WeTest.Athens 2026

UX research often does its job too well. Teams understand the findings, presentations are well received, and there is broad agreement on the conclusions. Then the roadmap is finalised and the research quietly disappears.


This talk explores why strong research so often fails to influence prioritisation and delivery, and what researchers can do differently to change that outcome. Based on real project experience, it looks beyond research methods to the organisational realities that shape decision making, including planning cycles, ownership boundaries, delivery pressure, and competing incentives.


The session focuses on how research can be designed and positioned to support difficult trade offs rather than ideal outcomes. It examines the importance of timing, stakeholder involvement, and how findings are framed when teams are making commitments, not just seeking understanding. Particular attention is given to the moments where research either becomes a reference point for decisions or is set aside as background context.


Attendees will leave with practical approaches for shaping research so that it remains visible and relevant when priorities are negotiated, budgets are constrained, and delivery plans are locked in. The aim is not to make research louder, but to make it harder to ignore.

AI tools are rapidly entering the UX research workflow, but not all tasks benefit equally, while some risk bias, false confidence, or degraded research quality if over-automated.

This hands-on workshop examines how AI can support different stages of the UX research process, from planning and recruitment to analysis and insight generation. Participants will learn where AI adds real value, where it may introduce risk, and what must remain human-led. The focus is not on specific tools or prompts, but on critical judgment, decision making and quality control. 

Attendees will leave with a clearer understanding of how to integrate AI into research work responsibly.

A quick-quide to how to create intelligent interfaces that user's actually like.

The great AI product interface regression is that we’ve returned to a command-line input style (prompting), and it’s the main reason for poor AI product experiences. It creates high cognitive load, often produces insufficient or flawed results, and leads to high frustration due to the lack of output control.
This session provides UX designers, product managers and Developers with practical human–AI design principles to create AI experiences that are not only functional but also user-centered, trustworthy, and engaging.