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- News (194)
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- Multimedia (6)
- Faculty Publications (423)
Show Results For
-
All HBS Web
(981)
- People (1)
- News (194)
- Research (533)
- Events (8)
- Multimedia (6)
- Faculty Publications (423)
- 2017
- Working Paper
The Need for Speed: Effects of Uncertainty Reduction in Patenting
By: Mike Horia Teodorescu
Patents are essential in commerce to establish property rights for ideas and to give equal protection to firms that develop new technologies. Young firms especially depend on the protection of intellectual property to bring a product from concept to market. However,...
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- 2022
- Book
The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI
By: Paul Leonardi and Tsedal Neeley
The pressure to "be digital" has never been greater, but you can meet the challenge.
The digital revolution is here, changing how work gets done, how industries are structured, and how people from all walks of life work, behave, and relate to each other. To thrive...
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Keywords:
Digital;
Artificial Intelligence;
Big Data;
Digital Transformation;
Technological Innovation;
Transformation;
Learning;
Competency and Skills
Leonardi, Paul, and Tsedal Neeley. The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI. Boston, MA: Harvard Business Review Press, 2022.
- May 2022
- Supplement
Borusan CAT: Monetizing Prediction in the Age of AI (B)
By: Navid Mojir and Gamze Yucaoglu
Borusan Cat is an international distributor of Caterpillar heavy machines. In 2021, it had been three years since Ozgur Gunaydin (CEO) and Esra Durgun (Director of Strategy, Digitization, and Innovation) started working on Muneccim, the company’s predictive AI tool....
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Keywords:
AI and Machine Learning;
Commercialization;
Technology Adoption;
Industrial Products Industry;
Turkey;
Middle East
Mojir, Navid, and Gamze Yucaoglu. "Borusan CAT: Monetizing Prediction in the Age of AI (B)." Harvard Business School Supplement 522-045, May 2022.
- 2020
- Book
Work, Mate, Marry, Love: How Machines Shape Our Human Destiny
By: Debora L. Spar
Covering a time frame that ranges from 8000 BC to the present, and drawing upon both Marxist and feminist theories, the book argues that nearly all the decisions we make in our most intimate lives—whom to marry, how to have children, how to have sex, how to think about...
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Keywords:
Innovation;
Family;
Women;
Reproduction;
Artificial Intelligence;
Robots;
Gender;
Demography;
History;
Innovation and Invention;
Relationships;
Society;
Information Technology;
AI and Machine Learning;
Biotechnology Industry;
Computer Industry;
Health Industry;
Information Technology Industry;
Manufacturing Industry;
Technology Industry;
Africa;
Asia;
Europe;
Latin America;
North and Central America
Spar, Debora L. Work, Mate, Marry, Love: How Machines Shape Our Human Destiny. New York: Farrar, Straus and Giroux, 2020.
- 13 Nov 2019
- Research & Ideas
Don't Turn Your Marketing Function Over to AI Just Yet
Imagine a future in which a smart marketing machine can predict the needs and habits of individual consumers and the dynamics of competitors across industries and markets. This device would collect data to answer strategic questions, guide managerial decisions, and...
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Keywords:
by Kristen Senz
- 11 Apr 2023
- Research & Ideas
Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide
industry lines as companies increasingly bring seemingly unrelated business lines together in unconventional ways. New research by Awada, Harvard Business School Professor Suraj Srinivasan, and doctoral student Paul J. Hamilton harnesses...
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- 2021
- Chapter
Towards a Unified Framework for Fair and Stable Graph Representation Learning
By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual...
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Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
- December 2023
- Case
Monsters in the Machine? Tackling the Challenge of Responsible AI
By: Paul M. Healy and Debora L. Spar
In November of 2022, the small tech company OpenAI released ChatGPT, an artificial intelligence chatbot which quickly captured the public’s imagination—becoming the world’s fastest-growing consumer application within months of its release. Though observers from across...
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Keywords:
Technological Innovation;
AI and Machine Learning;
Ethics;
Governing Rules, Regulations, and Reforms;
Technology Adoption;
Corporate Social Responsibility and Impact;
Technology Industry;
United States;
European Union;
China
Healy, Paul M., and Debora L. Spar. "Monsters in the Machine? Tackling the Challenge of Responsible AI." Harvard Business School Case 324-062, December 2023.
- 07 Mar 2023
- HBS Case
ChatGPT: Did Big Tech Set Up the World for an AI Bias Disaster?
company, Google CEO Sundar Pichai issued an apology. Learning from Google’s mistakes The takeaways from Gebru’s story are hardly singular to Google as Big Tech scrambles to build ever-larger AI data sets...
