Socio-Economic Impact of Generative AI on Workforce Transformation

Abstract

Generative Artificial Intelligence development is changing the way work processes, decision-making, and communication processes in different industries are structured. In operations and administration, Gen AI has become part of logistics and supply chain organizations too, and contributes to efficiencies and automation. However, there are broader socio-economic issues with the use of these technologies regarding the transformation of the workforce, the alteration of job roles, changing skill requirements, and the adaptation of organizations. Though the literature has been widespread in considering artificial intelligence in technological and productivity approaches, there are scarce studies about how the workforce is experiencing and perceiving the socio-economic impacts of Generative AI in organizational contexts, especially in the logistics and supply chain industries. This work bridges this gap by exploring how Generative AI has social-economic effects on transforming the workforce within logistics and supply chain companies in Finland.

The purpose of the research was to understand how workers experience and predict change in work practices, organizational processes, communication, skills requirements and human-AI collaboration upon the introduction of Gen AI technologies. The theoretical framework of the study was based on Socio-Technical Systems (STS) theory and Punctuated Socio-Technical Change (PSTC). The study adopted a qualitative research design using an abductive approach. Data were gathered empirically with the help of seven semi-structured interviews with the employees and managers who were employed in the logistics and supply chain organizations in Finland, particularly in Helsinki, Tampere, and Vaasa cities. The collected data were analysed using Abductive thematic analysis with the help of NVivo software.

The findings suggested that Gen AI is progressively becoming an encouraging tool in logistics and supply chain organizations because it improves the efficiency of workflows, reduces repetitive processes, accelerates the speed of information processing, and supports in operational decisions. Gen AI was received helpful as employees perceived it as a co-working tool and not a replacement for human labour. However, the results have also revealed challenges related to workforce adaptation, the increasing skills demands, technological dependence, doubts about the safety of information, and the uncertainty of future employment structures. The results also indicated that human supervision is still necessary in Gen AI assisted workplaces where issues pertaining to reliability and contextual accuracy of the AI-generated outputs exist. The study contributes to the existing literature with the qualitative empirical understanding of how the introduction of Gen AI affected employees in logistics and supply chain organisations. The study has practical implications for organizations and policymakers who seek to support workforce adaptation and human and AI integration in technological work environments.