Data Science & AI Development

The Laboratory for Innovation Science at Harvard (LISH) is conducting a number of different crowdsourcing challenges and field research aimed at using the tools of data science and artificial intelligence to provide innovative solutions. LISH is working to help its partner organizations understand the value of data collection and the power of data analysis to drive problem solving. Browse LISH’s Data Science & Artificial Intelligence projects and papers below.

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. 

Marco Iansiti and Karim R. Lakhani. 3/3/2020. “From Disruption to Collision: The New Competitive Dynamics.” MIT Sloan Management Review.Abstract
In the age of AI, traditional businesses across the economy are being attacked by highly scalable data-driven companies whose operating models leverage network effects to deliver value.
Roberto Verganti, Luca Vendraminelli, and Marco Iansiti. 3/19/2020. “Innovation and Design in the Age of Artificial Intelligence”. Publisher's VersionAbstract

At the heart of any innovation process lies a fundamental practice: the way people create ideas and solve problems. This “decision making” side of innovation is what scholars and practitioners refer to as “design”. Decisions in innovation processes have so far been taken by humans. What happens when they can be substituted by machines? Artificial Intelligence (AI) brings data and algorithms to the core of innovation processes. What are the implications of this diffusion of AI for our understanding of design and innovation? Is AI just another digital technology that, akin to many others, will not significantly question what we know about design? Or will it create transformations in design that current theoretical frameworks cannot capture?

This article proposes a framework for understanding design and innovation in the age of AI. We discuss the implications for design and innovation theory. Specifically, we observe that, as creative problem solving is significantly conducted by algorithms, human design increasingly becomes an activity of sense making, i.e. understanding which problems should or could be addressed. This shift in focus calls for new theories and brings design closer to leadership, which is, inherently, an activity of sense making.

Our insights are derived from and illustrated with two cases at the frontier of AI ‐‐ Netflix and AirBnB (complemented with analyses in Microsoft and Tesla) ‐‐, which point to two directions for the evolution of design and innovation in firms. First, AI enables an organization to overcome many past limitations of human‐intensive design processes, by improving the scalability of the process, broadening its scope across traditional boundaries, and enhancing its ability to learn and adapt on the fly. Second, and maybe more surprising, while removing these limitations, AI also appears to deeply enact several popular design principles. AI thus reinforces the principles of Design Thinking, namely: being people‐centered, abductive, and iterative. In fact, AI enables the creation of solutions that are more highly user‐centered than human‐based approaches (i.e., to an extreme level of granularity, designed for every single person); that are potentially more creative; and that are continuously updated through learning iterations across the entire product life cycle.

In sum, while AI does not undermine the basic principles of design, it profoundly changes the practice of design. Problem solving tasks, traditionally carried out by designers, are now automated into learning loops that operate without limitations of volume and speed. The algorithms embedded in these loops think in a radically different way than a designer who handles complex problems holistically with a systemic perspective. Algorithms instead handle complexity through very simple tasks, which are iterated continuously. This article discusses the implications of these insights for design and innovation management scholars and practitioners.

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