AI for Industry: fundamentals of artificial intelligence for decision makers

The program is structured into thematic chapters that comprehensively cover theoretical, applicative, and strategic aspects:

1. Introduction and context. It starts from the evolution of industrial revolutions to digital transformation, revisiting the concepts of Industry 4.0 and 5.0 and the growing role of AI as a strategic lever.
2. Fundamental principles of Artificial Intelligence. Definition of AI, Machine Learning, Deep Learning, Generative AI, and Large Language Models, with simple explanations of key techniques such as regression, classification, and neural networks.
3. Supporting theories and production models. References to concepts like Comparative Advantage Theory, Fordism, World Class Manufacturing, and Lean Manufacturing, to understand how AI integrates into existing production paradigms.
4. Prerequisites and infrastructures. Focus on technical and organizational requirements: data quality, collection and management pipelines, technological infrastructures, skills, and key roles within the team.
5. Use cases and industrial applications. Practical examples of how AI is applied to optimize processes such as quality control, predictive maintenance, planning, and automation, with real success stories.
6. Challenges, risks, and ethics. Analysis of technical, organizational, and ethical limits, barriers to adoption, misuse, and key regulatory references such as GDPR, AI Act, and international provisions.
7. Recommendations and roadmap. Suggestions for responsible AI implementation in the company, with guidance on how to evaluate costs and benefits, start pilot projects, and promote a corporate culture open to innovation.