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the Impact of AI and Generative AI on Organizational Dynamics

Mustafa El Sayed

the Impact of AI and Generative AI on Organizational Dynamics.

Rel. Paolo Neirotti. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2024

Abstract:

This thesis investigates the adoption and impact of Generative AI and AI tools at the Freudenberg Group, with a focus on Corteco's operations throughout Europe. It examines how these technologies are reshaping various aspects of work, from productivity and decision-making to innovation and employee concerns, particularly around job security and the future of work. The research reveals a significant uptake of AI tools among employees, leading to notable improvements in work efficiency and creativity. This adoption is not uniform, however; it varies with the employee's experience level, suggesting a generational shift in technology usage at the workplace. While AI is seen as a catalyst for new ideas and work processes, it also raises concerns among employees about job displacement. Yet, there's a prevailing optimism that the changes AI brings will also create new opportunities. A gap in AI adoption is identified among those less familiar with the technology, attributed to a lack of awareness about its potential benefits. This highlights the need for better education and communication strategies within the organization. From a management perspective, there's a cautious but positive outlook towards AI integration. However, concerns about data security and the desire for more targeted training indicate areas where management's support for AI could be strengthened. This study offers valuable insights into the evolving role of AI in the workplace, underscoring the need for strategic alignment between technology adoption, employee development, and organizational goals. It provides a balanced view of AI's benefits and challenges, contributing to a more informed approach to integrating AI technologies in the corporate environment.

Relators: Paolo Neirotti
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 130
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: Corteco Srl
URI: http://webthesis.biblio.polito.it/id/eprint/31422
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