AI is making data access, model building, and analytical execution faster, easier, and more abundant. But for business and economics statisticians, the deeper challenge is not simply how to use AI tools—it is how to redesign analytics and engage more effectively in decision-making under uncertainty. Using forecasting as a practical lens, this webinar explores how AI is shifting the focus of analytics from model accuracy alone toward a decision-centered framework that integrates statistical methods, business context, human consensus, and actionable agility. Through real-world examples, we will discuss why imperfect data, changing environments, and uncertain futures require purpose-driven validation beyond empirical accuracy; how human judgment can remain central as AI accelerates technical work; and how educators and practitioners can prepare students and organizations to work with AI as both a learning partner and a thinking partner.
Speaker: Zhiwei Zhu, Ph.D., Clinical Associate Professor, Purdue University Daniels School of Business
Dr. Zhu is a Clinical Associate Professor at Purdue University’s Daniels School of Business and a former senior executive in global reinsurance. At Purdue, Zhu teaches business analytics and has helped lead the development of the school’s Business Analytics and Information Management program. His recent work includes research published in Harvard Data Science Review and his new textbook, Forecast by Design: Decision-Oriented Time Series Analytics in the Age of AI. A Fulbright U.S. Scholar and Poets&Quants Best Undergraduate Professor, Zhu brings together academic research and decades of business experience to help students, professionals, and organizations explore how human judgment and artificial intelligence can work together to create better decisions and lasting value.