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.
Author(s): Thomas H. Davenport, Steven M. Miller
Series: Management on the Cutting Edge
Publisher: The MIT Press
Year: 2022
Language: English
Pages: 311
City: Cambridge
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
