Can We Replace a UX Researcher with AI?

At the UX360 conference earlier this year, someone made a comment that stuck with me.

They shared that after a researcher left their team, they chose not to replace them. Instead, they planned to 'use AI' to fill the gap.

I’ll admit,  it felt a bit brutal. But I also understood where they were coming from.

Budgets are under pressure. Teams are stretched. AI is everywhere, in every flavour, with promises of speed, scale, and cost reduction.

But it raised a deeper question for me:

Would we say the same if a designer or product manager were to leave? Would a Head of Design say, 'Let’s just use AI instead'?

Probably not. So why is this conversation happening around UX research?

Yes, AI can reduce cost — but at what cost?

There’s no doubt that automation can support research operations:

• Summarising interview transcripts

• Synthesising open-text survey data

• Drafting discussion guides or personas

• Translating qualitative insights into visual reports

These tools are helpful. We already use several across my team. They save time. They reduce repetitive tasks. They free up researchers to focus on what matters most.

Yes, AI can reduce costs,  and that’s important. But it’s not just about saving money. When used effectively, AI can make researchers more efficient. It can help us move faster, focus on higher-value analysis, and spend more time where our human skills matter most: understanding nuance, framing the right questions, interpreting ambiguity, and driving strategic conversations.

The goal shouldn’t be to replace researchers; and it should be to amplify their impact.

So, what can AI do for UX research?

That’s a question I’ve been sitting with a lot lately, not from a place of fear but from curiosity.

Let us embrace AI. We should experiment with it, learn what it’s good at, and integrate it where it makes sense.

But we also need to approach it with caution and responsibility.

Because beyond efficiency, there are bigger questions:

• How reliable are AI-generated insights?

• Who is accountable for decisions made with or by AI?

• What does “good” look like in human-AI collaboration?

• Where are the ethical guardrails?

• And most importantly, how does research help organisations integrate AI in a way that’s truly human-centred?

UXR still feels undervalued and misunderstood

I sometimes wonder if people understand what UX researchers do.

How we work. What we contribute. The depth of thinking, coordination, and rigour involved. And most importantly, what the ROI of UX research is.

As I’ve said for years, we are far more than “usability.” When UX research is done correctly and strategically orchestrated with a clear view of the business, it becomes something else entirely:

A powerhouse.

Why I wrote UX Research Powerhouse™

That’s precisely why I’ve written my next book, UX Research Powerhouse™: Foundations of UX Research Leadership — to be released this autumn.

It’s the first in a series of volumes designed to explore how to build, scale, and lead impactful UX research teams. It’s about going beyond usability testing and placing research at the centre of product and business strategy.

But as I was finishing the manuscript, I realised something critical was missing: a reflection on the role of AI.

So, I’ve added a bonus chapter: UX Research & AI: A Powerful Complementarity

It covers:

• What AI can and can’t do for research

• How research can help organisations adopt AI responsibly

• The evolving role of researchers

• The ethical and strategic implications of AI

• What leadership looks like in the age of automation

Register interest for the bonus chapter.

This new chapter will be released later this month as a downloadable PDF ahead of the full book launch.

If you’d like early access, you can register interest here. I’ll let you know as soon as it’s ready and how to access it.