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- 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
- 2025
- Working Paper
The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise
By: Fabrizio Dell'Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stew Taub and Karim R. Lakhani
We examine how artificial intelligence transforms the core pillars of collaboration—
performance, expertise sharing, and social engagement—through a pre-registered field
experiment with 776 professionals at Procter & Gamble, a global consumer packaged goods
company.... View Details
Keywords: Artificial Intelligence; Teamwork; Human-machine Interaction; Productivity; Skills; Innovation; Field Experiment; AI and Machine Learning; Groups and Teams; Competency and Skills; Performance Productivity; Collaborative Innovation and Invention; Product Development
Dell'Acqua, Fabrizio, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stew Taub, and Karim R. Lakhani. "The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise." Harvard Business School Working Paper, No. 25-043, March 2025.
- March 2025 (Revised April 2025)
- Case
Perplexity: Redefining Search
By: Suraj Srinivasan, Michelle Hu, Sriraghav Srinivasan and Radhika Kak
By early 2025, Perplexity had rapidly evolved from a modest startup into a trailblazing "answer engine" valued at $9 billion. Led by founder Aravind Srinivas, Perplexity AI had boldly positioned itself as the disruptor aiming to redefine search altogether. Through... View Details
Keywords: AI and Machine Learning; Venture Capital; Innovation Leadership; Technological Innovation; Internet and the Web; Technology Industry; United States
Srinivasan, Suraj, Michelle Hu, Sriraghav Srinivasan, and Radhika Kak. "Perplexity: Redefining Search." Harvard Business School Case 125-093, March 2025. (Revised April 2025.)
- March 2025
- Case
Harvey: AI for Lawyers
By: Suraj Srinivasan, Charles Krumholz and Radhika Kak
In early 2025, Winston Weinberg and Gabe Pereyra, co-founders of Harvey AI, reflected on the company’s meteoric rise as a pioneer in AI-powered legal technology. Since its founding in 2022, Harvey had transformed how lawyers approached research, drafting, and document... View Details
Keywords: Innovation Strategy; Business Startups; AI and Machine Learning; Technological Innovation; Growth and Development Strategy; Product Positioning; Legal Services Industry; Technology Industry; New York (city, NY); San Francisco; London
Srinivasan, Suraj, Charles Krumholz, and Radhika Kak. "Harvey: AI for Lawyers." Harvard Business School Case 125-087, March 2025.
- February 2025
- Article
Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots
By: Julian De Freitas and I. Glenn Cohen
In the wake of recent advancements in generative AI, regulatory bodies are trying to keep pace. One key decision is whether to require app makers to disclose the use of generative AI-powered chatbots in their products. We suggest that some generative AI-based chatbots... View Details
Keywords: AI and Machine Learning; Governing Rules, Regulations, and Reforms; Applications and Software; Well-being
De Freitas, Julian, and I. Glenn Cohen. "Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots." New England Journal of Medicine AI 2, no. 2 (February 2025).
- January 2025
- Case
Duolingo: On a 'Streak'
By: Jeffrey F. Rayport, Nicole Tempest Keller and Nicole Luo
In December 2024, Severin Hacker, Co-Founder and Chief Technology Officer of Duolingo, reflected on the remarkable evolution of the language-learning app he helped launch in 2011. As the #1 most downloaded education app in the world, Duolingo had over 100 million... View Details
Keywords: Learning; AI and Machine Learning; Growth and Development Strategy; Motivation and Incentives; Diversification; Business Model; Market Entry and Exit; Technology Industry; Education Industry; United States
Rayport, Jeffrey F., Nicole Tempest Keller, and Nicole Luo. "Duolingo: On a 'Streak'." Harvard Business School Case 825-097, January 2025.
- January 2025
- Technical Note
AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix
By: Tsedal Neeley and Tim Englehart
This technical note introduces the confusion matrix as a foundational tool in artificial intelligence (AI) and large language models (LLMs) for assessing the performance of classification models, focusing on their reliability for decision-making. A confusion matrix... View Details
- 2025
- Working Paper
Crossing the Design-Use Divide: How Process Manipulation Shapes the Design and Use of AI
By: Rebecca Karp
Existing literature often separates research on the design of innovations from their implementation and use, neglecting the role of selection—how organizations choose which innovations to implement. Although scholars suggest scientific approaches for selecting novel... View Details
Keywords: Decision Choices and Conditions; Technology Adoption; Groups and Teams; Prejudice and Bias
Karp, Rebecca. "Crossing the Design-Use Divide: How Process Manipulation Shapes the Design and Use of AI." Harvard Business School Working Paper, No. 25-034, January 2025.
