Leading with AI: Building Trust and Powering Growth

Leading with AI: Building Trust and Powering Growth

Thursday, May 28 - Friday, May 29, 2026
Hopkins Bloomberg Center, Washington, D.C.

Register for Leading with AI (TBA)

We are in the midst of an economy-wide AI transformation—one that is reshaping industries, redefining work, and challenging leaders to rethink how their organizations create value.

Leading with AI: Building Trust and Powering Growth will explore this rapidly evolving landscape, offering frontier insights, new connections, and practical ideas for navigating change. Hosted by the Johns Hopkins University Technology and Society Initiative in Washington, D.C., this two-day executive summit brings together 300 business leaders, policymakers, and academics to examine how AI can drive innovation and growth responsibly.

Featuring leading academics and business executives, the agenda bridges research and practice at the frontier of AI leadership, addressing:

• The economic and organizational impact of AI
• Building trust in the age of AI
• Leading through an AI transformation
• Launching AI-based products

In an era defined by disruption, Leading with AI equips decision-makers across business, government, and academia to understand the evolving role of AI, leverage data and AI responsibly, and avoid the predictable missteps that derail transformation—helping them understand the current landscape and design trustworthy systems and strategies that will sustain innovation and growth.

Speaker Series Snapshot:

Dr. Michael Chui, McKinsey & Company
Dr. Michael Chui is a senior fellow at QuantumBlack. AI by McKinsey. He leads research on the impact of disruptive technologies and innovation on business, the economy, and society. Michael has led McKinsey research in such areas as artificial intelligence, robotics and automation, the future of work, data & analytics, collaboration technologies, the Internet of Things, and biological technologies. His PhD dissertation, entitled "I Still Haven't Found What I'm Looking For: Web Searching as Query Refinement," examined Web user search behaviors and the usability of Web search engines.

Dr. Beibei Li, Carnegie Mellon University, Heinz College
Beibei Li is the Professor of IT and Management. She received her PhD with distinction from Leonard N. Stern School of Business at NYU. Beibei’s research interests lie at the intersection between social and technical aspects of information technology. Specifically, the ubiquitous adoption and usage of mobile, web and sensor technologies today have completely changed the way individuals behave and make decisions. By looking into these digital footprints of individuals and their interactions with technologies, Beibei is interested in designing effective strategies for technology platforms and policy makers to improve technology design and economic welfare.

Duncan Gilchrist, Founder of Delphina
Duncan Gilchrist is cofounder of Delphina, where he’s building next-generation AI systems that help businesses turn data into smarter decisions. Before founding Delphina, he held multiple leadership roles at Uber, including Head of Ridesharing Pricing and Incentives Science and Director of Uber Eats Marketplace Science. Duncan earned a Ph.D. in Business Economics and an A.B. in Applied Mathematics from Harvard University, and his research has appeared in leading economics journals. He’s passionate about using AI to translate data into insight and to empower better, faster decision-making at scale.

Ye Tian, Founder and CEO of Theia Insights
Ye Tian is the Founder and CEO of Theia Insights, an AI company applying advances in machine learning and language technologies to real-world data challenges in the investment industry. Prior to founding Theia, Ye worked in natural language processing and artificial intelligence across leading universities, startups, and major technology companies. Driven by curiosity and a passion for building transformative technology, Ye is focused on developing AI tools that expand human understanding and support responsible innovation. Ye received a PhD in Computational Linguistics from UCL and a BSc in Economics and Business Management from Tsinghua University.

Jorge Tamayo, Harvard Business School
Professor Jorge Tamayo is an applied microeconomist primarily interested in industrial organization and development economics. His research focuses on theoretical modeling and structural estimation of firm decision-making and productivity. Professor Tamayo examines the market responses to settings in which firms use price discrimination (i.e. subscriptions, or membership fees) for goods and services. His research also focuses on the ways in which managers contribute to the productivity dynamics of their teams.