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(1,199)
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Show Results For
- All HBS Web
(1,199)
- People (1)
- News (234)
- Research (678)
- Events (17)
- Multimedia (8)
- Faculty Publications (563)
- 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
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).
- 22 Feb 2024
- Research & Ideas
How to Make AI 'Forget' All the Private Data It Shouldn't Have
There’s a virtual elephant in AI’s room: It’s nearly impossible to make the technology forget. And there are an increasing number of scenarios where consumers and programmers may not only want to remove data... View Details
- September 2023 (Revised April 2024)
- Case
Atomwise: Strategic Opportunities in AI for Pharma
By: Satish Tadikonda
Abraham Heifets and his co-founder, Izhar Wallach, had founded Atomwise to develop i) an AI engine to transform drug discovery by creating better medicines faster, and ii) a machine learning-based discovery engine that combined the power of convolutional neural... View Details
Keywords: Business Model; Business Startups; AI and Machine Learning; Science-Based Business; Technological Innovation; Biotechnology Industry; Pharmaceutical Industry
Tadikonda, Satish. "Atomwise: Strategic Opportunities in AI for Pharma." Harvard Business School Case 824-043, September 2023. (Revised April 2024.)
- 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.
- 30 May 2023
- Research & Ideas
Can AI Predict Whether Shoppers Would Pick Crest or Colgate?
but large language models like generative pre-trained transformers (GPTs) may allow companies to rely on AI to uncover consumers’ tastes, according to new research from Harvard Business School and Microsoft.... View Details
Keywords: by Kristen Senz
- Research Summary
Overview
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
- 2024
- Working Paper
Old Moats for New Models: Openness, Control, and Competition in Generative AI
By: Pierre Azoulay, Joshua L. Krieger and Abhishek Nagaraj
Drawing insights from the field of innovation economics, we discuss the likely competitive environment shaping generative AI advances. Central to our analysis are the concepts of appropriability—whether firms in the industry are able to control the knowledge generated... View Details
Azoulay, Pierre, Joshua L. Krieger, and Abhishek Nagaraj. "Old Moats for New Models: Openness, Control, and Competition in Generative AI." NBER Working Paper Series, No. 7442, May 2024.
- November–December 2022
- Article
Can AI Really Help You Sell?: It Can, Depending on When and How You Implement It
By: Jim Dickie, Boris Groysberg, Benson P. Shapiro and Barry Trailer
Many salespeople today are struggling; only 57% of them make their annual quotas, surveys show. One problem is that buying processes have evolved faster than selling processes, and buyers today can access a wide range of online resources that let them evaluate products... View Details
Dickie, Jim, Boris Groysberg, Benson P. Shapiro, and Barry Trailer. "Can AI Really Help You Sell? It Can, Depending on When and How You Implement It." Harvard Business Review 100, no. 6 (November–December 2022): 120–129.
- January–February 2020
- Article
Competing in the Age of AI
By: Marco Iansiti and Karim R. Lakhani
Today’s markets are being reshaped by a new kind of firm—one in which artificial intelligence (AI) runs the show. This cohort includes giants like Google, Facebook, and Alibaba, and growing businesses such as Wayfair and Ocado. Every time we use their services, the... View Details
Keywords: Artificial Intelligence; Algorithms; Technological Innovation; Business Model; Competition; Competitive Strategy; AI and Machine Learning
Iansiti, Marco, and Karim R. Lakhani. "Competing in the Age of AI." Harvard Business Review 98, no. 1 (January–February 2020): 60–67.
- 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
- October 2023
- Article
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details
Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
- 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.
- 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.
- January 2025
- Teaching Note
AGENTS.inc: Pathways to Growth at an AI Startup
By: Frank Nagle and Susan Pinckney
Teaching Note for HBS Case No. 724-444. In 2024, AI agent startup company AGENTS.inc faced multiple strategic decisions that could shape the company’s ability to grow into the future AI agent market leader. View Details
- December 2020
- Supplement
VIA Science (B)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the... View Details
Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (B)." Harvard Business School Supplement 721-368, December 2020.
- Web
Generative AI - Alumni
Careers Generative AI Careers Generative AI In today's dynamic job market, Generative Artificial Intelligence (GenAI) stands out as a powerful ally for job seekers, offering personalized insights into... View Details
- 2023
- Working Paper
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
By: Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine... View Details
Keywords: Large Language Model; AI and Machine Learning; Performance Efficiency; Performance Improvement
Dell'Acqua, Fabrizio, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani. "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality." Harvard Business School Working Paper, No. 24-013, September 2023.
- 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
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).
- 11 Oct 2024
- Research & Ideas
How AI Could Ease the Refugee Crisis and Bring New Talent to Businesses
says. “What we’re asking is, can we build algorithms that will help find better matches that will allow people to integrate more easily?” The paper presents data from Switzerland and the United States that showed promise in using View Details