Artificial Intelligence in Enterprise

 

In Tech We Trust... Maybe Too Much?

 

Without thoughtful foresight and rigorous testing, algorithmic bias can impact society in unexpected ways. In a new working paper, "In Tech We Trust... Maybe Too Much?", Hannah Mayer shares valuable insight from our most recent AI in Enterprise discussion with Latanya Sweeney (Professor of the Practice of Government and Technology, Harvard University) on what senior enterprise leaders can do to avoid negative externalities.

You can also check out the podcast or video of this event - and others - through our Innovation Science Guide.

 

Coming up: "AI Adoption in the Enterprise 2020" with Roger Magoulas

 

Wednesday, 14 Oct. 2020 - 11am-12pm EDT 

 

Apply to attend here: https://bit.ly/ai-enterprise-magoulas/Roger Magoulas Image

Delving into O'Reilly's most recent survey of AI adoption, the next session of our AI in Enterprise series will explore common risk factors, techniques, tools, as well as the data governance and data conditioning that large companies are using to build and scale their AI practices.

Roger Magoulas has worked on data analytics projects since the mid-90’s, designing and implementing the data infrastructure and analytics platform for a number of organizations, including Sybase, the SF Opera, Alberta Motor Club, and Columbia Pictures. At O’Reilly Roger built up the data team to support both internal analytics and tracking technology-adoption trends for O’Reilly’s Radar content. He also served as co-chair of O’Reilly's Strata conference, ran O’Reilly’s Foo Camp (unconferences) program, provided technology research to various companies and government agencies, and took over the Radar function, most recently writing the AI Adoption in the Enterprise 2020 survey report.

Roger currently volunteers for the state of California’s COVID Insights task force, providing guidance to the governor’s office, assists on Public Resource's Big Box of Science Project, produces webinars for those running data teams (CDx Connection), and advises a few startups.

 

 

Upcoming Sessions

 
Eric Newcomer ImagePeter Decrem Image

 

18 Nov. 2020 - 11am-12pm EDT

Eric Newcomer – Global Head of Security Architecture and Strategy for the Consumer Bank Division, Citi 

Peter Decrem – Director, Rates Trading Group, Citi

Apply to attend here: https://bit.ly/ai-enterprise-citi

Caroline Sherman headshot

10 Dec. 2020 - 11am-12pm EDT

Caroline Sherman – Chief Product Officer & Managing Director, Strategy at Quantopian

Apply to attend here: https://bit.ly/ai-enterprise-sherman

 

The AI in Enterprise Series

 
Enterprise firms across the globe are increasingly turning to AI-driven technologies to achieve key business goals. While potential benefits are significant, many firms underestimate the fundamental change necessary to successfully integrate AI into the enterprise. Successful adoption programs need to be developed to fit the particular needs of each organization—from its data strategy, project management, and product development to its engagement with the cloud, customers, and partners.
In November 2019, the Laboratory for Innovation Science (LISH), HBS Digital Initiative, and the Harvard School of Engineering and Applied Sciences (SEAS) launched AI in Enterprise,  an invitation-only series for selected executives to learn how to manage expectations and assimilate the knowledge and tools they need to implement a successful transition to AI in larger, more established organizations.

In contrast to other discussions on AI, this series aims to provide key insights on how enterprise firms are thinking critically and strategically about AI integration. Drawing on the LISH’s extensive experience over the past decade running programs that develop algorithm and AI-based solutions with partners across industries—as well as subject matter expertise from Harvard Business School faculty and partners—this series will provide a uniquely enterprise-focused forum for understanding artificial intelligence.

Watch highlights from the November 2019 event:

 

 

Moderators

 

Karim LakhaniKarim R. Lakhani is the founder and co-director of the Laboratory for Innovation Science at Harvard, the principal investigator of the NASA Tournament Laboratory at the Harvard Institute for Quantitative Social Science, and the faculty co-founder of the Harvard Business School Digital Initiative. He specializes in technology management and innovation. His research examines crowd-based innovation models and the digital transformation of companies and industries. 

Marco Iansiti ImageMarco Iansiti is an expert on digital innovation and transformation, with a special focus on strategy, business models, and new product development in high technology industries. His research studied innovation and operations in both firms and firm networks. He examined the strategy, business models, and innovation processes of a variety of organizations including Microsoft, Facebook, IBM, Hewlett Packard, Wal*Mart, AT&T, Dell, Amazon, and Google, among many others.

