Future-Proofing AI-Powered Market Research: Ensuring Authenticity with Human Insights in a World of Synthetic Data

Navigating the Impact of Synthetic Data on Insights in an AI-Powered World

Good data start and end with good participants. It’s something market researchers say often, but it can’t be overstated—bad participants equal bad data, and that leads to bad decisions.

Lately, though, there’s a new kind of challenge keeping us up at night. It’s not just inattentive respondents or outdated panel lists. It’s AI and how it is used in market research and analysis.

That might sound like an overstatement—but it’s not. What we’re seeing now goes beyond a few rogue bots slipping through the cracks. We’re witnessing a fundamental shift in how people (and machines) are engaging with research, and our industry is already feeling the impact of AI on insight generation.

The implications of AI on data quality, insight accuracy, and ultimately business decision making are significant—and growing. This isn’t a future problem. It’s happening now. At Escalent Group (Escalent, C Space and Hall & Partners), we’re taking a three-part approach to ensure clean, human-led, high-quality data.

Spotting Warning Signs of Synthetic Responses in Research

At Escalent, we’ve always invested heavily in vetting our respondents. From API-level security and identity verification to behavioral tracking and post-survey synthetic data filtering, we’re constantly working to ensure data quality. But the rise of AI-generated responses is making that work more complex.

Recently, we were reviewing open-ended responses from a B2B tech study. On the surface, the answers looked great—concise, well-structured, grammatically flawless. But they felt oddly uniform. A quick investigation revealed what we suspected: they weren’t written by the respondents themselves. They were AI-generated.

Here’s an example of the kind of response we’re seeing more often:

“Improve response speed and ensure timely problem resolution for users by enhancing customer service interactions.”

Technically sound. Business-appropriate. And completely lacking personality or context. Responses like this are polished enough to pass traditional quality checks—and that’s exactly what makes them so dangerous. They sound like high-quality human data, but they’re not coming from the minds of our target audiences. They’re synthetic responses. And they’re slipping through unless you know what to look for and have the right research strategies in place to weed out synthetic respondents.

Synthetic Responses Aren’t Just a Market Research Issue: The AI-Powered Data Challenge We Face Across Industries

Market research isn’t the only industry facing a synthetic data problem. Educators are struggling to detect AI-written essays. As per an AI study published in June of 2024, experienced teachers could correctly identify only 37.8% of AI-written texts culminating in cheating.

Financial institutions are seeing a spike in identity fraud thanks to AI-generated deepfakes.

E-commerce platforms are battling fake reviews that erode consumer trust. Even in creative fields like journalism and literature, AI-generated content is increasingly hard to distinguish from human work, and often just as well-received. In a Bynder survey, only 50% of UK and US consumers could identify AI-written copy.

For market researchers, this presents an existential challenge. If we can’t confidently say that our data comes from real people, then the strategic insights we deliver—and the decisions based on them—become unreliable. And that’s a problem none of us can afford to ignore.

Escalent’s Proactive Approach to Ensure Data Quality—and What Comes Next in AI-Powered Market Research

At Escalent, we’re treating the rise of synthetic responses like the urgent issue it is in market research. It’s not something we can solve once and walk away—we need to constantly evolve.

We start by working only with premium sample providers to bring an added layer of human verification to the table. And in some cases, we’re even reintroducing CATI (computer-assisted telephone interviewing)—yes, phone calls—to validate hard-to-reach audiences.

Through our business unit, C Space, we build and manage online insight communities that allow us to engage with niche audiences regularly. These aren’t one-and-done surveys—they’re ongoing relationships between brands and their customers who act as strategic consultants. That persistence gives us the ability to vet participants over time and get to know them as real people, not just data points.

“Insight communities are powerful because they help get past the noise, allowing us to get closer to what our customers would say to their friends, not just what they think they should say.” —Tiphany Yokas, Senior Director, Innovation & Strategy Insights, Mondelez International

We’re also investing in tools that help us stay one step ahead to flag suspicious digital fingerprints, catch blacklisted email addresses, verify respondent geolocation and track copy-paste behavior and unusual patterns in timing.

No single tool can catch all AI-generated content but combining multiple strategies and strengthening AI-human collaboration significantly improves our ability to uphold the quality of research and insights.

In an article from WIRED, Edward Tian, creator of GPTZero, highlights the escalating “arms race” between AI and detection methods and advocates for a multilayered defense—including cryptographic tags —to better verify and augment human authorship.

How Brands Can Source, Validate and Protect Data in Market Research

If your organization relies on research to guide decisions, now is the time to take a closer look at how that data is being sourced, validated, and protected. The influence of AI-generated responses isn’t a distant concern—it’s already here, and it’s quietly undermining the reliability of insights across industries for those who aren’t taking the steps to proactively address it.

At Escalent Group, we’re staying ahead of the curve by combining premium sample partnerships, innovative market research practices, and cutting-edge fraud detection tools to ensure the integrity of every dataset we deliver. Whether you’re running a global brand tracker, testing new product concepts, or exploring new markets, clean, human-led, high-quality data should never be a question mark.

If you’re concerned about AI impacting the quality of your data—or just want to make sure you’re future-proofing your insights—we’re here to help. Let’s connect and talk about how we can strengthen your research foundation and ensure you continue to get the truth behind the numbers.

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