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      • 2024
      • Working Paper

      Climate Solutions, Transition Risk, and Stock Returns

      By: Shirley Lu, Edward J. Riedl, Simon Xu and George Serafeim
      Using large language models to measure firms' climate solution products and services, we find that high-climate solution firms exhibit lower stock returns and higher market valuation multiples. Their stock prices respond positively to events signaling increased demand... View Details
      Keywords: Technology; Generative Ai; Large Language Models; Climate Finance; Climate Change; Innovation and Invention; Environmental Sustainability; AI and Machine Learning; Investment; Financial Markets
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      Lu, Shirley, Edward J. Riedl, Simon Xu, and George Serafeim. "Climate Solutions, Transition Risk, and Stock Returns." Harvard Business School Working Paper, No. 25-024, November 2024.
      • November–December 2024
      • Article

      Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

      By: Kirk Bansak and Elisabeth Paulson
      This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
      Keywords: AI and Machine Learning; Refugees; Geographic Location; Employment
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      Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research 72, no. 6 (November–December 2024): 2375–2390.
      • October 2024 (Revised April 2025)
      • Case

      Nvidia

      By: Andy Wu and Matt Higgins
      This case study examines Nvidia's strategic pivot from gaming GPUs to becoming a leader in general-purpose computing and AI. It explores how Nvidia leveraged its GPU architecture to dominate the growing fields of data center acceleration and AI training, outpacing... View Details
      Keywords: Strategy; Technological Innovation; AI and Machine Learning; Product Development; Manufacturing Industry; Technology Industry; Electronics Industry; United States; China; Taiwan
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      Wu, Andy, and Matt Higgins. "Nvidia." Harvard Business School Case 725-383, October 2024. (Revised April 2025.)
      • October 2024
      • Case

      Reed Group and Succession in a Family Business: An Impossible Job to Fill?

      By: Lauren H. Cohen and Tonia Labruyere
      James Reed had taken over Reed Group, the recruitment and career services company his father had founded and built, in 1994. He was now reflecting on succession planning and other challenges that lay ahead: with no obvious choice among his family members, he needed to... View Details
      Keywords: Charity; Succession Planning; Family Business; Values and Beliefs; Management Succession; Mission and Purpose; Family Ownership; Philanthropy and Charitable Giving; Family and Family Relationships; Recruitment; AI and Machine Learning; Employment Industry; United Kingdom; London
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      Cohen, Lauren H., and Tonia Labruyere. "Reed Group and Succession in a Family Business: An Impossible Job to Fill?" Harvard Business School Case 825-084, October 2024.
      • 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
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      De Freitas, Julian, and Elie Ofek. "AI and Brand Management: Promises and Perils." Harvard Business School Case 525-021, October 2024. (Revised February 2025.)
      • 2025
      • Working Paper

      Lessons from an App Update at Replika AI: Identity Discontinuity in Human-AI Relationships

      By: Julian De Freitas, Noah Castelo, Ahmet Kaan Uğuralp and Zeliha Oğuz-Uğuralp
      As consumers increasingly interact with AI applications specialized for social relationships, what is the nature and depth of these relationships among actual users, and can company actions influence these dynamics? We find that active users of the US-based AI... View Details
      Keywords: AI and Machine Learning; Welfare; Loss; Well-being; Identity; Perception; Relationships
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      De Freitas, Julian, Noah Castelo, Ahmet Kaan Uğuralp, and Zeliha Oğuz-Uğuralp. "Lessons from an App Update at Replika AI: Identity Discontinuity in Human-AI Relationships." Harvard Business School Working Paper, No. 25-018, October 2024. (Revised May 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
      Keywords: AI and Machine Learning; Gender; Equality and Inequality; Technology Adoption; Behavior
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      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.)
      • September–October 2024
      • Article

      The Crowdless Future? Generative AI and Creative Problem-Solving

      By: Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic and Karim R. Lakhani
      The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy... View Details
      Keywords: Large Language Models; Generative Ai; Crowdsourcing; AI and Machine Learning; Creativity; Technological Innovation
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      Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem-Solving." Organization Science 35, no. 5 (September–October 2024): 1589–1607.
      • September 2024
      • Background Note

      Copyright and Fair Use

      By: David B. Yoffie
      The U.S. Copyright Office defines a copyright as “a type of intellectual property that protects original works of authorship as soon as an author fixes the work in a tangible form of expression.” Two core principles of copyright are originality and fixation. A work is... View Details
      Keywords: AI and Machine Learning; Copyright; Lawsuits and Litigation; United States
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      Yoffie, David B. "Copyright and Fair Use." Harvard Business School Background Note 725-394, September 2024.
      • September 2024
      • Exercise

      Finding Your 'Jagged Frontier': A Generative AI Exercise

      By: Mitchell Weiss
      In 2023 a set of scholars set out to study the effect of artificial intelligence (AI) on the quality and productivity of knowledge workers—in this specific instance, management consultants. They wanted to know across a range of tasks in a workflow, which, if any, would... View Details
      Keywords: AI and Machine Learning; Performance Productivity; Performance Evaluation; Consulting Industry
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      Weiss, Mitchell. "Finding Your 'Jagged Frontier': A Generative AI Exercise." Harvard Business School Exercise 825-070, September 2024.
      • September 23, 2024
      • Article

      AI Wants to Make You Less Lonely. Does It Work?

