AI as a UX Research Partner: Friend, Foe, or Frenemy?

Exploring the messy, fascinating relationship between AI and human-centered design.

The First Time I Let AI Into My UX Process
I still remember the first time I used an AI tool in my UX research workflow. It was supposed to save me time just a simple transcription of a few user interviews. I hit “upload,” and in seconds, it returned neatly timestamped summaries, keyword tags, and even a sentiment breakdown. I was impressed. Maybe even a little smug.

But then I started reading the transcripts more closely. Something felt… off. The nuance was gone. A subtle chuckle that had signaled user hesitation? Labeled as “positive.” A pause before answering a sensitive question? Completely ignored.

That moment stuck with me. It was the first time I really asked myself: Is AI helping me become a better researcher or just a faster one?

AI is changing the way we work. For better and, sometimes, for worse. At its best, it can be a powerful sidekick, enhancing what we do instead of replacing it.

AI tools like Hotjar or UserTesting AI can transcribe hours of interviews, highlight recurring themes, and generate tidy summaries. What used to take me an entire afternoon now takes ten minutes freeing up time to actually think about the “why” behind the data.

I’ve worked on projects where predictive behavior models helped us understand friction points before a product was even launched. It was like peeking into the future. Platforms like Google’s PAIR gave us the ability to simulate user flows and pressure-test them early.

Small teams or startups with no dedicated researcher can now use AI-powered tools like Sprig or Lookback to run quick usability tests. It’s not perfect but it’s a start. Democratizing research is a win for everyone.

AI-driven sentiment analysis tools like MonkeyLearn or IBM Watson help process thousands of user comments across social platforms, reviews, or support tickets. Sometimes they surface issues users wouldn’t even mention in a formal interview.

But here’s the thing: for every time AI makes my work easier, there’s another moment where it threatens to flatten the richness of human experience.

I’ve seen tools misinterpret voices, miss nuance, or deliver recommendations that reflect the biases of their training data. One chatbot we tested worked beautifully with male users but struggled to interpret queries from women. That wasn’t the algorithm’s fault. It was the data.

Heatmaps and click data are helpful, but they don’t tell me why someone abandoned a page or what they were feeling. AI can tell me what happened. Only a human can uncover the so what.

When I rely too heavily on AI to do the thinking, I start to lose touch with my own intuition. I’ve caught myself trusting an algorithm’s recommendation over my gut and regretting it later. Optimization tools might chase clicks, but they can’t design for trust, meaning, or delight.

The more data AI needs, the more complicated things get. I’ve worked with teams that wanted to use AI-based eye-tracking in mobile apps without fully thinking through consent. Just because something’s possible doesn’t mean it’s ethical or even legal.

AI as a Frenemy:
Over time, I’ve learned that AI isn’t friend or foe, it’s something in between. A frenemy. It’s useful, but only if I approach it with clear boundaries and healthy skepticism.

I use it to augment — not automate — my research.
Now, I let AI flag potential issues in usability recordings, but I always review them myself. Tools like Dovetail are great for combining automated tagging with human insight. That’s where the real value lies.

Bias is sneaky. That’s why I’ve started adopting practices like using Adobe’s Ethical AI Toolkit or simply asking: “Who’s missing from this dataset?” The answers often reveal more than the data itself.

Ironically, some of the most exciting insights I’ve gotten from AI were the outliers. Like the time Spotify’s AI revealed a tiny group of users who listened to podcasts at 2x speed, an edge case that led to a now-standard feature.

I’m more transparent with users when AI is analyzing their data. I push for opt-outs. And I advocate for having a human reviewer, someone who can step back and ask, “Are we interpreting this responsibly?”

The most exciting future I see isn’t one where AI replaces researchers, but one where we work together. Like a creative partnership.

• Generative AI can sketch UI concepts: I might never have imagined these ideas on my own but it’s still up to me to know what users actually need.
• Emotion detection tools can flag reactions: They can capture real-time data during a test but I’m the one who interprets the story behind that reaction.
• AI Ethics Officers are becoming a thing: And honestly, it’s about time.

At the end of the day, AI can speed us up. It can surface trends. It can even surprise us.

But it can’t replace the human stuff the empathy, curiosity, and intuition that make UX research what it is. That’s our edge. That’s our responsibility.

So yes, I’ll keep using AI. But I’ll also keep asking hard questions, staying close to the humans behind the data, and reminding myself:
AI is a tool. Curiosity is the superpower. Ethics are the compass.