How to approach AI in UX research workflows

UX Researchers used to spend lots of time sifting through large amounts of data, coding them, and extracting meaningful insights from large research studies to translate to meaningful UX design ideas. What took a whole week can now be automated in a few clicks. UX directors from Hyatt and JP Morgan who explored the shifting role of UX Researchers in the age in a talk with Smart Design, highlighted the fact that while AI is now supporting us in research synthesis, we should still attend to it with a risk-averse mindset.

Their take on ai influencing research workflows include these takeaways:

⌛ Efficiency vs Time: The euphoric moment of uncovering insights is still a human delight for researchers (L2, L3 insights). There is importance and hope in ai not taking that away.

✳️ Low Risk x High Complexity = Can be automated. This framework that is used at JP Morgan can help researchers become better and faster.

🪵 For emerging researchers, going into 2026, it will be key to: Practice storytelling, learn the language of the business, and be heavily invested in the craft: Digging deep into the insights provided by ai and taking them to a more nuanced level.

🚪 About widening the access to data across departments, researchers have to remain the gatekeepers. Cognitive bias may show up more in other roles. But researchers are aware and skilled at getting to interesting data while staying neutral.

🦹‍♂️ Researchers should be careful not to adopt the stories of synthetic (users. The assumption that they would think like humans is not right. It can be a huge risk even if just 10% of the issues are not found.

Reference: https://smartdesignworldwide.com/ideas/defining-user-research-in-the-age-of-llms/