Virtual try-on and AI recommendation tools are everywhere in beauty retail — and most of them still feel like gimmicks to the client holding the shade stick. Drawing on her work building SLAY Mirror (AR retail tech) and operating BeautyLynk® across on-demand beauty services, Rica Elysée cuts through the hype: which AI/AR patterns actually reduce returns and decision fatigue, which ones amplify shade bias or overpromise, and how to design for consent, lighting reality, and cultural accuracy — not just demo wow. This session is for retailers, brand teams, and beauty founders who want try-on that feels like service, not surveillance. Attendees leave with a practical filter for evaluating vendors, a trust checklist for in-store and e-comm experiences, and a founder lens on where human stylists still win. Based in Boston with a global lens spanning Boston, Tulsa, and Dakar, Rica frames beauty tech as care infrastructure — not just conversion software.

Takeaways:
1. Distinguish demo-ready AI/AR from retail-ready AI/AR using a trust-and-accuracy checklist.
2. Map where try-on reduces returns vs. where it increases regret, shade mismatch, or privacy friction.
3. Decide when to keep a licensed human in the loop — and how to brief that role so tech amplifies care.