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- All HBS Web
(926)
- Faculty Publications (352)
- February 2024
- Teaching Note
TimeCredit
By: Emanuele Colonnelli, Raymond Kluender and Shai Benjamin Bernstein
Teaching Note for HBS Case No. 824-139. TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting... View Details
- February 2024
- Case
Continuity & Change at Boston Consulting Group
By: David G. Fubini, Suraj Srinivasan and David Lane
As the new CEO of Boston Consulting Group (BCG) since autumn 2021, Christoph Schweizer had big shoes to fill—his predecessor, Rich Lesser, had tripled the partnership’s total revenues and created digital initiatives that contributed 40+% of 2021 revenues, more than... View Details
Keywords: Business Organization; Change Management; Talent and Talent Management; Governance; AI and Machine Learning; Environmental Sustainability; Leading Change; Risk Management; Organizational Culture; Partners and Partnerships; Revenue; Growth and Development Strategy; Management Succession; Consulting Industry
Fubini, David G., Suraj Srinivasan, and David Lane. "Continuity & Change at Boston Consulting Group." Harvard Business School Case 124-011, February 2024.
- February 2024
- Case
ReSpo.Vision: The Kickstart of an AI Sports Revolution
By: Paul A. Gompers, Elena Corsi and Nikolina Jonsson
This case study explores the growth journey of Polish computer vision sports start-up ReSpo.Vision in an emerging entrepreneurial ecosystem. By providing 3D data and analysis to soccer clubs, ReSpo.Vision achieved significant milestones with a €1 million seed round, an... View Details
Keywords: Business Startups; Business Plan; Experience and Expertise; Talent and Talent Management; Decisions; Decision Choices and Conditions; Forecasting and Prediction; Entrepreneurship; Venture Capital; AI and Machine Learning; Analytics and Data Science; Applications and Software; Business Strategy; Sports Industry; Technology Industry; Poland; Europe
Gompers, Paul A., Elena Corsi, and Nikolina Jonsson. "ReSpo.Vision: The Kickstart of an AI Sports Revolution." Harvard Business School Case 824-151, February 2024.
- February 2024 (Revised September 2024)
- Case
TimeCredit
By: Emanuele Colonnelli, Raymond Kluender and Shai Benjamin Bernstein
TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting background, as she decides how much... View Details
Keywords: Accounting; Business Startups; Entrepreneurship; Financing and Loans; AI and Machine Learning; Entrepreneurial Finance; Identity; Technology Industry
Colonnelli, Emanuele, Raymond Kluender, and Shai Benjamin Bernstein. "TimeCredit." Harvard Business School Case 824-139, February 2024. (Revised September 2024.)
- February 2024
- Case
AGENTS.inc: Pathways to Growth at an AI Startup
By: Frank Nagle, Manuel Hoffmann, Karoline Ströhlein and Susan Pinckney
The case describes the history of AGENTS.inc. Despite being a small startup, with only four employees, that had never had a funding round, the company boasted an impressive client portfolio including multiple Fortune 500 companies. While AGENTS.inc had been an early... View Details
Keywords: Business Growth and Maturation; Business Model; Business Startups; Small Business; Transformation; Customer Focus and Relationships; Decisions; Entrepreneurship; Venture Capital; Financial Strategy; AI and Machine Learning; Digital Platforms; Technological Innovation; Copyright; Management; Growth and Development; Market Timing; Ownership; Risk and Uncertainty; Competition; Open Source Distribution; Entrepreneurial Finance; Computer Industry; Europe; Germany
Nagle, Frank, Manuel Hoffmann, Karoline Ströhlein, and Susan Pinckney. "AGENTS.inc: Pathways to Growth at an AI Startup." Harvard Business School Case 724-444, February 2024.
- February 2024
- Technical Note
AI Product Development Lifecycle
By: Michael Parzen, Jessie Li and Marily Nika
In this article, we will discuss the concept of AI Products, how they are changing our daily lives, how the field of AI & Product Management is evolving, and the AI Product Development Lifecycle. View Details
Keywords: Artificial Intelligence; Product Management; Product Life Cycle; Technology; AI and Machine Learning; Product Development
Parzen, Michael, Jessie Li, and Marily Nika. "AI Product Development Lifecycle." Harvard Business School Technical Note 624-070, February 2024.
