Panel: AI xx The Retail Value Chain

Liz is a transformational AI executive who builds enterprise AI capabilities from zero to scale—architecting the technical foundations, governance frameworks, and operating models that enable Fortune 500 companies to unlock measurable business value through artificial intelligence.

As Global Head of AI & Advanced Analytics at Under Armour, she launched the company's first AI division, delivering millions in margin improvements, deploying agentic systems powering autonomous operations, and scaling AI literacy across 800+ employees. Previously, at The Estée Lauder Companies, she established the first Global Tech Innovation Team, leading AI-driven transformation across 30+ luxury brands in partnership with Microsoft and training 500+ employees across 5 departments.

Her approach bridges boardroom strategy and technical execution: she translates complex AI capabilities into business outcomes, builds hub-and-spoke operating models that drive adoption at scale, and leads multidisciplinary teams of data scientists and engineers to deliver revenue growth and cost optimization through AI. She specializes in agentic AI systems, next-generation commerce innovation, and responsible AI governance.

Before AI leadership, Liz founded and exited two startups and worked as an Emmy-nominated journalist. She holds master's degrees in broadcast journalism (Columbia University) and blockchain & AI (University of Nicosia), and is passionate about responsible AI deployment and building diverse, high-performing technical teams.

Lisa Hankin is a Managing Director at Accenture, based in New York, where she partners with leading Retailers to drive transformation across the enterprise.  Lisa has deep experience supporting large, complex organizations, advising on topics spanning merchandising and assortment strategy, inventory and supply chain planning, and the application of advanced analytics and AI to retail operations. She is known for translating sophisticated concepts into practical, scalable solutions that deliver tangible business outcomes.  Through her work, Lisa brings a pragmatic, collaborative approach to helping retailers modernize processes and technology, improve agility, and unlock value across the end-to-end retail lifecycle.

Melissa Feeney is a NYC-based data scientist with nine years of professional experience in the retail industry. She has previously worked at PVH Corp, the parent company of Calvin Klein and Tommy Hilfiger, where she focused on customer data, including analyzing purchasing behavior, using machine learning to predict future customer behavior, and applying natural language processing (NLP) to better understand text data. She earned her bachelor’s degree in Business from Lehigh University and her master’s degree in Quantitative Methods and Data Science from Columbia University. Her first exposure to data science came through a course at Lehigh in which she mined ecommerce customer product reviews, sparking a lasting interest in using mathematical and statistical techniques to extract meaningful insights from text at scale. In her work, she strives to understand the who, what, when, where, why, and how of a brand’s customer base.

As a graduate student at Columbia, her thesis research examined the architectures of large language models from Google, Meta, and OpenAI, comparing their performance in aspect-based sentiment analysis across customer reviews from two distinct domains: restaurants and branded underwear. Using transfer learning, she evaluated how effectively these models could analyze text despite differences in vocabulary and expression, and found that they demonstrated strong domain-agnostic understanding.