What's the big deal about Big Qual?

Ruth Behr
May 8
3 min read

AI platforms are reshaping the research landscape. But let’s call it what it really is.

A few months ago, I had the opportunity to present at The Quirk’s Event in Los Angeles, one of the biggest gatherings in the market research world. As always, it was a chance to reconnect with peers, swap stories, and get a pulse on where the industry is headed.

One conversation dominated: the explosion of AI-powered platforms promising to scale, speed up, and streamline qualitative research. From Outset.ai to Listen Labs to aytm and beyond, these tools were everywhere; each touting a faster, more innovative way to uncover human insight.

But as I sat through demo after demo, something became clear: we’re not all speaking the same language when we say “qual.”

And that matters.

Is “AI Big Qual” Really Qual? Or Something Else?

This isn’t just a theoretical debate. I’ve spent nearly 15 years at Talk Shoppe as a qualitative researcher, and my background spans both qualitative and quantitative research. But qual is where I’ve built my career; asking the hard questions, listening closely, and following emotional threads wherever they lead.

So when AI tools claim to replicate or replace that depth of interaction, it strikes a nerve.

Yes, these platforms are doing impressive things:

Speed & Efficiency: AI can process thousands of open-ended questions in minutes, surfacing patterns far faster than a human team.
Scalability: Large-scale “qual” is now logistically feasible in a way that wasn’t before.
Authenticity Checks: Tools that collect video or audio responses help ensure we're hearing from real people and not bots or bad actors, and can surface tone and emotion better than text alone.

Still, calling this “Big Qual” doesn’t quite fit.

What These Platforms Are Really Doing

Rather than think of AI tools as either “scaled qual” or “quant with more open-ends,” we believe they represent something new: a hybrid space that blends the breadth of quant with some of the depth of qual.

It’s not a replacement for either; it’s an expansion of the research toolkit.

For example:

Pattern Recognition vs. Deep Understanding: AI can identify that 64% of respondents are excited and 32% are concerned, but it can’t unpack why with the same nuance a human can.
Data Abundance ≠ Insight Quality: These tools generate structured output quickly, but research has never been about volume alone. Interpretation is where insight lives. How relevant is a sentiment analysis ever been on its own?
Probing Isn’t Moderating: AI can ask follow-ups, but it doesn’t replace the human skill of sensing when to push, when to pause, or when something deeper is just beneath the surface.

Can AI spot sarcasm? Catch emotional hesitation? Read subtext? Not yet.

And that’s the point.

Where Do We Go From Here?

AI-powered tools aren’t replacing qual; they’re creating a new lane. And when used intentionally, they can offer real value:

AI as assistant, not a replacement: It can handle the heavy lifting of transcribing, tagging, and summarizing, so researchers can focus on interpreting meaning.
Scale with care: We can reach more people, but we still need to ask whether we get richer insight or faster answers.
Strategic pairing matters: The best research isn’t about picking sides. It’s about knowing when to use which tools, and why. AI might be the right choice, or it might not. A good research partner helps you figure that out.

Let’s Name the Value, Not Overstate It

In a recent project, a client needed to quickly scale the sentiment on tech attitudes. Traditional quant didn’t fit, and deep-dive qual would’ve been too narrow. So, we used an AI-moderated survey, not as a substitute, but as a different tool that allowed for broader reach while still capturing tone, attitude, and emotion.

That’s where AI shines: not in replicating what qual already does well, but in giving us a new option when timelines, budgets, or objectives call for it.

So, is AI enhancing qualitative research, or reshaping the research landscape altogether?

That’s the conversation worth having.

What’s your take? Let’s talk.