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- April 2024 (Revised December 2024)
- Case
Anthropic: Building Safe AI
By: Shikhar Ghosh and Shweta Bagai
In late 2024, Anthropic, a leading AI safety and research company, achieved a significant breakthrough with computer use capabilities that allowed AI to interact with computers like humans. Co-founded by former OpenAI employees and known for its generative AI... View Details
Keywords: AI and Machine Learning; Corporate Accountability; Corporate Social Responsibility and Impact; Business Growth and Maturation; Corporate Strategy; Technology Industry; United States
Ghosh, Shikhar, and Shweta Bagai. "Anthropic: Building Safe AI." Harvard Business School Case 824-129, April 2024. (Revised December 2024.)
- April 2024
- Case
Managing AI Risks in Consumer Banking
By: Suraj Srinivasan, Satish Tadikonda, Paul Dongha, Manoj Saxena and Radhika Kak
In early 2024, Ruth Jones, head of digital banking at Signa Bank, a (fictitious) European consumer bank, was thinking about how to best incorporate GenAI capabilities to improve efficiencies and create new ways to improve the customer experience. Where were the biggest... View Details
Keywords: Customer Relationship Management; AI and Machine Learning; Risk Management; Opportunities; Customization and Personalization; Banking Industry; Europe
Srinivasan, Suraj, Satish Tadikonda, Paul Dongha, Manoj Saxena, and Radhika Kak. "Managing AI Risks in Consumer Banking." Harvard Business School Case 124-093, April 2024.
- 2024
- Working Paper
Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions
By: Caleb Kwon, Ananth Raman and Jorge Tamayo
We investigate whether corporate officers should grant managers discretion to override AI-driven demand forecasts and labor scheduling tools. Analyzing five years of administrative data from a large grocery retailer using such an AI tool, encompassing over 500 stores,... View Details
Keywords: AI and Machine Learning; Forecasting and Prediction; Working Conditions; Performance Productivity
Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, April 2024.
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
- April 2024
- Teaching Plan
Fixie and Conversational AI Sidekicks
By: Jeffrey J. Bussgang and Tom Quinn
Teaching Plan for HBS Case No. 824-037, “Fixie and Conversational AI Sidekicks.” View Details
- April 2024 (Revised November 2024)
- Case
Moderna: Pioneering a People Platform to Accelerate Science Innovation
By: Tatiana Sandino, Emil Dy and Samuel Grad
Moderna was founded in 2010 to explore how messenger ribonucleic acid (mRNA) could be used to create breakthrough medicines by encoding instructions for the body to create antibodies. When Stéphane Bancel (HBS 2000) took over in 2011, he bet on the potential of this... View Details
Keywords: Disruptive Innovation; Talent and Talent Management; Selection and Staffing; AI and Machine Learning; Digital Strategy; Innovation and Management; Leadership Development; Management Practices and Processes; Management Systems; Organizational Culture; Performance Evaluation; Alignment; Employee Relationship Management; Science-Based Business; Expansion; Pharmaceutical Industry; Biotechnology Industry; United States
Sandino, Tatiana, Emil Dy, and Samuel Grad. "Moderna: Pioneering a People Platform to Accelerate Science Innovation." Harvard Business School Case 124-091, April 2024. (Revised November 2024.)
- March 2024 (Revised April 2024)
- Case
Coursera's Foray into GenAI
By: Suraj Srinivasan, Michael Parzen and Radhika Kak
In early 2023, Maggioncalda, CEO of US EdTech firm Coursera, launched Project Genesis to develop a strategy for incorporating GenAI capabilities into the firm's offerings, asking his teams to focus on value to the firm and cost of implementation. The team identified... View Details
Keywords: Business Model; AI and Machine Learning; Brands and Branding; Business Strategy; Competitive Advantage; Technological Innovation; Education Industry; Technology Industry; United States
Srinivasan, Suraj, Michael Parzen, and Radhika Kak. "Coursera's Foray into GenAI." Harvard Business School Case 124-089, March 2024. (Revised April 2024.)
- March 2024 (Revised May 2024)
- Case
Amperity: First-Party Data at a Crossroads
By: Elie Ofek, Hema Yoganarasimhan and Alexis Lefort
In the summer of 2023, Amperity management was facing a critical decision on its future direction. Given the dramatic changes occurring within the digital advertising ecosystem, as concerns over consumer privacy placed limits on the ability to engage in third-party... View Details
Keywords: AI and Machine Learning; Technology Adoption; Business Strategy; Digital Marketing; Price; Product; Business or Company Management; Advertising Industry
Ofek, Elie, Hema Yoganarasimhan, and Alexis Lefort. "Amperity: First-Party Data at a Crossroads." Harvard Business School Case 524-017, March 2024. (Revised May 2024.)
- March 2024
- Simulation
'Storrowed'
By: Mitchell Weiss
The game was built to accompany "Storrowed": A Generative AI Exercise, available through Harvard Business Publishing. The game adds a timing element to "Storrowed" and enables the teacher to reward teams for strong prompts or penalize teams for believing AI... View Details
- March 2024 (Revised April 2025)
- Case
TELEXISTENCE Inc.