- Working Paper
AI in Disguise—How AI-generated Ads' Visual Cues Shape Consumer Perception and Performance
By: Yannick Exner, Jochen Hartmann, Oded Netzer and Shunyuan Zhang
Generative AI’s recent advancements in creating content have offered vast potential to transform the advertising industry. This research investigates the impact of generative AI-enabled visual ad creation on real-world advertising effectiveness. For this purpose, we... View Details
Keywords: Digital Marketing; AI and Machine Learning; Advertising; Consumer Behavior; Advertising Industry
Exner, Yannick, Jochen Hartmann, Oded Netzer, and Shunyuan Zhang. "AI in Disguise—How AI-generated Ads' Visual Cues Shape Consumer Perception and Performance." SSRN Working Paper Series, No. 5096969.
- 2025
- Article
Humor as a Window into Generative AI Bias
By: Roger Samure, Julian De Freitas and Stefano Puntoni
A preregistered audit of 600 images by generative AI across 150 different prompts explores the link between humor and discrimination in consumer-facing AI solutions. When ChatGPT updates images to make them “funnier”, the prevalence of stereotyped groups changes. While... View Details
Samure, Roger, Julian De Freitas, and Stefano Puntoni. "Humor as a Window into Generative AI Bias." Art. 1326. Scientific Reports 15 (2025).
- January 2025
- Article
Reducing Prejudice with Counter-stereotypical AI
By: Erik Hermann, Julian De Freitas and Stefano Puntoni
Based on a review of relevant literature, we propose that the proliferation of AI with human-like and social features presents an unprecedented opportunity to address the underlying cognitive and affective drivers of prejudice. An approach informed by the psychology of... View Details
Keywords: Prejudice and Bias; AI and Machine Learning; Interpersonal Communication; Social and Collaborative Networks
Hermann, Erik, Julian De Freitas, and Stefano Puntoni. "Reducing Prejudice with Counter-stereotypical AI." Consumer Psychology Review 8, no. 1 (January 2025): 75–86.
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- January–February 2025
- Article
Want Your Company to Get Better at Experimentation?: Learn Fast by Democratizing Testing
By: Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley
For years, online experimentation has fueled the innovations of leading tech companies, enabling them to rapidly test and refine new ideas, optimize product features, personalize user experiences, and maintain a competitive edge. The widespread availability and lower... View Details
Keywords: Technological Innovation; AI and Machine Learning; Analytics and Data Science; Product Development; Competitive Advantage
Bojinov, Iavor, David Holtz, Ramesh Johari, Sven Schmit, and Martin Tingley. "Want Your Company to Get Better at Experimentation? Learn Fast by Democratizing Testing." Harvard Business Review 103, no. 1 (January–February 2025): 96–103.
- December 2024
- Case
Blue Cross Blue Shield of Michigan (BCBSM): The AI Journey
By: Shikhar Ghosh
In early 2024, Bill Fandrich, Executive VP and CIO of Blue Cross Blue Shield of Michigan (BCBSM), faced a critical decision about AI adoption within the organization. Fandrich had championed AI implementation at BCBSM. After successfully developing three AI... View Details
Keywords: AI; Machine Learning; Blue Cross; Automation; Digital Strategy; Digital Transformation; Generative Ai; Health Insurance; Insurance Companies; Innovation; IT Strategy; Leadership; Organizational Transformations; Technology; Non-profit; Michigan; AI and Machine Learning; Health; Health Industry; Michigan
Ghosh, Shikhar. "Blue Cross Blue Shield of Michigan (BCBSM): The AI Journey." Harvard Business School Case 825-082, December 2024.
