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Show Results For
- All HBS Web
(1,034)
- People (1)
- News (155)
- Research (659)
- Events (13)
- Multimedia (3)
- Faculty Publications (570)
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- July 2024
- Article
Chatbots and Mental Health: Insights into the Safety of Generative AI
By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp and Stefano Puntoni
Chatbots are now able to engage in sophisticated conversations with consumers. Due to the ‘black box’ nature of the algorithms, it is impossible to predict in advance how these conversations will unfold. Behavioral research provides little insight into potential safety... View Details
Keywords: Autonomy; Chatbots; New Technology; Brand Crises; Mental Health; Large Language Model; AI and Machine Learning; Behavior; Well-being; Technological Innovation; Ethics
De Freitas, Julian, Ahmet Kaan Uğuralp, Zeliha Uğuralp, and Stefano Puntoni. "Chatbots and Mental Health: Insights into the Safety of Generative AI." Journal of Consumer Psychology 34, no. 3 (July 2024): 481–491.
- September 2023
- Case
Ada: Cultivating Investors
By: Reza Satchu and Patrick Sanguineti
Mike Murchison, co-founder and CEO of Ada, has an enviable dilemma. Launched in 2016 by Murchison and his co-founder David Hariri, Ada is an AI-native company that aims to revolutionize how businesses approach customer service. The company has already attracted a buzz,... View Details
Keywords: Founder; Fundraising; Business Startups; Decisions; Entrepreneurship; Venture Capital; AI and Machine Learning; Technology Industry
Satchu, Reza, and Patrick Sanguineti. "Ada: Cultivating Investors." Harvard Business School Case 824-090, September 2023.
- June 2025
- Case
Scale AI Scales Up
By: Boris Groysberg and Sarah L. Abbott
Scale AI, the data labeling and AI infrastructure company, had grown rapidly since it was founded in 2016; however, as Scale’s generative AI business was taking off, Alexandr Wang, Scale’s founder and CEO, became concerned that Scale was slowing down. Wang and the... View Details
Keywords: Artificial Intelligence; Technology And Innovation Management; Start-ups; Entrepreneur; Managing Growth; Hiring; Generative Ai; Data Labeling; Scale; AI and Machine Learning; Entrepreneurship; Talent and Talent Management; Growth Management; Leadership; Culture; Technology Industry; United States
Groysberg, Boris, and Sarah L. Abbott. "Scale AI Scales Up." Harvard Business School Case 425-082, June 2025.
- March 2025
- Case
Mobvoi's Path Through Market Challenges and Business Reinvention
By: Paul A. Gompers and Shu Lin
Founded in 2012, Mobvoi evolved through multiple transformations—from AI-driven voice technology to smart wearables and later AI-generated content. Backed by major investors, the company navigated shifts in strategy while facing two failed IPO attempts. As market... View Details
Keywords: Business Startups; Entrepreneurship; AI and Machine Learning; Transformation; Initial Public Offering; Business Strategy; Technology Industry; China
Gompers, Paul A., and Shu Lin. "Mobvoi's Path Through Market Challenges and Business Reinvention." Harvard Business School Case 825-158, March 2025.
- July 2023
- Supplement
Honeycomb (B): Jumping on The Generative AI Bandwagon?
By: Jeffrey J. Bussgang and Kumba Sennaar
Honeycomb, an audio app enabling users to record stories and save family memories, considers pivoting to embrace generative AI. What should the co-founders business model look like if they pursued this new direction? View Details
- April 2025
- Case
Breezm: Innovative 3D-Printed Eyewear (A)
By: Juan Alcácer, Brian Mao Fu and Adina Wong
In 2023, Breezm, a South Korean startup, faced a strategic decision about how to grow its innovative 3D-printed, custom-fit eyewear business. Co-founded in 2017 by Zenma Park and Wooseok Sung, Breezm combined facial scanning, AI, and in-house production to solve the... View Details
Keywords: 3D Printing; Eyeyewear; Growth; Business Startups; AI and Machine Learning; Technological Innovation; Growth and Development Strategy; Risk and Uncertainty; Expansion; South Korea
Alcácer, Juan, Brian Mao Fu, and Adina Wong. "Breezm: Innovative 3D-Printed Eyewear (A)." Harvard Business School Case 725-376, April 2025.
- 2025
- Working Paper
Global Evidence on Gender Gaps and Generative AI
By: Nicholas G. Otis, Solène Delecourt, Katelynn Cranney and Rembrand Koning
Generative AI has the potential to transform productivity and reduce inequality, but only if adopted broadly. In this paper, we show that recently identified gender gaps in generative AI use are nearly universal. Synthesizing data from 18 studies covering more than... View Details
Otis, Nicholas G., Solène Delecourt, Katelynn Cranney, and Rembrand Koning. "Global Evidence on Gender Gaps and Generative AI." Harvard Business School Working Paper, No. 25-023, October 2024. (Revised January 2025.)
