Making Artificial Intelligence Work: Organizational Mediation and Employee Experience in Multinational Firms

Despite the rapid growth of artificial intelligence investments in multinational corporations, the organizational and human aspects of artificial intelligence integration remain analytically underdeveloped. Although the literature has contributed substantially to the development of a differentiated view of macro-level enablers of artificial intelligence integration, such as strategic leadership, organizational readiness, cultural orientation, and technical infrastructure, it has conceptualized employees as recipients of organizational initiatives and has failed to address the experiential, interpretive, and sensemaking processes of artificial intelligence integration in practice. The current thesis seeks to address this issue by examining the effects of artificial intelligence integration on employees in multinational corporations and by examining the organizational processes that shape employee orientations towards artificial intelligence integration. This study is grounded in a qualitative research approach and utilizes eight semi-structured interviews with senior professionals in two multinational fast-moving consumer goods (FMCG) corporations and Gioia data analysis methodology. The results are organized along four dimensions of aggregated findings: structural reconfiguration of work, wherein AI transforms work processes, jobs, and the hierarchy of valued tasks; capability reconfiguration pressures, which show a dependency sequence in organizational learning needs; perceptual and cognitive tensions, wherein employees develop and increasingly refine their orientations towards AI in an uncertain world; and organizational mediation and enablement, which subsumes governance structures, strategic framing practices, leadership activation, and cultural mechanisms through which organizations influence the above three dimensions. The dimensions are synthesized into a recursive process model, wherein AI adoption is conceptualized as a path-dependent, iterative process, not a singular event. An important theoretical contribution of the model lies in proving that organizational mediation is pervasive, not sequential, influencing the nature of structural reconfiguration at all stages, not merely in reaction to tensions. An important contribution of the research lies in advancing the socio-technical systems perspective by emphasizing employees' roles as agents in constructing their orientations towards AI, which in turn constitute adoption outcomes. An important managerial implication of the research is that AI adoption success is not dependent on technological capabilities per se, but on the quality of the organizational architecture in which human interaction with AI occurs.