By: Paul A. Gompers and Akiko Saito
A case about a Japanese robotics startup aiming to enter the U.S. market with its robots that combine AI and human intervention to complete restocking tasks in retail stores. View Details
Keywords: Business Startups; Entrepreneurship; Market Entry and Exit; Technology Adoption; Decisions; AI and Machine Learning; Retail Industry; Technology Industry; Japan; United States
Gompers, Paul A., and Akiko Saito. "TELEXISTENCE Inc." Harvard Business School Case 224-031, March 2024. (Revised April 2025.)
- 2024
- Working Paper
The Cram Method for Efficient Simultaneous Learning and Evaluation
By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
Keywords: AI and Machine Learning
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
- March 2024
- Teaching Note
'Storrowed': A Generative AI Exercise
By: Mitchell Weiss
Teaching Note for HBS Exercise No. 824-188. “Storrowed” is an exercise to help participants raise their proficiency with generative AI. It begins by highlighting a problem: trucks getting wedged underneath bridges in Boston, Massachusetts on the city’s Storrow Drive.... View Details
- March 2024
- Exercise
'Storrowed': A Generative AI Exercise
By: Mitchell Weiss
"Storrowed" is an exercise to help participants raise their capacity and curiosity for generative AI. It focuses on generative AI for problem understanding and ideation, but can be adapted for use more broadly. Participants use generative AI tools to understand a... View Details
Weiss, Mitchell. "'Storrowed': A Generative AI Exercise." Harvard Business School Exercise 824-188, March 2024.
- March 2024 (Revised March 2024)
- Teaching Note
CoPilot(s): Generative AI at Microsoft and GitHub
By: Frank Nagle and Maria P. Roche
This teaching note is the companion to case N9-624-010 CoPilot(s): Generative AI at Microsoft and GitHub, which takes place in late 2021. The case briefly describes the history of both GitHub and Microsoft with a particular focus on open source software (OSS)—software... View Details
- March 7, 2024
- Article
Integrating Digital Tools into Every Stage of Your Sales Strategy
By: Frank V. Cespedes and Georg Krentzel
In their growth and customer-acquisition activities, most companies now face twin challenges: understanding and responding to omni-channel buying behavior and doing that without inadvertently decreasing sales productivity. Thirty years ago, Peter Drucker noted that... View Details
Keywords: Sales Management; Digital Tools; Sales; Marketing Channels; Technology Adoption; Brands and Branding
Cespedes, Frank V., and Georg Krentzel. "Integrating Digital Tools into Every Stage of Your Sales Strategy." Harvard Business Review (website) (March 7, 2024).
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
- March 2024 (Revised May 2025)
- Case
Governing OpenAI (A)
By: Lynn S. Paine, Suraj Srinivasan and Will Hurwitz
In late November 2023, OpenAI’s new board of directors took stock of the situation. The company, which sought to develop artificial general intelligence (AGI)—computer systems with capabilities exceeding human abilities—was looking to regain its footing after a chaotic... View Details
Keywords: Artificial Intelligence; Board Of Directors; Board Decisions; Board Dynamics; Corporate Boards; Governance Changes; Governance Structure; Leadership Change; Legal Aspects Of Business; Nonprofit Governance; Strategy And Execution; Technological Change; AI and Machine Learning; Corporate Governance; Leadership; Management; Mission and Purpose; Technological Innovation; Governing Rules, Regulations, and Reforms; Governing and Advisory Boards; Resignation and Termination; Ethics; Nonprofit Organizations; Open Source Distribution; Partners and Partnerships; Technology Industry; San Francisco; United States
Paine, Lynn S., Suraj Srinivasan, and Will Hurwitz. "Governing OpenAI (A)." Harvard Business School Case 324-103, March 2024. (Revised May 2025.)
- March 2024 (Revised August 2024)
- Case
Darktrace: Scaling Cybersecurity and AI (A)
By: Jeffrey F. Rayport and Alexis Lefort
In 2023, Darktrace CEO Poppy Gustafsson was contemplating her growth strategy at a leading U.K.-based cybersecurity venture, launched in 2013 by a group of anti-terror cyber specialists, University of Cambridge mathematicians, and artificial intelligence (AI) experts.... View Details
Keywords: Technology; Talent; Scaling; Entrepreneurship; Cybersecurity; Leadership; Business Growth and Maturation; Recruitment; Resignation and Termination; AI and Machine Learning; Growth and Development Strategy; Organizational Culture; Going Public; Technology Industry; United Kingdom; Europe; United States
Rayport, Jeffrey F., and Alexis Lefort. "Darktrace: Scaling Cybersecurity and AI (A)." Harvard Business School Case 824-092, March 2024. (Revised August 2024.)
- March 2024 (Revised August 2024)
- Supplement
Darktrace: Scaling Cybersecurity and AI (B)
By: Jeffrey F. Rayport and Alexis Lefort
Rayport, Jeffrey F., and Alexis Lefort. "Darktrace: Scaling Cybersecurity and AI (B)." Harvard Business School Supplement 824-179, March 2024. (Revised August 2024.)
- February 26, 2024
- Article
Making Workplaces Safer Through Machine Learning
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
Machine learning algorithms can dramatically improve regulatory effectiveness. This short article describes the authors' scholarly work that shows how the U.S. Occupational Safety and Health Administration (OSHA) could have reduced nearly twice as many occupational... View Details
Keywords: Government Experimentation; Auditing; Inspection; Evaluation; Process Improvement; Government Administration; AI and Machine Learning; Safety; Governing Rules, Regulations, and Reforms
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Making Workplaces Safer Through Machine Learning." Regulatory Review (February 26, 2024).