AI in Business: Learning AI and Machine Learning Through Real Enterprise Cases

Professor Christopher B. Califf from Western Washington University developed the free open-access online textbook AI in Business. Using real enterprise cases like EveryCure, Netflix, and Spotify, it teaches core machine learning concepts through the "AI Factory" framework, helping business students without programming backgrounds understand AI's business applications. The textbook has zero barriers to entry (no need to purchase or install software—only links are required) and uses tools like Claude and Google Colab to support hands-on practice.

Traditional AI textbooks often start with algorithms and mathematical formulas, which are too high a barrier for business students. How to effectively teach AI and machine learning concepts to business students without technical backgrounds has long been a difficult problem in education. Professor Califf's textbook is designed to address this challenge.

1. Case-Driven: Each chapter starts with a real enterprise problem to introduce core concepts;
2. AI Factory Framework: A cyclic model (Data → Model → Prediction → Decision → Value → Data) that runs through all cases, helping to understand the essence of AI systems;
3. Chapter Structure: Business case study → Key concept explanation → Hands-on experiments with Claude + Colab → Framework mapping and discussion;
4. Toolchain: Claude (AI tutor), Google Colab (browser-based Python environment), GitHub Pages (showcase of results).

The textbook has published 8 chapters covering cross-industry cases:

• EveryCure: Knowledge graph for discovering drug treatment plans
• Netflix: A/B testing to optimize content display
• Spotify: Personalized music recommendations
• Uber: Algorithms as a business model
• Waymo: Deep learning and autonomous driving
• Airbnb: Dynamic pricing and supply-demand matching
• Epic Systems: Innovation in healthcare systems
• AI and Environment: AI training energy consumption and disclosure gaps Each case is mapped to the AI Factory framework.

Effectiveness is reflected in:

1. Contextualized Learning: Concretizes abstract concepts through cases like Waymo;
2. Cross-Industry Perspective: Covers healthcare, entertainment, transportation, etc., fostering lateral thinking;
3. Ethical Awareness: A dedicated chapter discusses AI's environmental costs, strengthening sense of responsibility;
4. Practice-Oriented: Hands-on experiments promote skill internalization. The textbook is free for educators to use, adapt, and distribute directly.

Target Audience:

• Senior business undergraduates without programming experience
• MBA students wanting to understand AI's business applications
• Working learners and educators Access Links:
• Online Website: https://prof-califf.github.io/ai-in-business
• GitHub Repository: https://github.com/prof-califf/ai-in-business

AI in Business represents a new paradigm in AI education: using real cases as carriers, the AI Factory framework as the main line, and hands-on practice as the method. While lowering the learning threshold, it helps students build AI business decision-making thinking, which is a core competency for future business leaders. This case-driven, practice-oriented approach will become an important way to cultivate business talents in the AI era.