Exploring Artificial Intelligence in the Decision-making Process

Artificial intelligence (AI) is a branch of computer science that has increasingly attracted the attention of academics and professionals. According to the perspective of Hosseini and Rossi, "Artificial intelligence refers to programs, algorithms, systems, and machines that imitate human intelligence in tasks such as learning, planning, and problem-solving by generating higher-level and indipendent knowledge". In recent years, AI systems have played an essential and significant role in organizational decision-making across various domains. These systems have created potential benefits for organizations, including workforce enhancement, cost reduction, increases human interactions, and the creation of desirable job opportunities. Moreover, AI systems include subfields such as machine learning, natural language processing, computer vision, and data learning, which are widely used by organizations to integrate AI into their decision-making processes. This thesis adresses the broader question of how the role and impact of AI in the decision-making process - by integrating both theoretical and empirical analyses - and shape human - AI collaboration. Despite extensive research on AI and its impact, significant gaps remain regarding its influence on organizational decision-making and the ways in which it collaborates with humans. In particular, there are conflicting perspectives on the adoption of AI and on human-AI collaboration in decision-making processes and task execution. To adress this gap, this thesis adopts a multi-study approach that progressively examines different dimensions of this phenomenon.