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- September 2020 (Revised March 2022)
- Case
JOANN: Joannalytics Inventory Allocation Tool
By: Kris Ferreira and Srikanth Jagabathula
Michael Joyce, Vice President of Inventory Management at JOANN, championed an effort to develop and implement an inventory allocation analytics tool that used advanced analytics to predict in-season demand of seasonal items for each of JOANN’s nearly 900 stores and...
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Keywords:
Analytics;
Machine Learning;
Optimization;
Inventory Management;
Mathematical Methods;
Decision Making;
Operations;
Supply Chain Management;
Resource Allocation;
Distribution;
Technology Adoption;
Applications and Software;
Change Management;
Fashion Industry;
Consumer Products Industry;
Retail Industry;
United States;
Ohio
Ferreira, Kris, and Srikanth Jagabathula. "JOANN: Joannalytics Inventory Allocation Tool." Harvard Business School Case 621-055, September 2020. (Revised March 2022.)
- January 2018 (Revised February 2023)
- Teaching Note
The Future of Patent Examination at the USPTO
This teaching note pairs with the case entitled: “The Future of Patent Examination at the USPTO” (case no. 617-027).
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- Web
Online AI Course | HBS Online
culture. Recognize how AI is transforming business across industries and geographies and why it’s vital to accelerate your AI journey now....
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- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging...
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Keywords:
Influencer Advertising;
Video Advertising;
Computer Vision;
Machine Learning;
Advertising;
Online Technology
Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
- Article
Fake AI People Won't Fix Online Dating
Computer-generated images may inspire even more distrust and surely won’t lead to the love of a lifetime.
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Keywords:
Artificial Intelligence;
Dating Services;
Internet and the Web;
Ethics;
AI and Machine Learning
Kominers, Scott Duke. "Fake AI People Won't Fix Online Dating." Bloomberg Opinion (January 16, 2020).
- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models...
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Keywords:
Retention/churn;
Proactive Churn Management;
Field Experiments;
Heterogeneous Treatment Effect;
Machine Learning;
Customer Relationship Management;
Risk Management
Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
- 09 Jan 2020
- Book
Rethinking Business Strategy in the Age of AI
challenges that these digital businesses have to deal with to provide an effective, unbiased service that protects the rights of consumers. And we can learn a lot from this new generation of digital firms as...
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Keywords:
by Dina Gerdeman
- August 2022 (Revised January 2023)
- Case
Icario Health: AI to Drive Health Engagement
By: David C. Edelman
Icario Health has built a market-leading artificial intelligence (AI) engine to help health insurers drive better health behaviors for their members, enabling the insurers to improve their Medicare performance.
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Keywords:
Marketing;
Health Care and Treatment;
AI and Machine Learning;
Health Industry;
United States
Edelman, David C. "Icario Health: AI to Drive Health Engagement." Harvard Business School Case 523-025, August 2022. (Revised January 2023.)
- May 2020
- Case
Numenta in 2020: The Future of AI
By: David B. Yoffie, Cameron Armstrong, Mei Tao and Marta Zwierz
In 2020, Numenta’s co-founder, Jeff Hawkins, completed his pathbreaking research on artificial intelligence. His co-founder and CEO, Donna Dubinsky, had to find a business model to monetize the technology. This case explores the challenges of building a business...
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Keywords:
Artificial Intelligence;
Monetization;
Information Technology;
Strategy;
Intellectual Property;
Business Model;
AI and Machine Learning;
Technology Industry
Yoffie, David B., Cameron Armstrong, Mei Tao, and Marta Zwierz. "Numenta in 2020: The Future of AI." Harvard Business School Case 720-463, May 2020.
- April 2022
- Article
AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care
By: Ariel Dora Stern, Avi Goldfarb, Timo Minssen and W. Nicholson Price II
Despite enthusiasm about the potential to apply artificial intelligence (AI) to medicine and health care delivery, adoption remains tepid, even for the most compelling technologies. In this article, the authors focus on one set of challenges to AI adoption: those...
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Keywords:
Artificial Intelligence;
Medicine;
Health Care and Treatment;
Legal Liability;
Insurance;
Technology Adoption;
AI and Machine Learning
Stern, Ariel Dora, Avi Goldfarb, Timo Minssen, and W. Nicholson Price II. "AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care." NEJM Catalyst Innovations in Care Delivery 3, no. 4 (April 2022).