AI and Machine Learning - Publications - Faculty & Research - Harvard Business School

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May 2025
Teaching Note

The VideaHealth AI Factory: CEO Florian Hillen on Speed, Scale, and Innovation

By: Tsedal Neeley

Teaching Note for HBS Case No. 425-720. Florian Hillen, co-founder and CEO of VideaHealth, a startup using artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. This AI factory,... View Details

Keywords: Diagnostics; Organization Design; Change Management; Disruption; Transformation; Health Care and Treatment; AI and Machine Learning; Technological Innovation; Technology Adoption; Management Style; Organizational Culture; Success; Technology Industry; Health Industry; United States

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

November–December 2023
Article

Keep Your AI Projects on Track

By: Iavor Bojinov

AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential... View Details

Keywords: Generative Models; AI and Machine Learning; Success; Failure; Product Development; Technology Adoption

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Article

Oracle Efficient Private Non-Convex Optimization

By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu

One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details

Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods

18 Apr 2000
Research & Ideas

Learning in Action

Bean, the U.S. Army's Center for Army Lessons Learned (CALL), AT&T's Bell Laboratories, the Timken Companies and General Electric's Change Acceleration Process (CAP). L.L. Bean has long relied on... View Details

Keywords: by David A. Garvin

Working Paper

Shifting Work Patterns with Generative AI

By: Eleanor W. Dillon, Sonia Jaffe, Nicole Immorlica and Christopher T. Stanton

We present evidence on how generative AI changes the work patterns of knowledge workers using data from a 6-month-long, cross-industry, randomized field experiment. Half of the 7,137 workers in the study received access to a generative AI tool integrated into the... View Details

Keywords: AI and Machine Learning; Behavior; Time Management

June 2024
Teaching Note

Beamery: Using Skills and AI to Modernize HR

By: Boris Groysberg, David Lane, Susan Pinckney and Alexis Lefort

Teaching Note for HBS Case No. 424-004. Unicorn human relationships startup Beamery evaluates it growth versus depth strategy as its strategic partners and customers could become future competitors in a quickly changing AI based human resources and talent management... View Details

Web

What an Army Commander Learned About Using AI to Combat Cyberattacks | Working Knowledge

because it was slower and potentially more dangerous than having AI on our side. The lessons we learned from this simulation certainly don’t apply only to the military.... View Details

2022
Working Paper

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju

As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details

Keywords: AI and Machine Learning; Analytics and Data Science; Mathematical Methods

April 2024
Article

Detecting Routines: Applications to Ridesharing CRM

By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman

Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details

Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation

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April 2017
Case

The Future of Patent Examination at the USPTO

By: Prithwiraj Choudhury, Tarun Khanna and Sarah Mehta

The U.S. Patent and Trademark Office (USPTO) is the federal government agency responsible for evaluating and granting patents and trademarks. In 2015, the USPTO employed approximately 8,000 patent examiners who granted nearly 300,000 patents to inventors. As of April... View Details

Keywords: Machine Learning; Telework; Collaborating With Unions; Human Resources; Recruitment; Retention; Intellectual Property; Copyright; Patents; Trademarks; Knowledge Sharing; Technology Adoption; Organizational Change and Adaptation; Performance Productivity; Performance Improvement; District of Columbia

November 2023 (Revised April 2024)
Case

Khanmigo: Revolutionizing Learning with GenAI

By: William A. Sahlman, Allison M. Ciechanover and Emily Grandjean

Already a leader in the edtech space since its 2008 launch, Khan Academy was now one of the first edtech organizations to embrace generative artificial intelligence ("genAI"). In March 2023, Khan Academy began beta testing Khanmigo, a genAI “guide” and tutor built with... View Details

Keywords: Technology Adoption; Leading Change; Entrepreneurship; Risk and Uncertainty; Education; AI and Machine Learning; Corporate Social Responsibility and Impact; Education Industry; Technology Industry; United States; San Francisco

18–19 Nov 2022
HBS Alumni Events

D^3 Catalyst: Building AI Companies

The Building AI Companies D^3 Catalyst Program is focused on the opportunities and challenges created by the digital transformation of our economy, and the emergence of digital networks and artificial intelligence (AI) as a foundation of the modern organization - both... View Details

June 2024
Case

Aidoc: Building a Hospital-Centric AI Platform

By: Ariel D. Stern and Susan Pinckney

In 2023, Israel-based AI health care company Aidoc evaluated its future. The company, founded in 2016, had grown from commercializing a single AI product for radiologists to a software platform that could detect 20 conditions and immediately notify care teams of... View Details

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

01 Jun 2024
News

Leveraging Generative AI

Four decades after HBS became the first business school in the country to require the use of personal computers in the MBA Program, the School is undergoing a different kind of technological transformation, one that leverages generative artificial intelligence (GenAI)... View Details

Keywords: Jennifer Gillespie

December 1, 2021
Article

Do You Know How Your Teams Get Work Done?

By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar

In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital... View Details

Keywords: Leading Teams; Work Recall Gap; Machine Learning; Algorithms; Groups and Teams; Management; Technological Innovation

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October 2021 (Revised September 2022)
Case

SmartOne: Building an AI Data Business

By: Karim R. Lakhani, Pippa Armerding, Gamze Yucaoglu and Fares Khrais

The case opens in August 2021, as Habib and Shahysta Hassim, husband and wife co-founders of the data labeling company SmartOne, contemplate the strategy of the high growth company. Between 2016 and 2021, SmartOne had kept doubling its size every two years and now,... View Details

Keywords: Artificial Intelligence; Data Labeling; Entrepreneurship; Strategy; Operations; Business Model; Growth Management; Growth and Development Strategy; AI and Machine Learning; Africa; Madagascar; Europe; France; United States

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

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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