Working with AI: Real Stories of Human-Machine Collaboration
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.
Contents Series Foreword Introduction In Praise of Augmentation Our Contribution: Documentation of Current Practice About Our Research Approach How This Book Is Organized I Case Studies Morgan Stanley: Financial Advisors and the Next Best Action System What Effective Financial Advisors Do How the Franklin Avenue Group Uses the NBA System How the NBA System Is Changing the FA’s Role Lessons We Learned from This Case ChowNow: Growth Operations and RingDNA RingDNA and the Application of AI to Sales Applying RingDNA at ChowNow Lessons We Learned from This Case Stitch Fix: AI-Assisted Clothing Stylists Tatsiana Maskalevich, Director of Data Science A Styling Supervisor Lessons We Learned from This Case Arkansas State University: Fundraising with Gravyty Developer Meets Gravyty How the System Helps Human Fundraisers Won’t Vanish Lessons We Learned from This Case Shopee: The Product Manager’s Role in AI-Driven E-Commerce A Product Manager for Data and AI-Related Products and Services A Data Science Product Manager The Future of the Product Manager’s Role at Shopee Lessons We Learned from This Case Haven Life and MassMutual: The Digital Life Underwriter Digital Underwriter at Haven Life The New Digital, AI-Based Process The Future of Human Life Underwriters Lessons We Learned from This Case Radius Financial Group: Intelligent Mortgage Processing AI and Automation at radius The Mortgage Quarterback Lessons We Learned from This Case DBS Bank: AI-Driven Transaction Surveillance The Limitations of Rule-Based Systems for Surveillance Monitoring Using the New Generation of AI Capabilities to Enhance Surveillance Impact on the Analyst The Next Phase of Transaction Surveillance Lessons We Learned from This Case Medical Diagnosis and Treatment Record Coding with AI More Codes, More Complexity AI-Assisted Coding Educating Coders Lessons We Learned from This Case Dentsu: RPA for Citizen Automation Developers Two Nontechnical Employees Who Embraced Citizen Development The Future of Citizen Developers Lessons We Learned from This Case 84.51° and Kroger: AutoML to Improve Data Science Productivity 84.51° Projects and Automated Machine Learning Two Data Scientists and Their Reaction to AutoML Working with Insights Specialists The Future of Data Scientists Lessons We Learned from This Case Mandiant: AI Support for Cyberthreat Attribution An Intelligent Tool for Similarity Comparisons of Uncategorized Cyberthreat Groups Domain Experts and Data Scientists Team Up to Create the New ML Tool ATOMICITY Supports Both ML and Human Learning Expanding Use Cases for ATOMICITY Reflections on Big Changes Lessons We Learned from This Case DBS Digibank India: Customer Science for Customer Service The Genesis of the DBS Customer Science Program Changing the Bank through Customer Science The Future of Customer Operations and Customer Science at Digibank India Lessons We Learned from This Case Intuit: AI-Assisted Writing with Writer.com Writer—the AI Software, That Is—at Intuit The Impact on Content Creators Lessons We Learned from This Case Lilt: The Computer-Assisted Translator Partners in Translation The Lilt Translation Ecosystem Lessons We Learned from This Case Salesforce: Architects of Ethical AI Practices Architect, Ethical AI Practice Scaling Up the Impact of the Ethical AI Effort Lessons We Learned from This Case The Dermatologist: AI-Assisted Skin Imaging Miiskin—An Imaging Platform for Dermatology A Dermatologist and Miiskin Adoption of Miiskin Lessons We Learned from This Case Good Doctor Technology: Intelligent Telemedicine in Southeast Asia How the GDT Platform Works The Doctors behind Good Doctor Technology Impacts of Using the GDT Platform for Consultations Future Directions for GDT Lessons We Learned from This Case Osler Works: The Transformation of Legal Services Delivery Osler Works—Transactional The Role of Technology Changes to the Work and Osler Employees Lessons We Learned from This Case PBC Linear: AI-Enabled Virtual Reality for Employee Training Taqtile and the HoloLens Taqtile and Manifest at PBC Linear Benefits and Progress Thus Far Lessons We Learned from This Case Seagate: Improving Automated Visual Inspection of Wafers and Fab Tooling with AI AI for Focus Analytics Lessons We Learned from This Case Stanford Health Care: Robotic Pharmacy Operations How a Robotic Pharmacy Works New Roles for Human Pharmacists and Technicians Moving toward a Fully Autonomous Pharmacy Lessons We Learned from This Case Fast Food Hamburger Outlets: Flippy—Robotic Assistants for Fast Food Preparation Finding Flippy Flippy in Florida The Future of Flippy Lessons We Learned from This Case FarmWise: Digital Weeders for Robotic Weeding of Farm Fields Digital Weeding and FarmWise The Daily Work of a Digital Weeder The Future of the Digital Weeder Lessons We Learned from This Case Wilmington, North