Andrew Pilny – författare

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 lifeExamines algorithmic management through a critical lens to offer a fresh perspective on emerging managerial practicesDiscusses overlooked issues such as bias in training data and the sociopolitical dimensions of data-centric AIIncludes hands-on thought experiments at the end of each chapter to stimulate discussion and critical thinkingExplores “Singularity Management” and the implications of AI on human roles, ethics, and empathy in organizationsOffers a five-part framework (the ”5 Es”) to guide ethical and strategic decision-making in AI-driven environmentsProvides a strategy that balances innovation and safety for regulating AI in the workplacePerfect 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.

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.

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.

This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples. Group processes are of central importance to many sectors of society, including government, the military, health care, and corporations. Computational methods are better suited to handle (potentially huge) group process data than traditional methodologies because of their more flexible assumptions and capability to handle real-time trace data.Indeed, the use of methods under the name of computational social science have exploded over the years. However, attention has been focused on original research rather than pedagogy, leaving those interested in obtaining computational skills lacking a much needed resource. Although the methods here can be applied to wider areas of social science, they are specifically tailored to group process research.A number of data-driven methodsadapted to group process research are demonstrated in this current volume. These include text mining, relational event modeling, social simulation, machine learning, social sequence analysis, and response surface analysis. In order to take advantage of these new opportunities, this book provides clear examples (e.g., providing code) of group processes in various contexts, setting guidelines and best practices for future work to build upon.This volume will be of great benefit to those willing to learn computational methods. These include academics like graduate students and faculty, multidisciplinary professionals and researchers working on organization and management science, and consultants for various types of organizations and groups.

This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples. Group processes are of central importance to many sectors of society, including government, the military, health care, and corporations. Computational methods are better suited to handle (potentially huge) group process data than traditional methodologies because of their more flexible assumptions and capability to handle real-time trace data.Indeed, the use of methods under the name of computational social science have exploded over the years. However, attention has been focused on original research rather than pedagogy, leaving those interested in obtaining computational skills lacking a much needed resource. Although the methods here can be applied to wider areas of social science, they are specifically tailored to group process research.A number of data-driven methodsadapted to group process research are demonstrated in this current volume. These include text mining, relational event modeling, social simulation, machine learning, social sequence analysis, and response surface analysis. In order to take advantage of these new opportunities, this book provides clear examples (e.g., providing code) of group processes in various contexts, setting guidelines and best practices for future work to build upon.This volume will be of great benefit to those willing to learn computational methods. These include academics like graduate students and faculty, multidisciplinary professionals and researchers working on organization and management science, and consultants for various types of organizations and groups.