      By: Julian De Freitas
      Keywords: AI and Machine Learning; Well-being
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      De Freitas, Julian. "AI Wants to Make You Less Lonely. Does It Work?" Wall Street Journal (September 23, 2024), R.11.
      • 2024
      • Working Paper

      Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python

      By: Melissa Ouellet and Michael W. Toffel
      This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in... View Details
      Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods
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      Ouellet, Melissa, and Michael W. Toffel. "Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python." Harvard Business School Working Paper, No. 25-010, August 2024.
      • September 2024 (Revised January 2025)
      • Exercise

      Building an AI First Snack Company: A Hands-on Generative AI Exercise

      By: Iavor I. Bojinov
      Although the term 'Generative AI' (GenAI) is widely recognized, its practical application in daily workflows has yet to be understood. This exercise introduces students to GenAI tools, demonstrating how they can be seamlessly integrated into professional work practices... View Details
      Keywords: AI and Machine Learning; Technology Adoption; Marketing Strategy; Product Launch; Brands and Branding
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      Bojinov, Iavor I. "Building an AI First Snack Company: A Hands-on Generative AI Exercise." Harvard Business School Exercise 625-052, September 2024. (Revised January 2025.)
      • September–October 2024
      • Article

      How AI Can Power Brand Management

      By: Julian De Freitas and Elie Ofek
      Marketers have begun experimenting with AI to improve their brand-management efforts. But unlike other marketing tasks, brand management involves more than just repeatedly executing one specialized function. Long considered the exclusive domain of creative talent, it... View Details
      Keywords: Creativity; AI and Machine Learning; Brands and Branding; Product Positioning; Customer Focus and Relationships
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      De Freitas, Julian, and Elie Ofek. "How AI Can Power Brand Management." Harvard Business Review 102, no. 5 (September–October 2024): 108–114.
      • 2024
      • Article

      Learning Under Random Distributional Shifts

      By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
      Algorithmic assignment of refugees and asylum seekers to locations within host countries has gained attention in recent years, with implementations in the U.S. and Switzerland. These approaches use data on past arrivals to generate machine learning models that can... View Details
      Keywords: AI and Machine Learning; Refugees; Employment
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      Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Learning Under Random Distributional Shifts." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 27th (2024).
      • August 2024 (Revised March 2025)
      • Case

      DBS' AI Journey

      By: Feng Zhu, Harold Zhu and Adina Wong
      Headquartered in Singapore, DBS Bank, one of Asia's leading financial services groups, embarked on a multi-year digital transformation under CEO Piyush Gupta in 2014. It was then that DBS also began experimenting with AI to drive value for the business and customers.... View Details
      Keywords: Corporate Governance; AI and Machine Learning; Digital Transformation; Risk Management; Value Creation; Banking Industry; Financial Services Industry; Asia; Singapore
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      Zhu, Feng, Harold Zhu, and Adina Wong. "DBS' AI Journey." Harvard Business School Case 625-053, August 2024. (Revised March 2025.)
      • August 2024
      • Background Note

      Mitigating Climate Change with Machine Learning

      By: Michael W. Toffel, Kelsey Carter, Amy Chambers, Avery Park and Susan Pinckney
      This note highlights how machine learning is being used to decarbonize (reduce GHG emissions) several key sectors including electricity, transportation, building, industrial processes, and agriculture -- and how machine learning is being used to accelerate efforts to... View Details
      Keywords: Climate; Artificial Intelligence; Adaptation; Climate Change; AI and Machine Learning; Innovation and Invention
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      Toffel, Michael W., Kelsey Carter, Amy Chambers, Avery Park, and Susan Pinckney. "Mitigating Climate Change with Machine Learning." Harvard Business School Background Note 625-014, August 2024.
      • August 13, 2024
      • Editorial

      Can AI Save Physicians from Burnout?

      By: Susanna Gallani, Lidia Moura and Katie Sonnefeldt
      Keywords: Well-being; AI and Machine Learning; Work-Life Balance; Health Industry
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      Gallani, Susanna, Lidia Moura, and Katie Sonnefeldt. "Can AI Save Physicians from Burnout?" Harvard Business School Working Knowledge (August 13, 2024).
      • 2025
      • Working Paper

      Narrative AI and the Human-AI Oversight Paradox in Evaluating Early-Stage Innovations

      By: Jacqueline N. Lane, Léonard Boussioux, Charles Ayoubi, Ying Hao Chen, Camila Lin, Rebecca Spens, Pooja Wagh and Pei-Hsin Wang
      Do AI-generated narrative explanations enhance human oversight or diminish it? We investigate this question through a field experiment with 228 evaluators screening 48 early-stage innovations under three conditions: human-only, black-box AI recommendations without... View Details
      Keywords: Large Language Models; AI and Machine Learning; Innovation and Invention; Decision Making
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      Lane, Jacqueline N., Léonard Boussioux, Charles Ayoubi, Ying Hao Chen, Camila Lin, Rebecca Spens, Pooja Wagh, and Pei-Hsin Wang. "Narrative AI and the Human-AI Oversight Paradox in Evaluating Early-Stage Innovations." Harvard Business School Working Paper, No. 25-001, August 2024. (Revised May 2025.)
      • 2024
      • Article

      Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules

      By: Michael Lingzhi Li and Kosuke Imai
      A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
      Keywords: AI and Machine Learning; Research
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      Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
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