Thesis title: Intelligenza artificiale, competitività e strategia aziendale
The thesis explores the impact of Artificial Intelligence (AI) on business management through four interdependent analytical trajectories. The first chapter traces the technological development from its symbolic origins to deep neural networks, outlining methodological implications, repercussions on decision-making flows, legal profiles, and sustainability guidelines. The second chapter investigates how predictive algorithms, production automation, and dynamic pricing fuel competitive advantage, offering economic evaluation models calibrated to risk and return on investment. The third chapter shifts the focus to the internal structure: data-driven planning, hybrid roles, leadership that coordinates experimental culture and ethical governance, and inter-company alliances based on the sharing of qualified datasets. The fourth chapter converts the strategic vision into measurement: emerging indicators (Algorithmic Learning Velocity, Carbon Compute Intensity, Quantum of Bias Mitigated), predictive analysis techniques for rolling forecasts, and advanced reporting systems that combine historical accounting, probabilistic scenarios, and ESG parameters. The thesis suggests that AI is not merely an operational lever but a comprehensive paradigm that redefines control procedures, performance metrics, and, overall, market configurations. This results in guidelines for scholars, policymakers, academics, and managers who want to integrate algorithms, responsibility, and the creation of lasting value.