THE GROWTH OF ARTIFICIAL INTELLIGENCE WITHIN THE BUSINESS CONTEXT: How the supply chain is changing and adapting

The paper aims to analyze the potential benefits and the managerial consequences of the introduction of artificial intelligence-based technologies. The thesis wants to individualize which key parts of the supply chain could be involved in this transformation, the complexities and difficulties linked to the integration process and to explain the differences between generative AI and the traditional one. The first chapter takes an in-depth look at the supply chain. The supply chain is a complex concept whose objective is to satisfy consumer demand, create the right network for stakeholders, and improve responsiveness. Nowadays, achieving these objectives is very complicated due to the constantly evolving geopolitical scenario, but technological implementation can help companies adapt and compete. The implementation of artificial intelligence is only one part of the digitization process that characterizes technological transformation, and the first chapter also analyzes all the other elements that comprise it. The second chapter of the study focuses on gaining a deeper understanding of the concept of artificial intelligence. It describes machine learning, deep learning, and artificial neural networks and their integration into different parts of the supply chain. Subsequently, generative artificial intelligence is also identified similarly, along with all the implications of this technology, which could be disruptive because it opens up novel possibilities for different activities throughout the supply chain. Technological evolution not only leads to innovation and improvement within the supply chain but also raises doubts and concerns about its use, especially from an ethical and data security perspective. This topic is discussed more prominently between the end of the second and third chapters, providing insights into how the successful introduction of these technologies depends on workforce involvement and adequate internal preparation. The fourth chapter focuses on how AI is changing demand forecasting and inventory management. In addition, a brief empirical section is developed to verify the reliability of AI in demand forecasting compared to some academic methodologies. The study concludes by focusing on the organizational challenges, strategic impacts, and future prospects associated with the introduction of AI and generative AI within the corporate context. In particular, it examines how barriers and resistance are changing and how companies need to review their business models and innovation policies.