Carolina, Police Department: AI-Driven Policing Respond: AI-Based Gunshot Detection from ShotSpotter Connect: AI-Based Patrol Missions from ShotSpotter Management Support and Results Thus Far Lessons We Learned from This Case Certis: AI Support for the Multifaceted Security Guard at Jewel Changi Airport Certis, Jewel’s Partner for Security and Related Services A Digitally Transformed Approach to Delivering Security and Related Services The New World of Work for the Security Executive The Smart Operations Center as Mission Control Challenges for Security Specialists and Guest Service Ground Staff The Future for AI and Humans at Certis Lessons We Learned from This Case Southern California Edison: Machine Learning Safety Data Analytics for Front-Line Accident Prevention A Structure for Producing Analytical Change The Risk Model and Its Findings Deploying the Model and Needed Organizational Changes The Field Perspective Next Steps for the Safety Model Lessons We Learned from This Case Massachusetts Bay Transportation Authority: AI-Assisted Diesel Oil Analysis for Train Maintenance Diesel Locomotive Oil Institutionalizing Oil Analysis and Prediction Lessons We Learned from This Case Singapore Land Transport Authority: Rail Network Management in a Smart City The FASTER System The REAMS System Lessons We Learned from This Case II Insights It Takes a Village to Change a Job with AI Leaders and Sponsors Front-Line Supervisors Front-Line Workers IT and Data Science Professionals Cross-Functional Roles and Teams That Span the Enterprise External Vendors Customers and Partners The Village in Summary Everybody’s a Techie—Or at Least Has a Hybrid Job Role Job Roles That Are Business-IT Hybrids Technology for Digital Transformation Is Eating the World Preparing for Hybridized Business and IT Job Roles More of This Is Coming The Platforms That Make AI Work The Data Component of a Platform The Action Component of a Platform Types of Platforms Giving AI Support Platforms the Attention They Deserve Intelligent Case Management Systems The Ability for Human Override Issues Related to Using Intelligent Case Management Systems Opportunities for Entry-Level Workers: Diminishing or Not? Negative Situations Leading to Fewer Opportunities for Entry-Level Work Positive Situations Leading to More Opportunities for Entry-Level Work Dual Effects Situations (Both Negative and Positive) Enable Increases in Productivity and Output without Increases in Staffing, Leading to Business Expansion Positive Situations Where the System Enabled Job Role Expansion for Existing Employees Positive Situations Where the System Enabled Increased Job Access for Population Segments with Capability Disadvantages Problems Created by Diminishing Opportunities for Entry-Level Workers Ways to Enable New Graduates to Gain Relevant Work Experience Opportunities for Entry-Level Workers: Diminishing or Not? Remote and Independent Work The Upside and Downside of Independent Remote Work How to Make Remote Work Less Remote Making Independent and Remote Work More Productive What Machines Can’t Do (Yet) Understand Context Perform Tasks with Subjective Elements Prioritize Alerts in Complex, Dynamic Settings Make Final Decisions That Have Consequences Make Final Disease Diagnoses Create a Coherent Story for Other Humans Frame a Problem, Train, or Coach Coordinate Multistakeholder Alignment, Negotiation, and Decision-Making Understand Complex, Integrated Entities Build Relationships with Humans Provide Job Satisfaction and Nurture Morale Analyze Tone Understand Emotional Situations and Needs Consider the Ethical Implications of AI Exercise Discretion about When to Use AI Manage Organizational Change Orchestrate Physical Settings for Analysis Create New Knowledge and Transfer It to a System Fix AI Systems What Should Be Done about AI Limitations and Human Strengths? III Conclusions Looking Ahead to the Future of Work with Smart Machines Conclusion 1: Human Work Isn’t Going Away Conclusion 2: Things Are Moving Slowly and Expensively Conclusion 3: Be Prepared to Work with AI Conclusion 4: AI Augmentation Works Pretty Well Conclusion 5: More Automation Is Coming Conclusion 6: If the Singularity Comes, All Bets Are Off Notes Introduction Shopee: The Product Manager’s Role in AI-Driven E-Commerce 84.51° and Kroger Mandiant: AI Support for Cyberthreat Attribution Seagate Fast Food Hamburger Outlets: Flippy—Robotic Assistants for Fast Food Preparation Massachusetts Bay Transportation Authority: AI-Assisted Diesel Oil Analysis for Train Maintenance It Takes a Village to Change a Job with AI Everybody’s a Techie—Or at Least Has a Hybrid Job Role The Platforms That Make AI Work Intelligent Case Management Systems Opportunities for Entry-Level Workers: Diminishing or Not? Remote and Independent Work What Machines Can’t Do (Yet) Looking Ahead to the Future of Work with Smart Machines Index
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