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Annual AI & Analytics Online Symposium – Mind Meets Machines: Insight and Artificial Intelligence Converge
At the heart of the Center for Technology Innovation’s AI and Analytics Symposium is a fundamental shift: as these technologies become integral to decision-making across industries, the question is no longer if we’ll collaborate with machines—but how. This event explores that evolving collaboration: how data, algorithms, and human judgment come together to drive innovation, shape policy, and influence business strategy. Through keynote talks, cutting-edge research, and a thought-provoking industry panel, we’ll examine the practical realities, challenges, and opportunities of working at the intersection of human and machine intelligence.
INDUSTRY KEYNOTE
Agentic AI and IT’s Use Cases
9:00am – 9:55am, followed by a 5 minute break
Balamurugan Balakreshnan
Chief Architect WMW Microsoft
Microsoft Agentic AI represents a transformative approach to artificial intelligence, integrating autonomous agents with human ambition to drive business innovation and productivity. These AI-powered agents, such as Microsoft 365 Copilot, Azure AI Foundry Agents, are designed to automate routine tasks, enhance decision-making, and streamline workflows across various industries.
ACADEMIC KEYNOTE
Incentivizing The Content Creator When Tasks Can Be Delegated to Artificial Intelligence
10:00am – 10:50am, followed by a 10 minute break
DJ Wu
Ernest Scheller Jr. Chair in Innovation, Entrepreneurship and Commercialization, Georgia Tech
We develop a game-theoretic model to examine how platforms should adjust revenue-sharing to incentivize human creators in the presence of AI tools. We show that while AI can aid content creation, excessively strong AI may cause creators to shirk effort, ultimately harming the platform and reducing social welfare.
RESEARCH INSIGHTS
Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina
11:00am – 11:50am, followed by a 10 minute break
Dokyun Lee
Kelli Questrom Associate Professor in Information Systems, Boston University
Recent studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse. This has led many to propose that LLMs can be used as surrogates or simulations for humans in social science research. However, LLMs differ fundamentally from humans, relying on probabilistic patterns, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Nearly all advanced approaches fail to replicate human behavior distributions across many models. Causes of failure are diverse and unpredictable, relating to input language, roles, and safeguarding. These results advise caution when using LLMs to study human behavior or as surrogates or simulations.
REALITY CHECK: WHERE AI MEETS BUSINESS
12:00pm – 12:45pm
Sandeep Gangarapu, Senior Applied Scientist, Apple
Timothy Johnson, Demand Planning Manager, Charter Steel
Waqar Hasan, CEO, InComm Benefits
Moderator: Scott Schanke, Director, Center for Technology Innovation & Assistant Professor, University of Wisconsin-Milwaukee