Working with AI: Real Stories of Human-Machine Collaboration (Management on the Cutting Edge)

  • 8h 50m 47s
  • Steven M. Miller, Thomas H. Davenport
  • G&D Media
  • 2022

Two management and technology experts show that AI is not a job destroyer, exploring worker-AI collaboration in real-world work settings.

This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers.

These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems.

About the Author

Thomas H. Davenport is Distinguished Professor of Information Technology and Management at Babson College, Visiting Professor at Oxford’s Saïd Business School, Fellow of the MIT Initiative on the Digital Economy, and Senior Advisor to Deloitte’s AI practice. He is the author of The AI Advantage (MIT Press) and coauthor of Only Humans Need Apply and other books.

Steven M. Miller is Professor Emeritus of Information Systems at Singapore Management University, where he previously served as Founding Dean of the School of Computing and Information System Vice Provost for Research. He is coauthor of Robotics Applications and Social Implications.

In this Audiobook

  • Introduction
  • Chapter 1 - Morgan Stanley: Financial Advisors and the Next Best Action System
  • Chapter 2 - ChowNow: Growth Operations and RingDNA
  • Chapter 3 - Stitch Fix: AI-Assisted Clothing Stylists
  • Chapter 4 - Arkansas State University: Fundraising with Gravyty
  • Chapter 5 - Shopee: The Product Manager's Role in AI-Driven E-Commerce
  • Chapter 6 - Haven Life and MassMutual: The Digital Life Underwriter
  • Chapter 7 - Radius Financial Group: Intelligent Mortgage Processing
  • Chapter 8 - DBS Bank: AI-Driven Transaction Surveillance
  • Chapter 9 - Medical Diagnosis and Treatment Record Coding with AI
  • Chapter 10 - Dentsu: RPA for Citizen Automation Developers
  • Chapter 11 - 84.51° and Kroger: AutoML to Improve Data Science Productivity
  • Chapter 12 - Mandiant: AI Support for Cyberthreat Attribution
  • Chapter 13 - DBS Digibank India: Customer Science for Customer Service
  • Chapter 14 - Intuit: AI-Assisted Writing with
  • Chapter 15 - Lilt: The Computer-Assisted Translator
  • Chapter 16 - Salesforce: Architects of Ethical AI Practices
  • Chapter 17 - The Dermatologist: AI-Assisted Skin Imaging
  • Chapter 18 - Good Doctor Technology: Intelligent Telemedicine in Southeast Asia
  • Chapter 19 - Osler Works: The Transformation of Legal Services Delivery
  • Chapter 20 - PBC Linear: AI-Enabled Virtual Reality for Employee Training
  • Chapter 21 - Seagate: Improving Automated Visual Inspection of Wafers and Fab Tooling with AI
  • Chapter 22 - Stanford Health Care: Robotic Pharmacy Operations
  • Chapter 23 - Fast Food Hamburger Outlets: Flippy—Robotic Assistants for Fast Food Preparation
  • Chapter 24 - FarmWise: Digital Weeders for Robotic Weeding of Farm Fields
  • Chapter 25 - Wilmington, North Carolina, Police Department: AI-Driven Policing
  • Chapter 26 - Certis: AI Support for the Multifaceted Security Guard at Jewel Changi Airport
  • Chapter 27 - Southern California Edison: Machine Learning Safety Data Analytics for Front-Line Accident Prevention
  • Chapter 28 - Massachusetts Bay Transportation Authority: AI-Assisted Diesel Oil Analysis for Train Maintenance
  • Chapter 29 - Singapore Land Transport Authority: Rail Network Management in a Smart City
  • Chapter 30 - It Takes a Village to Change a Job with AI
  • Chapter 31 - Everybody's a Techie-Or at Least Has a Hybrid Job Role
  • Chapter 32 - The Platforms That Make AI Work
  • Chapter 33 - Intelligent Case Management Systems
  • Chapter 34 - Opportunities for Entry-Level Workers: Diminishing or Not?
  • Chapter 35 - Remote and Independent Work
  • Chapter 36 - What Machines Can't Do (Yet)
  • Chapter 37 - Looking Ahead to the Future of Work with Smart Machines