Data Science & AI Development

Hannah Mayer. 7/2020. “AI in Enterprise: AI Product Management.” Edited by Jin H. 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.

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?
Jaehan Cho, Timothy DeStefano, Hanhin Kim, Inchul Kim, and Jin Hyun Paik. 2/19/2022. “What's driving the diffusion of next-generation digital technologies?” Technovation. Publisher's VersionAbstract
The recent development and diffusion of next-generation digital technologies (NGDTs) such as artificial intelligence, the Internet of Things, big data, 3D printing, and so on are expected to have an immense impact on businesses, innovation, and society. While we know from extant research that a firm's R&D investment, intangible assets, and productivity are factors that influence technology use more generally, to date there is little known about the factors that determine how these emerging tools are used, and by who. Using Probit and OLS modeling on a survey of 12,579 South Korean firms in 2017, we conduct one of the first comprehensive examinations highlighting various firm characteristics that drive NGDT implementation. While much of the literature assesses the use of individual technologies, our research attempts to unveil the extent to which firms implement NGDTs in bundles. Our investigation shows that more than half of the firms that use NGDTs deployed multiple technologies simultaneously. One of the insightful complementarities identified in this research exists amongst technologies that generate, facilitate and demand large sums of data, including big data, IoT, cloud computing and AI. Such technologies also appear important for innovative tools such as 3D printing and robotics.
2021 Feb 17

AI in Enterprise Series: Mohamed El-Geish (Cisco)

11:00am to 12:00pm

For Cisco, AI has become an appealing technology in customer service, specifically, in the customer contact center. Contact centers handle large volumes of inbound and outbound interactions, and make interaction channels (voice, email, text messaging, social media, etc.) as efficient and optimized as possible. In this session of AI in Enterprise,  Mohamed El-Geish (Director of AI, Cisco) discussed with moderator Doug Levin (Executive-in-Residence at Harvard Business School) the role of AI in customer experience, including speech recognition, natural language processing, virtual assistants, forecasting, computer vision, etc. and their applications in the enterprise.

You can also access the podcast or video recording through our Innovation Science Guide. 


Single Cell Trajectory Contest

The Pinello Lab at Massachusetts General Hospital, LISH, and 25+ other single cell labs across the world collaborated to execute the Single Cell Trajectory contest. Single cell trajectory inference methods are essential tools in the analysis of cellular dynamic processes. In the field, there are over 60 methods used for prediction... Read more about Single Cell Trajectory Contest

Hannah Mayer. 10/2020. “Data Science is the New Accounting.” Edited by Jin H. Paik and Jenny Hoffman.Abstract

In the October session of the AI in Enterprise series, HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani and Roger Magoulas (Data Science Advisor) delved into O'Reilly's most recent survey of AI adoption in larger companies. The discussion explored 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. 


Read Hannah Mayer's recap of the event to learn more about what senior managers in enterprises need to know about AI - particularly, if they want to adopt at scale.