- 2024
- Working Paper
Displacement or Complementarity? The Labor Market Impact of Generative AI
By: Wilbur Xinyuan Chen, Suraj Srinivasan and Saleh Zakerinia
Generative AI is poised to reshape the labor market, affecting cognitive and white-collar occupations in ways distinct from past technological revolutions. This study examines whether generative AI displaces workers or augments their jobs by analyzing labor demand and... View Details
Keywords: Generative Ai; Labor Market; Automation And Augmentation; Labor; AI and Machine Learning; Competency and Skills
Chen, Wilbur Xinyuan, Suraj Srinivasan, and Saleh Zakerinia. "Displacement or Complementarity? The Labor Market Impact of Generative AI." Harvard Business School Working Paper, No. 25-039, December 2024.
- 2024
- Working Paper
Why Most Resist AI Companions
By: Julian De Freitas, Zeliha Oğuz-Uğuralp, Ahmet Kaan Uğuralp and Stefano Puntoni
Chatbots are now able to form emotional relationships with people and alleviate loneliness—a growing public health concern. Behavioral research provides little insight into whether everyday people are likely to use these applications and why. We address this question... View Details
Keywords: Generative Ai; Chatbots; Artificial Intelligence; Algorithmic Aversion; Lonelines; Technology Adoption; AI and Machine Learning; Well-being; Emotions
De Freitas, Julian, Zeliha Oğuz-Uğuralp, Ahmet Kaan Uğuralp, and Stefano Puntoni. "Why Most Resist AI Companions." Harvard Business School Working Paper, No. 25-030, December 2024. (Revised January 2025.)
- November 2024
- Case
AlphaGo (A): Birth of a New Intelligence
By: Shikhar Ghosh and Shweta Bagai
This case, the first of a three-part series, traces DeepMind's evolution from its 2010 founding through its acquisition by Google in 2014. Often referred to as the "Apollo project" of artificial intelligence, DeepMind used games as a testing ground to develop AI... View Details
Keywords: AI and Machine Learning; Game Theory; Technology Adoption; Games, Gaming, and Gambling; Technology Industry; South Korea; China; United States
Ghosh, Shikhar, and Shweta Bagai. "AlphaGo (A): Birth of a New Intelligence." Harvard Business School Case 825-073, November 2024.
- November 2024
- Supplement
AlphaGo (B): Birth of a New Intelligence
By: Shikhar Ghosh and Shweta Bagai
This case, the second in a three-part series, explores DeepMind's evolution from developing game-specific AI to more generalized learning systems. Following AlphaGo's 2017 victory over the Go world champion, DeepMind introduced two revolutionary systems that eliminated... View Details
Keywords: AI and Machine Learning; Games, Gaming, and Gambling; Technological Innovation; Information Technology Industry; United States; Russia; China
Ghosh, Shikhar, and Shweta Bagai. "AlphaGo (B): Birth of a New Intelligence." Harvard Business School Supplement 825-074, November 2024.
- November 2024
- Supplement
AlphaGo (C): Birth of a New Intelligence
By: Shikhar Ghosh and Shweta Bagai
This case, the final of a three-part series, explores DeepMind's pivotal transition from mastering games to solving real-world scientific challenges. In December 2020, DeepMind's AI system AlphaFold 2 achieved a breakthrough by solving protein folding—a 50-year-old... View Details
Keywords: AI; Artificial Intelligence; Machine Learning; Research; Autonomy; Deep Learning; Drug Discovery; Healthcare Innovation; Neural Networks; Information Technology; Research And Development; Scientific Research; Technology Startup; AI and Machine Learning; Technological Innovation; United States
Ghosh, Shikhar, and Shweta Bagai. "AlphaGo (C): Birth of a New Intelligence." Harvard Business School Supplement 825-075, November 2024.
- 2024
- Working Paper
Scaling Core Earnings Measurement with Large Language Models
By: Matthew Shaffer and Charles CY Wang
We study the application of large language models (LLMs) to the estimation of core earnings, i.e., a firm's persistent profitability from its core business activities. This construct is central to investors' assessments of economic performance and valuations. However,... View Details
Keywords: Large Language Models; AI and Machine Learning; Accounting; Profit; Corporate Disclosure; Analytics and Data Science; Measurement and Metrics
Shaffer, Matthew, and Charles CY Wang. "Scaling Core Earnings Measurement with Large Language Models." Working Paper, November 2024.