Michael Wu
Michael Wu
Chief AI Strategist
PROS
Dr. Michael Wu is one of the world’s premier authorities on artificial intelligence (AI), machine learning (ML), data science, and behavioral economics. He’s currently the Chief AI Strategist at PROS (NYSE: PRO), an AI-powered SaaS provider that helps companies monetize more efficiently in the digital economy. He’s been appointed as a Senior Research Fellow at the École des Ponts Business School for his work in data science, and he serves as an advisor and a lecturer for UC Berkeley Extension’s AI programs.
Prior to PROS, Michael was the chief scientist at Lithium for a decade, where he focuses on developing predictive and prescriptive algorithms to extract insights from social media big data. His research spans many areas, including customer experience, CRM, online influence, gamification, digital transformation, AI, and more. His R&D earned him the recognition as an Influential Leader by CRM Magazine along with Mark Zuckerberg, Marc Benioff, and other industry giants.
Michael has served as a DOE fellow at the Los Alamos National Laboratory conducting research in face recognition, and was awarded four years of full fellowship under the Computational Science Graduate Fellowship. Prior to industry, Michael received his triple major undergraduate degree in applied math, physics, and molecular & cell biology; and his PhD from UC Berkeley’s biophysics program, where he used machine learning to model visual processing within the human brain. Michael believes in knowledge dissemination, and speaks internationally at universities, conferences, and enterprises. His insights have inspired many global enterprises and are made accessible through “The Science of Social,” and “The Science of Social 2”—two easy-reading e-books.
Executive Insights
Converging Worlds: AI Pricing Innovations in the Post-Pandemic Era
The nuances of the airline vs. B2B world have always called for highly specialized and disparate approaches to pricing. However, as market volatility grew (especially in the post-pandemic world), many assumptions and conditions that drove the specialization of these respective pricing practices are no longer valid. Today, airline revenue managers must forecast demand far into the future using a much shorter set of relevant histories. On the flip side, B2B pricing analysts must now deal with supply shortages and volatile shipping costs due to the disrupted supply chain.
These challenges have led to a series of AI-powered innovations where airline RM and B2B pricing can learn a lot from each other. Thus, we are seeing a rare occurrence of technological convergence, where airline revenue management (RM) and B2B pricing are converging! We will discuss some of the crucial machine learning advancements that made these AI solutions possible. However, because these AI tools are highly disruptive to the ways that pricing analysts work, their successful deployment requires trust and adoption. We will present a tried and tested 3-phase AI adoption strategy that can greatly improve your odds of success in rolling out AI-based tools in an enterprise.
