Publications
Publications
Show Results For
- All HBS Web
(926)
- Faculty Publications (354)
- 2018
- Working Paper
Some Facts of High-Tech Patenting
By: Michael Webb, Nick Short, Nicholas Bloom and Josh Lerner
Patenting in software, cloud computing, and artificial intelligence has grown rapidly in recent years. Such patents are acquired primarily by large U.S. technology firms such as IBM, Microsoft, Google, and HP, as well as by Japanese multinationals such as Sony, Canon,... View Details
- April 2018 (Revised February 2019)
- Supplement
Improving Worker Safety in the Era of Machine Learning (B)
By: Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla and Matthew S. Johnson
Supplements the (A) case. View Details
- March 2018
- Case
IBM: Watson and the Internet of Things
By: Rajiv Lal and Scott Johnson
IBM has recently launched a business unit devoted to the Internet of Things. The group's leadership team needs to figure out the best way to quickly scale its business in a fragmented and nascent market. View Details
- February 2018
- Case
Vodafone: Managing Advanced Technologies and Artificial Intelligence
By: William R. Kerr and Emer Moloney
Vodafone was operating in the fast-moving telecommunications market where innovation and scale were key. Faced with an onslaught of technological advances—big data, automation, and artificial intelligence—CEO Vittorio Colao reflected on how he should change the... View Details
- February 2018
- Case
Amazon, Google, and Apple: Smart Speakers and the Battle for the Connected Home
By: Rajiv Lal and Scott Johnson
Amazon, Google, and Apple all offer their own smart speaker. The devices represent each firm's entry point into the connected home market. All three companies come into the space with their own strengths and weaknesses. Who will win? View Details
Keywords: Apple; Apple Inc.; Google; Amazon; Amazon.com; Google Home; Homepod; Echo; Smart Home; Connected Home; Voice; Artificial Intelligence; Machine Learning; Internet Of Things; Smart Speaker; Connected Speaker; Intelligent Assistants; Virtual Assistants; Voice Assistants; Alexa; Google Assistant; Siri; Technological Innovation; Disruptive Innovation; Competitive Strategy; Business Strategy; Adoption; Information Infrastructure; Information Technology; Internet and the Web; Mobile and Wireless Technology; Applications and Software; Technology Adoption; Digital Platforms; Household; AI and Machine Learning; Electronics Industry; Technology Industry; United States
- February 2018 (Revised March 2018)
- Case
Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)
By: Lauren Cohen, Christopher Malloy and William Powley
This case examines the intersection of two firms (Cogent Labs—a machine learning software firm in Tokyo; and Google, the technology infrastructure giant) attempting to exploit the benefits of artificial intelligence and machine learning in the financial services... View Details
- February 2018 (Revised October 2019)
- Case
HubSpot and Motion AI: Chatbot-Enabled CRM
By: Jill Avery and Thomas Steenburgh
HubSpot, an inbound marketing, sales, and customer relationship management (CRM) software provider, announced that it had acquired Motion AI, a software platform that enabled companies to easily build and deploy chatbots, fueled by artificial intelligence, to interact... View Details
Keywords: CRM; Sales Management; Customer Service; Artificial Intelligence; B2B Vs. B2C; Business Marketing; SaaS; Marketing; Marketing Strategy; Brands and Branding; Customer Focus and Relationships; Sales; Salesforce Management; Technological Innovation; Applications and Software; Customer Relationship Management; AI and Machine Learning; Technology Industry; Service Industry; United States; North America
- February 2018 (Revised June 2021)
- Case
New Constructs: Disrupting Fundamental Analysis with Robo-Analysts
By: Charles C.Y. Wang and Kyle Thomas
This case highlights the business challenges associated with a financial technology firm, New Constructs, that created a technology that can quickly parse complicated public firm financials to paint a clearer economic picture of firms, remove accounting distortions,... View Details
- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models... View Details
- January 2018 (Revised February 2023)
- Teaching Note
The Future of Patent Examination at the USPTO
This teaching note pairs with the case entitled: “The Future of Patent Examination at the USPTO” (case no. 617-027). View Details
- 2019
- Working Paper
Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles
By: Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna
We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural... View Details
- January 2018 (Revised March 2019)
- Case
Autonomous Vehicles: The Rubber Hits the Road...but When?
By: William Kerr, Allison Ciechanover, Jeff Huizinga and James Palano
The rise of autonomous vehicles has enormous implications for business and society. Despite the many headlines and significant investment in the technology by early 2019, it was still unclear when truly autonomous vehicles would be a commercial reality. Students will... View Details
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups.... View Details
- October 2017 (Revised April 2018)
- Case
Improving Worker Safety in the Era of Machine Learning (A)
By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I... View Details
- August 2017 (Revised July 2019)
- Case
GROW: Using Artificial Intelligence to Screen Human Intelligence
By: Ethan Bernstein, Paul McKinnon and Paul Yarabe
Over 10% of all 2017 university graduates in Japan used GROW, an artificial intelligence platform and mobile app developed by Tokyo-based people analytics startup IGS, to recruit for a job. This case puts participants in the shoes of IGS founder and CEO Masahiro... View Details
Keywords: Big Data; Artificial Intelligence; Talent and Talent Management; Recruitment; Selection and Staffing; Human Resources; Information Technology; AI and Machine Learning; Analytics and Data Science; Financial Services Industry; Air Transportation Industry; Advertising Industry; Manufacturing Industry; Technology Industry; Japan
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying... View Details
- June 2017
- Teaching Note
IBM Transforming, 2012–2016: Ginni Rometty Steers Watson
By: Rosabeth Moss Kanter and Jonathan Cohen
Ginni Rometty, who became IBM CEO in 2012, led efforts to transform the company around cognitive computing and the AI platform Watson. This Teaching Note helps instructors understand and teach the Harvard Business School case “IBM Transforming, 2012–2016: Ginni Rometty... View Details
- 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
- March 2017
- Supplement
Donna Dubinsky, Numenta and Artificial Intelligence
By: David B. Yoffie
Donna Dubinsky, CEO of Numenta, discusses her views of the future of artificial intelligence and the strategic challenges of building a new platform. View Details