- November 2024 (Revised April 2025)
- Case
Cheerful Music
By: Shunyuan Zhang, Feng Zhu and Nancy Hua Dai
Established by Snow Jiang in 2019 in Shenzhen, China, Cheerful Music was a record label company that had created many hit songs in China. “Yi Xiao Jiang Hu,” its most famous hit song, gained billions of views on social media platforms in China and overseas as the... View Details
Keywords: Generative Ai; Music Entertainment; Global Strategy; Business Model; AI and Machine Learning; Market Entry and Exit; Music Industry; China; United Kingdom; London
Zhang, Shunyuan, Feng Zhu, and Nancy Hua Dai. "Cheerful Music." Harvard Business School Case 525-031, November 2024. (Revised April 2025.)
- October 2024 (Revised February 2025)
- Case
AI and Brand Management: Promises and Perils
By: Julian De Freitas and Elie Ofek
As AI gains traction across industries, companies anticipate that AI will revolutionize both backend processes and customer-facing interactions—with brands eager to leverage AI for tailored marketing materials and automated consumer engagements. Yet, despite a dramatic... View Details
Keywords: AI and Machine Learning; Brands and Branding; Reputation; Technology Adoption; Competitive Advantage
De Freitas, Julian, and Elie Ofek. "AI and Brand Management: Promises and Perils." Harvard Business School Case 525-021, October 2024. (Revised February 2025.)
- June 2024 (Revised September 2024)
- Case
Driving Scale with Otto
By: Rebecca Karp, David Allen and Annelena Lobb
This case asks how startup founders make scaling decisions in light of their priorities for their business and for themselves. Otto was a technology company that applied artificial intelligence technology to sales. It deployed natural language processing to find sales... View Details
Keywords: Artificial Intelligence; Natural Language Processing; B2B; B2B Innovation; Scaling; Scaling Tech Ventures; Business Startups; AI and Machine Learning; Finance; Sales; Business Strategy; Growth and Development Strategy; Entrepreneurship; Information Technology Industry; United States; Cambridge; New York (city, NY); Spain
Karp, Rebecca, David Allen, and Annelena Lobb. "Driving Scale with Otto." Harvard Business School Case 724-407, June 2024. (Revised September 2024.)
- June 2020
- Article
Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure
By: Omar Isaac Asensio, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer and Sooji Ha
By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to... View Details
Keywords: Environmental Sustainability; Transportation; Infrastructure; Behavior; AI and Machine Learning; Demand and Consumers
Asensio, Omar Isaac, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer, and Sooji Ha. "Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure." Nature Sustainability 3, no. 6 (June 2020): 463–471.
- 06 May 2015
- What Do You Think?
Are You Ready for Personalized Predictive Analytics?
Summing Up Personal Predictive Analytics: Should We Be Careful What We Wish For? The world of continuous monitoring of numerous sensors for machines and humans, limitless information storage capacity, and big data combined with rapid... View Details
Keywords: by James Heskett
- 2024
- Working Paper
The Value of AI Innovations
By: Wilbur Xinyuan Chen, Terrence Tianshuo Shi and Suraj Srinivasan
We study the value of AI innovations as it diffuses across general and application sectors, using the United States Patent and Trademark Office’s (USPTO) AI patent dataset. Investors value these innovations more than others, as AI patents exhibit a 9% value premium,... View Details
Keywords: AI and Machine Learning; Valuation; Technological Innovation; Open Source Distribution; Patents; Policy; Knowledge Sharing; Technology Industry
Chen, Wilbur Xinyuan, Terrence Tianshuo Shi, and Suraj Srinivasan. "The Value of AI Innovations." Harvard Business School Working Paper, No. 24-069, May 2024.
- 02 Oct 2018
- First Look
New Research and Ideas, October 2, 2018
policies. Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=55050 "Developing Theory Using Machine Learning Methods By: Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres... View Details
Keywords: Dina Gerdeman
- June 2025
- Case
AI Wars in 2025
By: Andy Wu and Anna Yang
In June 2025, Google leaders in Mountain View, CA convened after its parent company Alphabet shed a quarter-trillion in market capitalization in a matter of months. The immediate spark—the quiet revelation that Google searches had dipped for the first time in 20... View Details
- July–August 2025
- Article
Don’t Let an AI Failure Harm Your Brand
How companies market their AI systems affects the repercussions they face when their products fail. Marketers must promote their AI products with potential failure in mind. To do that, they must first understand consumers’ unique attitudes toward AI. Marketers who... View Details
Keywords: AI and Machine Learning; Brands and Branding; Product Marketing; Consumer Behavior; Attitudes
De Freitas, Julian. "Don’t Let an AI Failure Harm Your Brand." Harvard Business Review 103, no. 4 (July–August 2025): 126–133.
- 2023
- Article
Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten
By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an... View Details
Keywords: Analytics and Data Science; AI and Machine Learning; Decision Making; Governing Rules, Regulations, and Reforms
Lakkaraju, Himabindu, Satyapriya Krishna, and Jiaqi Ma. "Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 17808–17826.
- June 2025
- Article
Unregulated Emotional Risks of AI Wellness Apps
By: Julian De Freitas and Glenn Cohen
We propose that AI-driven wellness apps powered by large language models can foster extreme emotional attachments and dependencies akin to human relationships—posing risks like ambiguous loss and dysfunctional dependence—that challenge current regulatory frameworks and... View Details
De Freitas, Julian, and Glenn Cohen. "Unregulated Emotional Risks of AI Wellness Apps." Nature Machine Intelligence 7, no. 6 (June 2025): 813–815.
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming). (Pre-published online March 24, 2025.)
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.