In their book, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Lakhani and Iansiti show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have constrained business growth for hundreds of years. 

Doug LevinDouglas A. Levin is Executive-in-Residence at the Laboratory for Innovation Sciences at Harvard and best known as the sole founder and first CEO of Black Duck Software. He is an advisor to an array of young companies in the cybersecurity and AI/machine learning segments, including Carbon Relay, Stynt, Tolemi, Wabbi, and ZyloTech.  He also is a frequent guest lecturer at the MIT Sloan School of Management and Harvard Business School where he speaks on a range of topics, including finance, technology and markets, and entrepreneurship. 

Publications

Hannah Mayer. Working Paper. “AI in Enterprise: AI Product Management.” Edited by Jin Paik, Jenny Hoffman, and Steven Randazzo.Abstract

While there are dispersed resources to learn more about artificial intelligence, there remains a need to cultivate a community of practitioners for cyclical exposure and knowledge sharing of best practices in the enterprise. That is why Laboratory for Innovation Science at Harvard launched the AI in the Enterprise series, which exposes managers and executives to interesting applications of AI and the decisions behind developing such tools. 

Moderated by HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani, the July virtual session featured Peter Skomoroch from DataWrangling and formerly at LinkedIn. Together, they discussed what differentiates AI product management from managing other tech products and how to adapt to the uncertainty in the AI product lifecycle.

Hannah Mayer. Working Paper. “AI in Enterprise: In Tech We Trust.. Maybe Too Much?Edited by Jin Hyun Paik and Jenny Hoffman.Abstract

While there are dispersed resources to learn more about artificial intelligence, there remains a need to cultivate a community of practitioners for cyclical exposure and knowledge sharing of best practices in the enterprise. That is why Laboratory for Innovation Science at Harvard launched the AI in the Enterprise series, which exposes managers and executives to interesting applications of AI and the decisions behind developing such tools. 

In the September session of the AI in Enterprise series, HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani spoke with Latanya Sweeney about algorithmic bias, data privacy, and the way forward for enterprises adopting AI. They explored how AI and ML can impact society in unexpected ways and what senior enterprise leaders can do to avoid negative externalities. Professor of the Practice of Government and Technology at the Harvard Kennedy School and in the Harvard Faculty of Arts and Sciences, director and founder of the Data Privacy Lab, and former Chief Technology Officer at the U.S. Federal Trade Commission, Latanya Sweeney pioneered the field known as data privacy and launched the emerging area known as algorithmic fairness.

Hannah Mayer, Jin Hyun Paik, Timothy DeStefano, and Jenny Hoffman. Working Paper. “From Craft to Commodity: The Evolution of AI in Pharma and Beyond”.Abstract

While there are dispersed resources to learn more about artificial intelligence, there remains a need to cultivate a community of practitioners for cyclical exposure and knowledge sharing of best practices in the enterprise. That is why Laboratory for Innovation Science at Harvard launched the AI in the Enterprise series, which exposes managers and executives to interesting applications of AI and the decisions behind developing such tools. 

Moderated by HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani, the August virtual session featured Reza Olfati-Saber, an experienced academic researcher currently managing teams of data scientists and life scientists across the globe for Sanofi. Together, they discussed the evolution of AI in life science experimentation and how it may become the determining factor for R&D success in pharma and other industries.

Jin Paik, Steven Randazzo, and Jenny Hoffman. Working Paper. “AI in the Enterprise: How Do I Get Started?”.Abstract

While there are dispersed resources to learn more about artificial intelligence, there remains a need to cultivate a community of practitioners for cyclical exposure and knowledge sharing of best practices in the enterprise. That is why Laboratory for Innovation Science at Harvard launched the AI in the Enterprise series, which exposes managers and executives to interesting applications of AI and the decisions behind developing such tools. 

Moderated by HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani, the most recent virtual session with over 240 attendees featured Rob May, General Partner at PJC, an early-stage venture capital firm, and founder of Inside AI, a premier source for information on AI, robotics and neurotechnology. Together, they discussed why we have seen a rise in interest in AI, what managers should consider when wading into the AI waters, and what steps they can take when it is time to do so.