Rebooting the Workforce: How MNCs Manage Skill Development for AI-Driven Transformation

Abstract

Artificial intelligence is reshaping work tasks, roles, and required competencies faster than many organizations can adapt. For multinational corporations operating across geographically dispersed units, this creates a challenge: how to develop and coordinate workforce capabilities for AI at scale, across diverse organizational and institutional contexts. This thesis explores how multinational corporations manage skill development for employees in response to AI-driven transformation. An interpretivist, qualitative research approach was adopted, using a multiple-case study design with deductive reasoning. Data were collected through semi-structured interviews with managers and specialists involved in AI adoption and skill development. The findings show that multinational corporations manage AI-driven skill development through widely different approaches, from formally structured executive driven programs to entirely informal, individually led experimentation. Governance, architecture, and deployment do not develop evenly or sequentially across the cases. Deployment consistently remains the least resolved dimension, with the primary barrier being behavioral rather than technical. The research concludes that AI-driven skill development in MNCs is best understood as a coordination problem rather than a training problem, and contributes a process oriented framework that bridges strategic HRM, knowledge transfer theory, and international business research on HQ-subsidiary coordination.