WeTest.Athens 2026

Measuring UX impact has traditionally relied on stable interfaces and clearly defined success metrics. But what happens when the experience itself changes every day?
In AI-powered recommendation systems, content is dynamic, personalized, and continuously evolving. Traditional UX evaluation approaches -often centered around a single “North Star” metric- quickly fall short in capturing relevance, engagement, and real user value.
This talk presents a real-world case from an AI-driven product, exploring how UX evaluation shifts when recommendations become a moving target. It introduces a layered approach to UX measurement, moving from isolated KPIs to a connected “metric story” that reflects user discovery, engagement, and progression over time.
Attendees will learn how to rethink UX success in AI-driven environments, how to structure meaningful metric hierarchies, and how to evaluate recommendation experiences beyond conversion alone - grounded in practical decisions, trade-offs, and lessons learned from production systems.