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Artificial Intelligence in Enterprise

As the new decade unfolds, profound changes are occurring in the business world. The AI in Enterprise discussion series is adapting to address the new challenges facing enterprises pursuing artificial intelligence and machine learning solutions. Designed for executives and senior management at enterprise-level companies, these one-hour talks provide insights from leaders in the field into how enterprise firms are thinking critically and strategically about AI/ML integration.

 

APRIL 2021: "Experimenting with Emerging Technologies in Australia’s Biggest Bank"

with Jesse Arundell (Head of Emerging Technology, Commonwealth Bank)

Photo of Jesse Arundell

Wednesday, April 21, 2021

NEW TIME: 4:00pm - 5:00pm EDT

Apply to attend at https://bit.ly/ai-enterprise-arundell

 

In this session of AI in Enterprise, we will hear from Jesse Arundell to learn how Australia’s largest financial services institution is accelerating its adoption of new and emerging technologies (e.g. AI, RegTech, DLT, 5G, Quantum Computing) through active experimentation with its technology partners, investments in new deep technology start-ups, and the creation of new software and algorithms. He will share what his team does, how they work and what they are currently focused on.

Apply to attend via Zoom at https://bit.ly/ai-enterprise-arundell
 
 

"Different Strategies for Adopting AI in the Enterprise" with Allie Miller (Amazon, AWS)

 

Photo of Allie MillerWhether you build your own ML models or grab an off-the-shelf AI solution from a startup, your team can integrate machine learning into your business today. In the March session of AI in Enterprise, Allie K. Miller discussed five common strategies that enterprise decision-makers take, along with the decision tree of internal team resources, external team resources, skillsets, budgets, and delivery time. Allie has worked in three of the most critical areas of AI—conversational, computer vision, and data. She is the Global Head of Machine Learning Business Development for Startups and Venture Capital at Amazon (AWS).  Allie was named as AIconic’s 2019 "AI Innovator of the Year", LinkedIn Top Voice for Technology and AI 2019 and 2020, Award Magazine’s Top 50 Women in Tech and Top 100 Global Thought Leaders, ReadWrite’s Top 20 AI Speakers in the World, Data Salon’s Top 25 Data Science influencers in the world, and Neptune’s Top 20 AI Influencers. Prior to joining Amazon, Allie was Lead Product Manager at IBM Watson where she led a team of 30 engineers to build IBM's first-ever trained multi-modal AI system and built core artificial intelligence vision technology for start-ups to Fortune 50 clients for use cases as diverse as photo analysis, inspections, insurance, fraud detection, and media tracking. 
 
If you missed this insightful discussion, you can find the podcast and video recording on our Innovation Science Guide. 
 
 

Upcoming Sessions for 2021

Wednesday, May 12th
11:00am-12:00pm EDT
Global Artificial Intelligence Lead, PwC
Register: https://bit.ly/ai-enterprise-rao ​​​​​​​
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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. 

Photo of Jin Paik Jin H. Paik is the program director and senior researcher at the Laboratory for Innovation Science at Harvard (LISH). He has worked extensively on programs focused on data science, development and use of artificial intelligence, digital transformation in organizations, and the future of work. He is an advisor to the National Academies of Sciences, Engineering, and Medicine’s Committee on the Use of Inducement Prizes. He also oversees the development of open innovation projects at LISH through partnerships with the National Aeronautics and Space Agency (NASA), Harvard Medical School, federal government agencies, academic and research institutions, and industry leaders.

Doug LevinDoug Levin is XIR 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 Accuknox, Nirmata, StormForge, Prescient Devices, Stynt, Tolemi and Wabbi. He serves as a board director for DJ MicroLaminates, FiVerity and ReversingLabs. Levin 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. Levin received his bachelor’s degree from the University of North Carolina at Chapel Hill, earned an advanced degree in International Economics from the College d’Europe in Bruges, Belgium and a Clean Technology fellowship.

 

See also: Organization & Processes, Data Science & AI Development

Publications

Hannah Mayer. 7/2020. “AI in Enterprise: AI Product Management.” Edited by Jin H. Paik, Jenny Hoffman, and Steven Randazzo.Abstract
AI in Enterprise - AI Product Management (P Skomoroch).pdf

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. 10/2020. “Data Science is the New Accounting.” Edited by Jin H. Paik and Jenny Hoffman.Abstract
AI in Entreprise - Data is the New Accounting (R Magoulas)

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. 

 

Hannah Mayer. 9/2020. “AI in Enterprise: In Tech We Trust.. Maybe Too Much?” Edited by Jin H. Paik and Jenny Hoffman.Abstract
AI in Enterprise - In Tech We Trust - Maybe Too Much (L Sweeney)

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 H. Paik, Timothy DeStefano, and Jenny Hoffman. 8/2020. “From Craft to Commodity: The Evolution of AI in Pharma and Beyond”.Abstract
AI in Enterprise - From Craft to Commodity (R Olfati-Saber).pdf

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 H. Paik, Steven Randazzo, and Jenny Hoffman. 6/2020. “AI in the Enterprise: How Do I Get Started?”.Abstract
AI in Enterprise - How Do I Get Started (R May).pdf

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.