by incorporating sensemaking into resource allocation decisions. Drawing on organizational learning and sensemaking theories, a binary optimization model was formulated to maximize the weighted contribution of selected learning capability items under unit-level budget limits, an organization-wide budget constraint, and minimum learning capability requirements. Sensemaking was incorporated through a unit-specific effectiveness coefficient and a minimum capacity constraint representing collective interpretation and shared understanding. A structured scoring procedure was specified to estimate the effectiveness coefficients, and the model was solved using LINGO (a commercial optimization software package) for a hypothetical organization comprising five units. Sensitivity analyses were conducted by varying sensemaking coefficients, minimum capacity requirements, and the total budget. The computational results produced different allocations of learning capability dimensions across the five units. Changes in sensemaking coefficients altered selected allocations, particularly in Unit 2, while tighter minimum sensemaking requirements affected Unit 4. Budget reductions also changed the selected dimensions, whereas budget increases left the allocation unchanged under the examined conditions. These findings indicate that incorporating sensemaking into learning capability allocation can account for differences in organizational contexts and identify how resource constraints shape knowledge management priorities. The model provides a structured basis for comparing unit-specific learning investments and examining alternative allocation decisions, although its effectiveness in organizational practice requires empirical validation.
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