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Generative AI for MBA Students

AI is already in the room. The question is whether you understand it well enough to lead with it.

This book is written for MBA students who want more than a surface-level introduction to Generative AI. Across six modules and 33 chapters, it builds the complete toolkit — from how large language models actually work, to building AI products, writing governance policies, auditing AI systems for bias, and using AI tools to ace your exams and land your first role.

What you will be able to do after reading this book:

  • Build a Product Requirements Document for an AI-powered application
  • Design a Go-to-Market strategy for an AI product in your chosen sector
  • Identify governance failures in real AI deployments — and write the policy that fixes them
  • Conduct a 10-question management-level AI audit without technical expertise
  • Apply Generative AI across Finance, Marketing, HR, and Business Analytics
  • Use ChatGPT and other AI tools to prepare for classes, revise for exams, and practice for job interviews

Who this book is for:

MBA students in their final year, working professionals entering AI-adjacent roles, and business leaders who need to make consequential decisions about AI systems — without a technical background.

What makes this book different:

Most AI books explain what AI is. This one teaches you what to do with it. Every chapter is built around applied frameworks, worked examples, and hands-on labs drawn from real business contexts across India, the UK, and global markets.

33 chapters. 6 modules. One competitive edge.

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About the Book

Generative AI is no longer a future skill for business leaders. It is a present requirement.

This book gives MBA students the complete toolkit to understand, apply, and govern AI across every major business function. Across 33 chapters and 6 modules, you will move from the fundamentals of how large language models work to building product requirements documents, designing go-to-market strategies, auditing AI systems for bias, writing governance policies with named owners, and preparing for AI-focused job interviews.

Every chapter is built for the MBA level — rigorous enough to be credible, practical enough to be immediately useful. The labs and frameworks in this book are the same ones used in professional AI governance consulting. They are now in your hands.

By the end of this book, you will not just understand Generative AI. You will know how to deploy it, govern it, and build a career around it.

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Contents

Table of Contents

TABLE OF CONTENTS MODULE 1 — Foundations of Generative AI
  • Chapter 1: What Is Generative AI? A Business Leader's Primer
  • Chapter 2: How LLMs Work: What Every MBA Must Know
  • Chapter 3: The GenAI Toolbox: Choosing the Right Tool
  • Chapter 4: Prompt Engineering: The Art and Science of Talking to AI
  • Chapter 5: Evaluating GenAI Output: Quality, Accuracy, and Reliability
  • Chapter 6: GenAI in the Real World: Industry Applications and Case Studies
MODULE 2 — GenAI Across Business Functions
  • Chapter 7: GenAI in Marketing: From Insights to Campaigns
  • Chapter 8: GenAI in Finance: Analysis, Modelling, and Risk
  • Chapter 9: GenAI in Human Resources: Hiring, Training, and Culture
  • Chapter 10: GenAI in Operations and Supply Chain
  • Chapter 11: GenAI for Strategy and Consulting
  • Chapter 12: GenAI in Business Analytics: From Data to Decision
MODULE 3 — Advanced GenAI Applications
  • Chapter 13: Building AI-Native Products: From Concept to Launch
  • Chapter 14: Agentic AI: When Models Take Action
  • Chapter 15: Retrieval-Augmented Generation (RAG) for Business
  • Chapter 16: Fine-Tuning and Customising Language Models
  • Chapter 17: Multimodal AI: Text, Image, Audio, and Beyond
  • Chapter 18: GenAI and the Future of Work
MODULE 4 — AI Strategy and Competitive Advantage
  • Chapter 19: Building an AI Strategy: A Framework for Business Leaders
  • Chapter 20: AI Business Models and Monetisation
  • Chapter 21: Competitive Advantage in the Age of GenAI
  • Chapter 22: AI Investment and ROI: Making the Business Case
  • Chapter 23: Scaling AI: From Pilot to Production
  • Chapter 24: The AI-Ready Organisation: Culture, Talent, and Structure
MODULE 5 — AI Product Management, Governance, and Ethics
  • Chapter 25: AI Product Management and GTM Strategy
  • Chapter 26: From Idea to Requirement: AI-Powered PM and GTM
  • Chapter 27: From Idea to PRD: A Guided AI-Prompting Workbook
  • Chapter 28: GTM Strategy, Technical Spec and Peer Review Lab
  • Chapter 29: GTM Strategy, Technical Spec and Peer Review
  • Chapter 30: Who Is Responsible When AI Gets It Wrong?
  • Chapter 31: Governance Gap Diagnosis Lab
  • Chapter 32: Can You Explain What This AI Did — And To Whom?
MODULE 6 — AI in Your MBA Student Life
  • Chapter 33: Your AI Study Partner: From Classes to Career

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