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Show Results For
- All HBS Web
(1,272)
- People (1)
- News (232)
- Research (667)
- Events (17)
- Multimedia (8)
- Faculty Publications (560)
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- Research Summary
Making Machine Learning Robust to Adversarial Attacks
The goal of this research is to ensure that machine learning models that we build and deploy are not easily susceptible to attacks by adversarial or malicious entities. View Details
- 02 Aug 2017
- Working Paper Summaries
Machine Learning Methods for Strategy Research
Keywords: by Mike Horia Teodorescu
- January 2023 (Revised June 2023)
- Case
Replika: Embodying AI
By: Shikhar Ghosh, Shweta Bagai and Marilyn Morgan Westner
Replika was a virtual AI companion that provided a way for people to process their emotions, build connections in a safe environment, and get through periods of loneliness. The chatbot fulfilled a user's need for a friend, romantic partner, or purely an emotional... View Details
Ghosh, Shikhar, Shweta Bagai, and Marilyn Morgan Westner. "Replika: Embodying AI." Harvard Business School Case 823-090, January 2023. (Revised June 2023.)
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
- May 2024
- Supplement
HubSpot and Motion AI (B): Generative AI Opportunities
By: Jill Avery
The technologies driving artificial intelligence (AI) had progressed significantly since HubSpot’s acquisition of Motion AI in 2017. Generative AI was the newest major development. Software-as-a-service (SaaS) companies such as HubSpot were analyzing how generative AI... View Details
Keywords: Artificial Intelligence; CRM; Chatbots; Sales Management; Generative Ai; SaaS; Marketing; Sales; AI and Machine Learning; Customer Relationship Management; Applications and Software; Technological Innovation; Competitive Advantage; Technology Industry; United States
Avery, Jill. "HubSpot and Motion AI (B): Generative AI Opportunities." Harvard Business School Supplement 524-088, May 2024.
- January 2019 (Revised October 2019)
- Case
Liulishuo: AI English Teacher
By: John J-H Kim and Shu Lin
Educators and entrepreneurs alike are excited about the potential for artificial intelligence (AI) and machine learning to change the way learning will look like in the future. There is a confluence of factors such as the availability of large sources of rich,... View Details
Keywords: AI; Artificial Intelligence; Education Technology; Information Technology; Education; Entrepreneurship; AI and Machine Learning; Education Industry; China
Kim, John J-H, and Shu Lin. "Liulishuo: AI English Teacher." Harvard Business School Case 319-090, January 2019. (Revised October 2019.)
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- April 2025
- Background Note
Climate Change Adaptation with Artificial Intelligence and Machine Learning
By: Michael W. Toffel and Nabig Chaudhry
Artificial Intelligence (AI) and machine learning (ML) have emerged as powerful tools to address climate change. This note summarizes a wide range of the uses of AI/ML to drive climate change adaptation and resilience, the measures organizations and governments are... View Details
- November 2023
- Case
Open Source Machine Learning at Google
Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
- 25 Oct 2017
- Research & Ideas
Will Machine Learning Make You a Better Manager?
buy, how we talk, and even how we feel—and use that to make predictions about how we’ll act next. As the field of machine learning (ML) has become increasingly mainstream, says... View Details
- November 2023 (Revised June 2024)
- Case
Zest AI: Machine Learning and Credit Access
By: David S. Scharfstein and Ryan Gilland
Scharfstein, David S., and Ryan Gilland. "Zest AI: Machine Learning and Credit Access." Harvard Business School Case 224-033, November 2023. (Revised June 2024.)
- December 2023 (Revised November 2024)
- Case
Generative AI and the Future of Work
By: Christopher Stanton, Matt Higgins, Shira Aronson and Meg Shriber
Generative AI seemed poised to reshape the world of work, including the higher-wage, white-collar jobs typically pursued by MBA graduates. Informed by the latest research, this case explores generative AI's potential impacts on work, productivity, value creation, and... View Details
Keywords: AI; Future Of Work; Labor Market; AI and Machine Learning; Labor; Value Creation; Performance Productivity; Technology Industry; United States
Stanton, Christopher, Matt Higgins, Shira Aronson, and Meg Shriber. "Generative AI and the Future of Work." Harvard Business School Case 824-130, December 2023. (Revised November 2024.)
- Working Paper
Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application
By: Flora Feng, Charis Li and Shunyuan Zhang
Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years featured by unique offerings from individual providers. Despite the perceived value of uniqueness, scalable quantification of visual uniqueness in P2P platforms like Airbnb has been largely... View Details
Keywords: Peer-to-peer Markets; Marketplace Matching; AI and Machine Learning; Demand and Consumers; Digital Platforms; Marketing
Feng, Flora, Charis Li, and Shunyuan Zhang. "Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application." SSRN Working Paper Series, No. 4665286, February 2024.
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers; AI and Machine Learning; Applications and Software; Analytics and Data Science; Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- 04 Oct 2019
- Working Paper Summaries
Soul and Machine (Learning)
- 2018
- Working Paper
Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning
By: Xiaojia Guo, Yael Grushka-Cockayne and Bert De Reyck
Problem definition: In collaboration with Heathrow Airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces... View Details
Keywords: Quantile Forecasts; Regression Tree; Copula; Passenger Flow Management; Data-driven Operations; Forecasting and Prediction; Data and Data Sets
Guo, Xiaojia, Yael Grushka-Cockayne, and Bert De Reyck. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Harvard Business School Working Paper, No. 19-040, October 2018.
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
Keywords: Economics Of AI; Machine Learning; Non-stationarity; Perishability; Value Depreciation; Analytics and Data Science; Value
Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. (Revised November 2021.)
- February 2022 (Revised September 2022)
- Case
InstaDeep: AI Innovation Born in Africa (A)
By: Shikhar Ghosh and Esel Çekin
Karim Beguir and Zohra Slim were the co-founders of InstaDeep, a deep tech startup focusing on artificial intelligence (AI) solutions. Instadeep was one of the few companies globally that were partnering with DeepMind, an AI subsidiary of Google [Alphabet Inc.].... View Details
Keywords: AI; Artificial Intelligence; Entrepreneurship; Operations; Business Subsidiaries; Brands and Branding; Innovation and Invention; Growth and Development Strategy; AI and Machine Learning; Technology Industry; Africa
Ghosh, Shikhar, and Esel Çekin. "InstaDeep: AI Innovation Born in Africa (A)." Harvard Business School Case 822-104, February 2022. (Revised September 2022.)
- Article
Productivity and Selection of Human Capital with Machine Learning
By: Aaron Chalfin, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig and Sendhil Mullainathan
Keywords: Analytics and Data Science; Selection and Staffing; Performance Productivity; Mathematical Methods; Policy
Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 124–127.
- 2025
- Working Paper
Generative AI and the Nature of Work
By: Manuel Hoffmann, Sam Boysel, Frank Nagle, Sida Peng and Kevin Xu
Recent advances in artificial intelligence (AI) technology demonstrate a considerable potential
to complement human capital intensive activities. While an emerging literature documents wide-ranging
productivity effects of AI, relatively little attention has been paid... View Details
Keywords: Generative Ai; Digital Work; Open Source Software; Knowledge Economy; AI and Machine Learning; Open Source Distribution; Organizational Structure; Performance Productivity; Labor
Hoffmann, Manuel, Sam Boysel, Frank Nagle, Sida Peng, and Kevin Xu. "Generative AI and the Nature of Work." Harvard Business School Working Paper, No. 25-021, October 2024. (Revised April 2025.)