Foundations of AI in Business

This unit introduces major AI paradigms, their business applications, and core ethical and governance challenges relevant to business and finance. Students examine data privacy and cybersecurity concerns, hardware and quantum constraints, and sustainability implications for AI-enabled urban and transit systems to prepare for deeper technical and applied units.

Learning Objectives

• Analyze and differentiate major AI paradigms (logic/algorithmic, cognitive/necurrent/neuromorphic, emergentist, brain emulation, intelligence amplification, and embodied robotics) and categorize real-world business use cases for each.
• Evaluate ethical, governance, and control challenges of AI systems—including anthropomorphic design, accountability mechanisms, and strategies to mitigate harm and prevent AI oppression—in business contexts.
• Apply data-driven decision-making principles to identify and propose mitigation strategies for data privacy, cybersecurity, and quantum-related risks in AI deployments.
• Assess emerging hardware and sustainability constraints (post‑Moore computing trends, quantum noise, and computational sustainability) and analyze their implications for AI-enabled urban systems and business models, including public transit.