Overview

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.

Full Product Details

Author:   Andrew Pilny (University of Kentucky) ,  Camille Endacott (University of North Carolina Charlotte) ,  Jeffrey W. Treem (Northwestern University)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Blackwell
Dimensions:   Width: 14.70cm , Height: 1.80cm , Length: 22.40cm
Weight:   0.340kg
ISBN:  

9781394247561

ISBN 10:   1394247567
Pages:   208
Publication Date:   26 November 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

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

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Author Information

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.

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