- February 6, 2024
- Article
Find the AI Approach That Fits the Problem You’re Trying to Solve
By: George Westerman, Sam Ransbotham and Chiara Farronato
AI moves quickly, but organizations change much more slowly. What works in a lab may be wrong for your company right now. If you know the right questions to ask, you can make better decisions, regardless of how fast technology changes. You can work with your technical... View Details
Keywords: AI and Machine Learning; Organizational Change and Adaptation; Technological Innovation; Analytics and Data Science
Westerman, George, Sam Ransbotham, and Chiara Farronato. "Find the AI Approach That Fits the Problem You’re Trying to Solve." Harvard Business Review (website) (February 6, 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.
- 2024
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift
By: Matthew DosSantos DiSorbo and Kris Ferreira
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,... View Details
DosSantos DiSorbo, Matthew, and Kris Ferreira. "Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift." Working Paper, February 2024.
- January 2024
- Case
Huawei: Resilience amid Autarky and Adversity
By: William C. Kirby and Daniel Fu
In September 2023, Huawei made a dramatic return to the global smartphone space with the launch of its Mate 60 Pro smartphone, equipped with an indigenously designed, 7nm chip. This came despite a myriad of export controls and restrictions imposed against the company... View Details
Keywords: International Strategy; Semiconductors; Smartphone; Government And Politics; Government And Business; Digital Infrastructure; 5G; Political Risk; Business and Government Relations; Global Strategy; Multinational Firms and Management; Governing Rules, Regulations, and Reforms; AI and Machine Learning; Mobile and Wireless Technology; Leadership; Retirement; Corporate Strategy; Technology Industry; China; United States; Europe; Asia; Middle East
- January 2024
- Case
The Financial Times (FT) and Generative AI
By: Andrew Rashbass, Ramon Casadesus-Masanell and Jordan Mitchell
In September 2023, John Ridding, CEO of the Financial Times, was considering the possible impact of Generative AI on the industry and his business. Having navigated successfully the seismic shift from print to digital, and reporting record results, the company... View Details
Keywords: AI and Machine Learning; Technology Adoption; Change Management; Journalism and News Industry
Rashbass, Andrew, Ramon Casadesus-Masanell, and Jordan Mitchell. "The Financial Times (FT) and Generative AI." Harvard Business School Case 724-410, January 2024.
- January 2024 (Revised February 2024)
- Case
OpenAI: Idealism Meets Capitalism
By: Shikhar Ghosh and Shweta Bagai
In November 2023, the board of OpenAI, one of the most successful companies in the history of technology, decided to fire Sam Altman, its charismatic and influential CEO. Their decision shocked the corporate world and had people wondering why OpenAI had designed a... View Details
Keywords: AI; AI and Machine Learning; Governing and Advisory Boards; Ethics; Strategy; Technological Innovation; Leadership
Ghosh, Shikhar, and Shweta Bagai. "OpenAI: Idealism Meets Capitalism." Harvard Business School Case 824-134, January 2024. (Revised February 2024.)
- January 2024 (Revised February 2024)
- Case
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its... View Details
Keywords: Investor Relations; Forecasting; Machine Learning; Artificial Intelligence; Apparel; Corporate Finance; Forecasting and Prediction; AI and Machine Learning; Digital Transformation; Apparel and Accessories Industry; United States
Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM... View Details
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 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.)
- December 2023 (Revised August 2024)
- 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... View Details
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Book
Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow
By: Ken Huang, Yang Wang, Feng Zhu, Xi Chen and Chunxiao Xing
This book explores the transformative potential of ChatGPT, Web3, and their impact on productivity and various industries. It delves into Generative AI (GenAI) and its representative platform ChatGPT, their synergy with Web3, and how they can revolutionize business... View Details
Huang, Ken, Yang Wang, Feng Zhu, Xi Chen, and Chunxiao Xing, eds. Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow. Springer, 2023.
- 2023
- Article
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models
By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for... View Details
Keywords: AI and Machine Learning
Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.