Technology Commercialization

The Laboratory for Innovation Science at Harvard’s (LISH) work on technology commercialization consists primarily of two courses both aimed at teaching students how to bring ideas to market. In these courses, LISH allows students to work with real, existing technologies and work to build their ideas into market ready products or enterprises. Browse LISH’s Technology Commercialization course offerings below.


Karim R. Lakhani, Yael Grushka-Cockayne, Jin H. Paik, and Steven Randazzo. 10/2021. “Customer-Centric Design with Artificial Intelligence: Commonwealth Bank”. Publisher's VersionAbstract
As Commonwealth Bank (CommBank) CEO Matt Comyn delivered the full financial year results in August 2021 over videoconference, it took less than two minutes for him to make his first mention of the organization's Customer Engagement Engine (CEE), the AI-driven customer experience platform. With full cross-channel integration, CEE operated using 450 machine learning models that learned from a total of 157 billion data points. Against the backdrop of a once-in-a century global pandemic, CEE had helped the Group deliver a strong financial performance while also supporting customers with assistance packages designed in response to the coronavirus outbreak. Six years earlier, in 2015, financial services were embarking on a transformation driven by the increased availability and standardization of data and artificial intelligence (AI). Speed, access and price, once key differentiators for attracting and retaining customers, had been commoditized by AI, and new differentiators such as customization and enhanced interactions were expected. Seeking to create value for customers through an efficient, data-driven practice, CommBank leveraged existing channels of operations. Angus Sullivan, Group Executive of Retail Banking, remarked, "How do we, over thousands of interactions, try and generate the same outcomes as from a really in-depth, one-to-one conversation?" The leadership team began to make key investments in data and infrastructure. While some headway had been made, newly appointed Chief Data and Analytics Officer, Andrew McMullan, was brought in to catalyze the process and progress of the leadership's vision for a new customer experience. Success would depend on continued drive from leadership, buy-in from frontline staff, and a reliable team of passionate and knowledgeable data professionals. How did Comyn and McMullan bring their vision to life: to deliver better outcomes through a new approach to customer-centricity? How did they overcome internal resistance, data sharing barriers, and requirements for technical capabilities?
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.