How to Do More with Less

An honest and practical strategy guide to working in a world transformed by AI In today’s workplace, headlines about artificial intelligence can feel overwhelming. With headlines swinging between promises of utopia and warnings of mass unemployment, for most knowledge workers, the truth feels unclear. In this book, Sharon Gai cuts through the noise. Drawing from real-world examples and global insights, she explains how AI is reshaping the way we work—without hype or fearmongering. Instead of choosing between blind optimism or outright pessimism, she offers a practical, balanced perspective that helps readers make sense of the rapidly evolving AI landscape. You’ll learn how to: Reskill and future-proof your career in the face of AI disruption Identify which parts of your role can be automated, and which require human creativity and judgment Use proven frameworks to evaluate AI’s impact on your work and your organization Apply actionable tips and tools to boost productivity, make smarter decisions, and do more with less Gain clarity as a parent, leader, or professional navigating what this means for the next generation Whether you’re an employee anxious about your future, a parent concerned about your children’s opportunities, or a leader managing a lean team with tight budgets, this book provides the strategies and mindset you need to adapt so you can stop worrying and start preparing.

Preface ix Introduction xi Part I The Great Disruption 1 Chapter 1 Will AI Replace Me? 3 Chapter 2 What Exactly Is AI? 11 Chapter 3 How Do I Get Started with AI? 43 Chapter 4 Redefining Creativity 57 Part II Integrating Us and AI 65 Chapter 5 Introducing AI Agents 67 Chapter 6 How AI Is Changing the Rules of the Web 89 Chapter 7 Competing with China 101 Chapter 8 Doing More with Less Using AI 117 Part III Looking Into the Future 139 Chapter 9 The Future of Commerce 141 Chapter 10 The Future of Education 149 Chapter 11 The Future of Work 157 Chapter 12 The Future of Relationships 187 Chapter 13 When AI Goes Wrong 195 Conclusion 229 Notes 231 Acknowledgments 235 About the Author 237 Index 241