Master of Science in Business Administration

1 Foundations of Generative AI and the AI Landscape:

• A brief history of AI: AI, Machine Learning, Deep Learning, Generative AI
• Introduction to Large Language Models (LLMs) and the transformer architecture
• How we got here: from early OpenAI announcements to today
• Key technological breakthroughs and macroeconomic impact of GenAI
• Gen Z and the digital-native workforce influence
• From experimentation to enterprise adoption
• Overview of the current AI landscape and key players (OpenAI, Microsoft, Google, Meta, Anthropic, Amazon, etc.)
• Evolution of foundational models and LLMs (GPT, Gemini, Claude, LLaMA, Phi, Turing, Bedrock)

Didactic goal: build a shared conceptual and historical foundation and frame GenAI as a systemic business transformation.

2 Models, Platforms, and the Evolving Tech Ecosystem:

• Comparing models: tools, benchmarks, and performance trade-offs
• Practical examples from the Italian and international context
• Implications for startups, scaleups, and early-stage investors
• Off-the-shelf vs. custom AI solutions: speed, scalability, and specificity
• Exploring OpenAI APIs and embedding AI into applications and workflows
• How Big Tech integrates AI: strategies from Microsoft, Google, Apple, Adobe
• Live demos

Didactic goal: critically evaluate platforms, models, and architectural choices.

3 Generative Techniques and Prompt Engineering for Business:

• Generative techniques for text, audio, image, and video with AI
• Prompting, context, and data: why quality matters
• The art and science of prompt engineering
• Crafting effective prompts: structure, tone, and context
• Optimizing prompts for business tasks (summarization, content creation, data analysis, market analysis)
• Interactive session: hands-on challenges and best practices in prompt engineering

 Didactic goal: provide hands-on operational skills to effectively interact with GenAI systems.

4 GenAI as a Business Capability and Consulting Tool:

• GenAI as a strategic business capability across decision-making and execution
• AI as Advisor: supporting data-driven and executive-level decisions
• AI as Analyst: automating analysis, research, and insight generation
• AI as Catalyst: enabling business model and service innovation
• AI as Augmentor: enhancing human judgment and professional expertise
• AI as Assistant: streamlining everyday tasks and operational decisions
• The role of Microsoft 365 Copilot across business productivity workflows (Word, Excel, PowerPoint, Outlook, Teams)
• Analyst and Researcher agents in consulting and knowledge-intensive workflows
• Integrating GenAI into consulting offerings, including AI Strategy, Risk & Ethics, and Change Management
• Measuring business value, productivity gains, and ROI from GenAI initiatives
• Industry case studies and real-world applications (e.g., Walmart, Coca-Cola, KPMG, CarMax)

Didactic goal: translate GenAI capabilities into concrete business and consulting value.

5 Building AI-Ready and Agentic Organizations:

• Integrating GenAI into enterprise systems and processes
• Role of AI in employee experience and internal operations
• The rise of AI assistants: transforming user experience across productivity, creativity, and devices
• The search engine revolution: Google AI Mode, Bing Copilot, Perplexity, ChatGPT Search
• Microsoft Copilot ecosystem and enterprise AI orchestration
• Redefining Business Process Management with Generative AI
• Understanding AI agents and agent architectures
• From single agents to multi-agent systems
• Talking to each other: open protocols for agent interoperability, Model Context Protocol (MCP) and Agent2Agent protocol (A2A)
• Live agent construction workshop and demos

Didactic goal: move from tools to organizational design and operating models.

6 Ethics, Governance, and the Future of Generative AI:

• Risks of Generative AI: bias, hallucinations, security, privacy
• Ethical challenges: bias, discrimination, algorithmic fairness
• Provenance and trust: C2PA and Content Credentials
• Navigating AI regulation: EU AI Act, sector-specific rules, global trends
• Multimodal and domain-specific GenAI
• Reasoning models and “Flash Thinking”
• Emerging innovations (image, video with sound, live translation, etc.)
• The evolving competitive landscape: Big Tech vs startups vs open source

Didactic goal: equip future leaders to govern GenAI responsibly and anticipate strategic evolution.