AI in the Workplace
Description
Offers a smart, witty guide to understanding AI's role in the future of work
With AI tools now integrated into everything from hiring systems and team collaboration platforms to strategic decision-making processes, there's a pressing need to move beyond either fear or hype. AI in the Workplace explores how artificial intelligence is reshaping organizational life, often in ways that are subtle yet deeply consequential. This book answers that need by providing an accessible, critical, and often humorous guide for understanding what AI is, how it works, and what it means for the ways we work and interact in professional settings.
AI in the Workplace demystifies AI through a blend of theory, storytelling, and practical insight. Readers are introduced to foundational AI concepts without overwhelming technical detail and are given frameworks to think through pressing questions of algorithmic management, workplace surveillance, bias, and ethics. Through contemporary case studies, real-world examples, reflective exercises, and actionable strategies, the book equips readers to think critically and act thoughtfully in the evolving AI landscape, whether that means embracing tools, resisting trends, or something in between.
Helping readers grasp how AI is not simply replacing human labor, but reorganizing work itself, AI in the Workplace:
- Introduces the novel Human-Story-Text-AI Network model to conceptualize AI's integration into organizational life
- Examines algorithmic management through a critical lens to offer a fresh perspective on emerging managerial practices
- Discusses overlooked issues such as bias in training data and the sociopolitical dimensions of data-centric AI
- Includes hands-on thought experiments at the end of each chapter to stimulate discussion and critical thinking
- Explores "Singularity Management" and the implications of AI on human roles, ethics, and empathy in organizations
- Offers a five-part framework (the "5 Es") to guide ethical and strategic decision-making in AI-driven environments
- Provides a strategy that balances innovation and safety for regulating AI in the workplace
Perfect for students and professionals alike, AI in the Workplace serves advanced undergraduate and graduate courses in Communication, Business, Technology Management, and related fields. It is also an invaluable resource for executive education and professional development in AI adoption, leadership, and organizational change.
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Persons
ANDREW PILNY, is an Associate Professor in the departments of Communication and Sociology at the University of Kentucky.
CAMILLE ENDACOTT, is an Assistant Professor in the Department of Communication Studies at the University of North Carolina, Charlotte.
JEFFREY W. TREEM, is the Theodore R. and Annie Laurie Sills Professor at the Northwestern University Medill School of Journalism, Media, Integrated Marketing Communications.
Content
Preface ix
About the Authors xi
Ai Use Disclosure Statement xiii
1 Organizing in the Age of AI 1
Metaphors of AI 2
A Brief History of Organizing as Communication Networks 4
A Preview of the Rest of This Book 19
2 AI and Algorithmic Management 21
A Definition: What Is (Not) AI? 23
How Does AI Learn? 29
Unpacking Algorithmic Management 33
A Sociomaterial Soup: Where Technology and Human Practices Stew Together 34
The Nature of Algorithms 36
On Coevolution 38
Coleman's Boat 39
Singularity Management 42
Thought Experiment: The Algorithmic Apprentice 45
3 How Do Organizations Even Use AI? 47
An Introduction to the Typology 48
Predictive AI 50
Perceptual AI 53
Generative AI 55
Decisional AI 58
Optimization AI 61
Organizational AI 63
Robotic AI 66
Broader Challenges of AI in the Workplace 69
4 Consequences of Algorithmic Management 73
AI Is Coming for Some Jobs 73
You Can't Argue with an Algorithm 76
Reskill, Upskill, for What Skill? 79
Somebody's Watching You 82
When AI Is Not So Intelligent 85
AI's Training Data Is Biased... and Racist and Sexist and Ageist and Xenophobic 89
We Can, but Should We? 94
Thought Experiment: The Great Malaise 95
The Great Question 96
5 AI Literacy and Large Language Models 97
Tokenization: How LLMs Gobble Up Text Data 97
Reinforcement Learning Through Human Feedback 110
How Are LLMs Evaluated? 112
Issues with Using LLMs in the Workplace 113
Can You Spot a Bot? 118
Thought Experiment: The Hallucinated Memo 121
6 Deciding Who Does What in an AI-Workplace 123
Finding Our Way in the Marketplace of AI Solutions 124
Explicitness 126
Evaluation 129
Experimentation 130
Engagement 132
Ethics 134
The Five Es, Revisited 137
Thought Exercise: Training HelpBot: The Perfect Hire, or a Perfect Disaster? 151
Some Important Questions to Think About 152
7 A Framework to Regulate, Survive, and Prosper with AI 153
Risk #1: Weaponization 153
Risk #2: Loss of Control 155
Risk #3: Reliability and Validity 155
Risk #4: Explainability and Interpretability 156
From Viagra to Male Enhancement: The Need for Regulating AI 157
Navigating AI: A Worker's Guide 169
Becoming an Expert Reverse Engineer 169
Decoding AI's Vision 170
How to Make a Peanut Butter and Jelly Sandwich 171
Waste Your Time 174
Resistance: When to Push Back Against the Algorithm 177
Conclusion 180
One Final Thought Experiment: The Elephants Who Paint Pictures 181
Index 183
