Acknowledgments

This book was drafted with AI assistance and published by DataField.Dev. It draws on the published work of the business leaders, data scientists, engineers, ethicists, and regulators who have written openly about what AI projects require, including their failures alongside their successes. The characters and organizations in this book are fictional; the problems they face reflect patterns that are widely documented in the business and research literature.

No book on a subject that moves this fast is free of errors. If you are an instructor or reader and you find a technical mistake, an outdated regulatory detail, a code example that does not run, or an explanation that could be clearer, please send corrections and suggestions through datafield.dev. Those corrections are how this book gets better.

To the many instructors in business schools around the world who are wrestling with the same pedagogical challenge — how to teach AI to future business leaders — and who share their syllabi and teaching ideas publicly: your openness makes resources like this one possible.

To the open-source community — the tools used throughout this book (Python, pandas, scikit-learn, Jupyter, and many others) represent thousands of hours of unpaid labor by people who believed that knowledge tools should be accessible. This book stands on their shoulders.