Preface

In the spring of 2024, I sat in a conference room with the executive team of a Fortune 500 retailer. The CEO had just announced a $50 million "AI Transformation Initiative." The room buzzed with enthusiasm. Then the Chief Marketing Officer raised her hand: "This is going to sound like a ridiculous question, but... what exactly are we going to do with AI?"

It was not a ridiculous question. It was the only question that mattered.

Over the past decade, I have watched artificial intelligence evolve from a niche academic discipline into the most discussed technology in business history. I have also watched billions of dollars evaporate in poorly conceived AI projects — projects that failed not because the technology was inadequate, but because the people deploying it lacked the frameworks to connect AI capability to business value.

This textbook exists to close that gap.

Who This Book Is For

AI & Machine Learning for Business is written for MBA students and business professionals who need to understand AI deeply enough to lead it — without necessarily becoming data scientists themselves. If you can describe your company's competitive strategy, read a financial statement, and manage a cross-functional team, you have everything you need to begin.

This book is not a computer science textbook that occasionally mentions business. It is a business textbook that teaches the technology necessary to make informed strategic decisions about AI. The difference matters enormously.

You will learn to write Python code — enough to understand what your data science team is doing, to prototype ideas, and to evaluate AI solutions critically. You will not derive backpropagation equations or prove convergence theorems. Those topics are covered superbly in other textbooks written for other audiences.

The Athena Retail Group Story

Abstract concepts become concrete through narrative. Throughout this book, you will follow Athena Retail Group — a mid-size omnichannel retailer — through a multi-year AI transformation. You will watch the company stumble through data quality crises, celebrate its first successful pilot, navigate bias scandals, respond to data breaches, and emerge with a mature AI practice that delivers measurable business value.

Athena is fictional, but every challenge it faces is drawn from real organizations I have worked with. The specifics are changed; the patterns are authentic.

You will also follow two MBA students — NK Adeyemi and Tom Kowalski — as they study AI in the classroom and apply it at Athena. NK brings a marketer's intuition and a healthy skepticism toward technology hype. Tom brings deep technical fluency and a growing appreciation for the organizational complexity that technology alone cannot solve. Together, their perspectives mirror the range of backgrounds that MBA students bring to this subject.

Five Themes

Five themes recur across all forty chapters:

  1. The Hype-Reality Gap. AI can do remarkable things. It cannot do everything the marketing materials claim. Distinguishing genuine capability from inflated expectations is perhaps the most important skill a business leader can develop.

  2. Human-in-the-Loop. AI augments human judgment; it does not replace it. Every chapter explores where the boundary between human and machine decision-making should be drawn — and who gets to draw it.

  3. Data as a Strategic Asset. Models are only as good as the data that feeds them. Data quality, governance, and provenance are not unglamorous prerequisites — they are the foundation of every successful AI system.

  4. The Build-vs-Buy Decision. At every level of the AI stack — from infrastructure to models to applications — organizations must decide what to build internally and what to purchase. This decision is strategic, not merely technical.

  5. Responsible Innovation. Ethics is not a constraint on innovation. It is a component of sustainable innovation. Organizations that treat responsible AI as an afterthought will pay for that choice in lawsuits, regulatory penalties, reputational damage, and — most importantly — harm to the people their systems affect.

How This Book Is Organized

The book progresses from foundational concepts to advanced strategy:

  • Part 1 (Chapters 1–6) establishes foundations: the AI landscape, data literacy, Python basics, and the ML project lifecycle.
  • Part 2 (Chapters 7–12) covers core machine learning: classification, regression, clustering, recommendations, evaluation, and MLOps.
  • Part 3 (Chapters 13–18) introduces deep learning, NLP, computer vision, time series, and generative AI.
  • Part 4 (Chapters 19–24) focuses on practical AI tools: prompt engineering, RAG, AI agents, no-code platforms, and cloud services.
  • Part 5 (Chapters 25–30) addresses ethics, bias, governance, regulation, and privacy.
  • Part 6 (Chapters 31–36) covers AI strategy, team building, product management, ROI measurement, and change management.
  • Part 7 (Chapters 37–38) looks ahead to emerging technologies and the future of work.
  • Part 8 (Chapters 39–40) provides a capstone project and closing reflection.

Each chapter includes exercises, two case studies, a quiz, key takeaways, and annotated further reading. Chapters with Python content include complete, runnable code.

A Note on Pace

AI moves fast. Between the time I write this preface and the time you read it, new models will have been released, new regulations will have been proposed, and at least one company will have experienced a high-profile AI failure that generates a semester's worth of discussion material.

This book is designed to be durable despite that pace. The specific tools and platforms will evolve; the strategic frameworks, ethical principles, and organizational dynamics explored here will not. The goal is not to teach you today's AI — it is to teach you how to evaluate, deploy, and govern whatever AI looks like when you encounter it.

Acknowledgments

To the executives who let me observe their AI transformations up close, including the messy parts. To the MBA students whose questions were always better than my initial answers. To the data scientists who patiently explained what I had oversimplified, and the ethicists who reminded me what I had overlooked.

And to that CMO in the conference room: this book is my attempt at an answer.

March 2026