AI Integration in Organisational Workflows: A Case Study on Job Reconfiguration, Efficiency, and Workforce Adaptation

Abstract

This study investigates how the integration of artificial intelligence (AI) transforms job practices within a leading European infrastructure company. Grounded in the Feeling Economy framework, the research explores the shift in task composition following AI implementation, focusing on the emergence of new roles, required competencies, and the ongoing reconfiguration of work. Using a qualitative, single-case study methodology, data were collected through semi-structured interviews with ten employees and company documentation. Thematic analysis revealed five key dimensions: the reconfiguration of job tasks, the improvement of efficiency and quality, psychological and adaptation challenges, the need for AI-related competencies, and concerns about dehumanisation. Findings show that AI systems increasingly assume repetitive and analytical tasks, enabling workers to focus on strategic, empathetic, and creative responsibilities. However, psychological resistance, fears of job displacement, and a perceived erosion of human interaction present implementation barriers. The study provides theoretical contributions by empirically extending the Feeling Economy and task modularisation frameworks. It also offers managerial insights into workforce adaptation, training needs, and the importance of ethical and emotionally intelligent AI integration. Additionally, this study highlights that the Feeling Economy must address AI’s epistemic risks, emphasising fairness, transparency, and participatory governance as essential for trustworthy, emotionally intelligent, and sustainable AI systems.