Research Tidbits

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 Hearing the Customer is No Longer the Hard Part

    Why interpretation matters more as AI gets better at listening.

    For a long time, market research required a great deal of work simply to capture and make sense of what customers were saying – recruiting participants, conducting interviews and groups, transcribing conversations, coding responses and identifying patterns.

    AI is making much of that faster and easier. It can capture conversations, organize large amounts of customer input, identify recurring themes and summarize what people are saying – often remarkably quickly.

    Which raises the question: If AI can capture, organize and summarize customer input faster and more cheaply, how can we researchers still provide value to our clients?

    I think the answer lies in interpretation. Because knowing what customers said is not the same as understanding what it means.

    Interpretation can take many forms. It might mean recognizing that two seemingly contradictory findings reflect legitimate needs pulling customers in different directions. It might mean realizing that the reason customers give for a choice isn’t necessarily the factor actually distinguishing one option from another. Or it might mean seeing that a frequently mentioned issue is less important to the decision than a quieter finding that changes how we understand the whole situation.

    In each case, the value isn’t simply in identifying the pattern. It’s in putting forward an explanation of what the pattern means and why it matters. What needs, values or competing priorities might explain what we’re seeing? What is the underlying story? And, perhaps most important, does that interpretation help make a decision clearer?

    One simple way to make that jump is to treat an interpretation as a hypothesis rather than a conclusion. Instead of asking only “What did we hear?”, ask “What would explain why we heard this?” Then look back across the research for evidence that supports, complicates or contradicts that explanation.

    • For example, imagine customers repeatedly say that a company’s range of options feels overwhelming. The obvious conclusion might be that they want fewer choices. But perhaps, across the interviews, you also hear people worrying about missing an important feature, comparing options repeatedly, or wanting reassurance that they’ve made the right decision. That suggests a different interpretation: the problem may not be too much choice itself, but anxiety about choosing incorrectly.
    • That distinction matters because it leads to a different response. Instead of simply reducing the number of options, the company might make choosing feel safer through better guidance, recommendations or reassurance.

    A useful way to think about the process is:

    • What did we hear?
    • What might explain it?
    • What else in the research supports or challenges that explanation?
    • If we’re right, what should the client do differently?

    That final question is an important test. If the interpretation doesn’t change how we understand the situation or what the client might do, we may still be describing the findings rather than interpreting them.

    That’s also why interpretation is difficult to automate completely. AI can contribute, but interpretation requires judgment – deciding which patterns matter, testing possible explanations against the evidence, and taking a point of view about what the findings mean.

    In other words, the researcher’s role may be shifting from primarily bringing the voice of the customer into the room to helping the organization understand what that voice is actually telling them.

    I’ll be exploring that idea at the ESOMAR Congress 2026 in Valencia next week in a presentation called “Beyond the Voice of the Customer: The Rise of the Insight Interpreter.” It feels like an appropriate topic for a conference whose theme this year is Metamorphosis. Research is changing quickly. The opportunity is to make sure our role changes with it.

    Do you have customer input that’s easy to summarize but harder to interpret? I’d be happy to help. Contact me at info at bureauwest.com

    Posted on by Jay Zaltzman

    What customer contradictions can tell us

    Why the gap between what customers say and what they do can be more revealing than it first appears.

    Many people say that sustainability matters to them. They may worry about excess packaging, the environmental cost of shipping, and the larger consequences of a culture built around instant consumption. They may feel uneasy when a small item arrives in an oversized box, surrounded by cardboard, plastic, and air pillows.

    And yet many of those same people will choose next-day delivery when it is available.

    It is tempting to interpret that gap as evidence that sustainability does not really matter to them. After all, when customers are given a choice between a greener option and a faster one, the faster one often wins. But that doesn’t give us the whole story.

    • A stated value doesn’t have to determine every choice in order to be real. In many cases, it is competing with something else that is also meaningful to the customer. Next-day delivery is not simply about speed. It can make someone feel prepared, organized, or in control. It can reduce the worry that an item will not arrive in time. It can be especially appealing when life feels busy or unpredictable, or when a customer simply does not want one more thing to manage.

    The customer may care about sustainability. They may also care about the reassurance that comes from knowing an item is on its way and will arrive tomorrow. That is why it can be useful to look at the choice as a tension rather than a contradiction: between a longer-term value – reducing environmental impact – and an immediate need for certainty, convenience, or control.

    • Customers may manage that tension in different ways. One person may decide that one package will not make much difference. Another may tell themselves that the truck is already making deliveries in their neighborhood. Someone else may acknowledge the environmental cost but decide that, in this instance, speed matters more.

    For researchers, the point is not to excuse the choice or to conclude that what customers say is meaningless. It is to understand what the more sustainable option is asking them to give up.

    • That requires going beyond questions such as, “How important is sustainability to you?” We also need to ask: What would make you hesitate before choosing slower delivery? What would you lose by waiting? What would make the lower-impact option feel like a reasonable choice rather than a sacrifice?
    • Those questions can reveal whether the real issue is urgency, habit, cost, uncertainty, or a desire to feel in control. And that distinction matters for companies trying to encourage a different behavior.
    • If the barrier is uncertainty, clearer delivery windows and better tracking may matter more than an environmental message. If it is control, giving customers more options to schedule or redirect deliveries may help. If it is habit, a well-designed prompt at checkout may be enough to make people pause and reconsider.

    Understanding the tension does not guarantee that customers will choose the option a company hopes to encourage. But it gives companies a more useful place to start: identifying what customers would need to gain – or no longer feel they are giving up – for that option to become genuinely attractive.

    Let’s explore the tradeoffs behind your customers’ decisions and what they mean for your strategy. Contact me at info at bureauwest.com

    Posted on by Jay Zaltzman