marketresearch

Qual at Scale: More Interviews or Better Insights?

How to get more value from AI-powered research without overlooking what matters most.

In conversations with clients at the recent TMRE conference, I was struck by how often “qual at scale” came up as the future of qualitative research.

It’s easy to understand the enthusiasm. AI now makes it possible to conduct hundreds or even thousands of conversational interviews quickly and affordably. That’s an exciting development.

But more interviews don’t necessarily mean better insights. The real value of qualitative research isn’t simply collecting people’s answers to open-ended questions. It’s understanding what lies behind those answers, exploring contradictions, challenging assumptions, and discovering things we didn’t even know to ask about.

AI can help with some of this. But when we have hundreds or thousands of interviews to analyze, it’s tempting to focus on the most frequently mentioned themes. And sometimes the most valuable insight comes from an unusual observation that challenges everything we thought we knew.

So how can you get the most value from qual at scale?

1. Start with the business problem, not the methodology. You may know what decision you’re trying to make, but not necessarily what you need to learn to make it. Work with your research partner to identify the questions worth exploring and determine which research approach will be most useful.

2. Make room for the unexpected. Whether interviews are conducted by AI or humans, make sure there’s room for meaningful follow-up questions and exploration. And don’t limit the analysis to counting themes. Pay attention to contradictions, unusual responses, and observations that might lead to an entirely new understanding.

3. Use qual at scale where it adds value. It’s particularly useful when you want to hear from a broad range of customers, explore experiences across multiple segments or markets, or identify recurring issues. But interviewing 1,000 people doesn’t necessarily tell you what most customers think. If those people weren’t selected in a way that reflects your customer population, the findings may give a misleading picture of how common particular attitudes really are.

4. Consider combining approaches. Sometimes a smaller number of intensive interviews can uncover issues worth exploring more broadly. Sometimes qual at scale can reveal questions that deserve deeper investigation. And when you need reliable estimates of how widespread an attitude or behavior is, use a quantitative research design that supports those conclusions.

Ultimately, the biggest opportunity AI offers isn’t simply the ability to collect more information. It’s the possibility of developing better understanding – provided we apply the right research design and thoughtful interpretation.

The goal isn’t to conduct more interviews. It’s to make better decisions.

Have a business question you’re trying to answer? Let’s figure out the best research approach together. Contact me at info at bureauwest.com.

Posted on by Jay Zaltzman

What Becomes Scarce When Insights Are Abundant?

As AI makes customer insights easier to generate, judgment becomes more valuable.

AI may be about to give marketers a new problem: not too little customer information, but too much.

I came away from Esomar Congress 2026 wondering whether we’re beginning to see the early signs of this. A recurring theme across many of the conversations and presentations was just how much easier AI is making it to work with customer information. Interviews that once required hours of manual review can be searched and synthesized quickly. Large numbers of open-ended responses can be analyzed in minutes. Customer reviews, call-center conversations, CRM records, social media, previous research and other sources can increasingly be brought together and interrogated at the same time.

But AI isn’t only making existing information easier to analyze. It is also making some kinds of customer information cheaper and easier to collect. Automated interviewing, adaptive surveys, rapid concept exploration, synthetic respondents and other emerging approaches can significantly reduce the time and cost required to investigate a question.

  • In other words: more customer evidence × easier collection × easier analysis × cheaper questioning = dramatically more plausible insights.

That made me think about an earlier technological shift. When personal computers became widespread, many people assumed they would simply allow organizations to do the same work with fewer people. But productivity improvements don’t always work that way. When something becomes dramatically easier or cheaper, organizations often start doing more of it.

Something similar could happen with research. Imagine a marketer has a relatively modest question about customers. Today, they might decide it isn’t worth spending $30,000 and six weeks to investigate. But what if an AI-enabled approach can provide a useful first answer in a few hours? Suddenly, that question gets asked. And then another one. And another.

Competitors begin doing the same thing. Eventually, being able to investigate questions that once would have been considered too small or too expensive may stop being an extraordinary capability and become table stakes.

So perhaps the biggest effect of AI on research won’t be that companies need fewer answers. It may be that they start asking vastly more questions.

And that creates a different problem. When getting an answer becomes easy, the harder questions become:

  • Which evidence should we trust?
  • Which findings actually matter?
  • And, ultimately, what should we do?

That is where I think the researcher’s role becomes more important, not less. AI can identify patterns, summarize evidence and generate plausible interpretations. But understanding what a finding really means often requires something different: context, experience, knowledge of the customer and the business, and the ability to recognize when the obvious interpretation is not necessarily the right one.

    Researchers can also connect findings that may look contradictory, distinguish an interesting observation from something strategically important, and help stakeholders understand what the evidence means for the decisions they actually have to make.

    In other words, the value may increasingly shift from producing answers to interpreting them – and helping organizations know what to do with them.

