marketresearch

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 dramatically 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 suggests a useful discipline for anyone working with AI-generated insight. Before accepting an answer simply because it arrives quickly and sounds convincing, ask three more questions:

  • What is this conclusion based on?
  • What evidence would make me question it?
  • What decision would change if it were true?

Those questions may matter more as AI gets better, not less.

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

Making research worth the investment

Most companies don’t ignore research on purpose. They invest in it with the hope that it will sharpen strategy and reduce uncertainty. Yet too often, research ends up as a report that sits on a shelf, while teams return to old habits or gut feel.

  • Back in the early 2000s, even a giant like Procter & Gamble was struggling with research not delivering the business results they expected. Their flagship Tide brand was a household name, but innovation success rates were low: only about 15% of new initiatives were meeting profit and revenue targets.

    P&G didn’t respond by simply commissioning more studies. They changed how they used research. They built what they called a “new-growth factory:” processes and teams designed not just to generate insights but to carry them through to product development, marketing, and strategy. They opened up to outside partners, created internal coaches to guide innovation projects, and developed toolkits to help different functions act on insights.

    The results were dramatic. Within about a decade, Tide’s revenues nearly doubled, helping its fabric and home-care division grow from roughly $12 billion to nearly $24 billion. In other words, they turned research into a driver of growth by investing in what happens after the data is collected.

Of course, not every company has the scale or resources of P&G. But the lesson applies far beyond consumer-goods giants: research produces the greatest returns when you budget for the “last mile,” when you tie findings to financial outcomes, and when you make insights easy for people across the company to use.

Here are three practices you can apply, no matter your company size:

  1. Budget for the “last mile”
    Most research budgets go toward data collection and analysis, with little left for embedding insights. That’s a missed opportunity. The last mile is where insights turn into action: facilitated workshops, action plans, internal roadshows, training sessions. Even setting aside 10% of your research budget for activation can make the difference between a report that sits on a shelf and insights that change strategy.

    Try this: For your next project, include a session where key stakeholders co-create an action plan from the findings. When people shape the plan themselves, they’re far more likely to follow through.
  2. Translate insights into financial implications
    Executives act when they see dollar signs. An insight like “customers are frustrated by onboarding” is interesting. But reframed as “if we reduce onboarding drop-off by 5%, we could gain $2 million in annual revenue,” it becomes actionable.

    Try this: For each major finding, sketch a quick “back-of-the-napkin” estimate of what acting on it could mean financially. It doesn’t have to be precise: directional numbers spark urgency and support.
  3. Design outputs for shareability
    The people who need to act on research often won’t read a 60-page report. But they will look at a one-page infographic, a short video, or a set of personas they can pin to their office wall. The more portable and shareable your insights are, the more they spread inside the organization.

    Try this: Ask yourself, “If someone only had 60 seconds with this finding, what would I want them to remember?” Build a summary or visual around that.

Research is an investment. But like P&G discovered, the return depends on how the findings are activated. Companies that budget for the last mile, tie insights to dollars, and make findings easy to share don’t just learn about their customers, they become more competitive.

Want to make sure your next research project leads to real impact? Let’s talk about how to get there. Contact me at info at bureauwest.com.

Posted on by Jay Zaltzman