    For years, researchers have worried about information scarcity – not enough data, respondents, time or budget. AI may begin to reverse that equation. If customer information becomes abundant, the scarce resource may become judgment – deciding what deserves our attention, what deserves our trust, and what it means for the business.

    Need help deciding which customer inputs matter – and what they mean for your business? Contact me at info at bureauwest.com.

    Posted on by Jay Zaltzman

    When good research changes nothing

    The problem isn’t always the insight. It’s what the insight runs into once it’s shared.

    Sometimes market research does exactly what it’s supposed to do. The findings are clear, the pattern is real… and yet the client’s decision still doesn’t reflect what was learned.

    Most of us have seen this happen. And when it does, it’s easy to blame the usual things: Maybe the story wasn’t compelling enough. Maybe stakeholders interpreted it differently. Maybe the recommendations weren’t sharp enough.

    But thinking back on certain projects, I’ve started to notice a pattern.

    • In one study, I was asked to conduct qualitative research using a discussion guide built by pulling questions directly from a survey. Participants struggled, not because they had nothing to say, but because the questions didn’t match how they actually think about the topic.
    • In another, a new product concept resonated with customers, but the advertising had already been developed. None of the directions really worked, yet the team still had to pick one. What came out of the process wasn’t a strong decision so much as the “least bad” choice.
    • And in another project, participants kept raising the same issue on their own. It clearly mattered to them. But it was also something the organization wasn’t prepared to deal with. The finding made the room uncomfortable, and that discomfort told its own story.

    On the surface, these are very different situations. But they point to the same thing. By the time the findings are presented, a surprising amount has already been decided. The research has been framed a certain way. The questions have been narrowed. Sometimes there are topics no one wants to touch. And sometimes, more quietly, there are things the organization just isn’t ready to hear.

    One way I’ve started to think about it is that insights don’t just land in organizations – they run into things.

    Sometimes what they run into is a decision that’s already been made. Sometimes it’s an investment that’s too far along to revisit. And sometimes it’s something the organization isn’t ready to hear. In that context, the question becomes not just “is this insight right?” but “what is this insight going to run into?”

    I’ve found it can be useful to think about that ahead of time. What existing decisions, assumptions, or constraints might this challenge? Because that often shapes whether the insight has any real chance of being used.

    It has also changed how I think about the goal of the work. Early on, I probably would have said the goal is to get the insight exactly right. Over time, I’ve come to think the goal is to make the insight usable.

    And in some cases, that means using the insight to open a conversation rather than close one. Especially when it touches on something sensitive, presenting it as something to explore can create more movement than presenting it as a final answer.

    Some client-side researchers see value in bringing in an outside consultant for this reason. It’s not just about a fresh perspective. An external voice can say things that are harder to say internally, which can create space for the insight to be heard. (Or, at times, take the heat for it!)

    None of this guarantees that the insight will be used. But it does improve the odds. Because the real question isn’t just whether we found something important. It’s whether the work was set up in a way that gave that insight any real chance to matter. Let’s discuss the best way to make your insights land most effectively. Contact me at info at bureauwest.com.

    Posted on by Jay Zaltzman

    Customers don’t always want delight

    I recently completed a post-booking survey for Expedia. One of the questions asked whether I felt “amazed” or “delighted” by the experience.

    I paused. Not because the experience was bad – it wasn’t. It worked exactly as intended. I found a hotel, compared prices, booked it quickly, and moved on with my life.

    But that was the point. I wasn’t hoping to be amazed. I wasn’t seeking delight. I was in task mode. I just wanted the process to work efficiently. That question felt off not because it was poorly worded, but because it revealed a deeper misunderstanding about how people actually experience value.

    Many digital platforms – travel booking sites, airline apps, banking portals, insurance dashboards – are not emotional destinations. They are functional systems. Their primary job is to reduce effort, reduce uncertainty, and reduce time. When those systems work well, the dominant emotion isn’t delight. It’s relief. And relief is rarely measured.

    Instead, many companies reach for aspirational language – delight, wow, magic – and then build surveys to validate those ideas. The problem is that when you ask emotional questions of a functional task, you don’t get insight. You get noise. Worse, you risk steering teams toward the wrong priorities: polishing moments that don’t matter while overlooking friction that does.

    There’s a subtle but important difference between no emotion and no emotional work required. In many everyday buying contexts, people don’t want to be engaged – they want to be done. That’s not a failure of brand ambition; it’s an expression of buyer reality.

    This is something I see repeatedly in my research. Emotion still matters, but it’s not always where companies expect it to show up. Often, the strongest positive signal isn’t excitement or delight, but quiet confidence that the system will do what it promises without getting in the way.

    Sometimes, the best experience is the one you barely notice.

    This pattern – and others like it – is a core theme in my upcoming book, Mind of the American Buyer, which looks at how people actually make decisions in the real world, rather than how brands imagine they do.

    Curious where your customers are actually seeking delight – and where they just want things to work? You can reach me at info at bureauwest.com if you’d like to discuss the best way to understand that difference.

    Posted on by Jay Zaltzman