The rise of AI has been nothing short of revolutionary, and at its heart lies one of the most controversial and misunderstood innovations: synthetic data. To some, it’s a tool of limitless potential; to others, a slippery slope that threatens the authenticity of qualitative research.
As an anthropologist and qualitative researcher, my work has always centered around real human stories—their voices, their experiences, and their aspirations. I have spent years dissecting emotions, studying cultural contexts, and uncovering the deep, unspoken truths hidden in human narratives. So, when I first encountered the concept of synthetic data, I’ll admit—I bristled. Could AI-generated responses truly capture the complexity of human thought? Could an algorithm simulate the depth of insights that years of fieldwork, experience, and intuition have uncovered? I was skeptical, and rightfully so. But curiosity has always been my guiding force. Instead of dismissing it outright, I did what any researcher would do: I tested it. I interrogated it. I put it to work. Now, after actively using synthetic data in my work at Qualz.ai, I see its potential more clearly than ever. Synthetic data is not here to replace qualitative research—it’s here to push its boundaries, scale its impact, and complement human expertise in ways we never thought possible.
And I’ll be honest: it’s been a wild ride.
WHAT IS SYNTHETIC DATA?
Let’s get one thing straight: Synthetic data is not fake data. It’s not some auto-generated nonsense made up out of thin air. At its core, synthetic data is artificially generated information that mimics real-world patterns and behaviors. We train advanced AI models—often leveraging Generative Adversarial Networks (GANs) and Large Language Models (LLMs)—on real qualitative datasets. These models learn how humans communicate, how emotions manifest in text, and how sentiment shifts in response to different contexts. And then, using that knowledge, they generate new, synthetic responses that reflect the linguistic, behavioral patterns, and even experiences of actual human participants. At Qualz.ai, we started using synthetic data not because we were looking for a shiny AI experiment—but because we needed it.
When Synthetic Data Became Essential
There’s a moment in every researcher’s life when you realize you don’t have the time to recruit participants, don’t have the budget for large-scale qualitative studies, and don’t have access to the right audience—because they’re niche, high-profile, or just impossible to reach. We hit all of these roadblocks, over and over again. And then, we realized something: we could create AI-generated participants that modeled real-world respondents, allowing us to test research questions before launching full-scale studies.
One of our early breakthroughs came when we worked with a client who needed insights from professionals in an extremely specialized field. Finding these individuals, scheduling interviews, and conducting a full qualitative study would have taken months—if not longer. With synthetic data, we delivered preliminary insights in a matter of minutes. It wasn’t a substitute for the real thing, but it was a highly efficient, low-cost, and scalable way to test hypotheses, refine research questions, and prepare for real-world interviews.
I could feel my perspective shifting. This wasn’t about replacing qualitative research—this was about making it faster, smarter, and more strategic.
Eureka Moment!
I’ll never forget the moment it clicked. We were testing AI-driven interviews at Qualz.ai, running simulations of qualitative discussions using synthetic participants. I was reading through a set of AI-generated responses when I noticed something strange—a moment of hesitation. One of the AI respondents paused before answering a question, shifting tone midway through its response. It mirrored the kind of natural hesitation we see in real interviews—the internal conflict, the change in perspective, the act of thinking aloud. And that’s when I realized: this isn’t about AI replacing qualitative research. This is about AI learning to think more like humans—and helping us refine our research in ways we never thought possible.
The Benefits of Synthetic Data in Research
I went from skeptic to advocate not because synthetic data was perfect, but because it solved real problems:
It’s Fast—Really Fast: Traditional qualitative research moves at the speed of human logistics—which is to say, slow. Finding participants takes time. Scheduling interviews takes time. Transcribing and analyzing data? Even more time. With synthetic data, we can simulate conversations, test hypotheses, and iterate research designs in hours, not weeks.
It’s Scalable and Cost-Efficient: Let’s be honest: qualitative research is expensive. Recruiting participants, paying incentives, hiring interviewers—it adds up. With synthetic data, we eliminate recruitment costs, reduce early-stage research expenses, and optimize resources for real-world, human-led research.
It Helps Us Refine Research Before Engaging Real Participants: This is the big one. Synthetic data lets us test research questions before we even start recruitment. It allows us to fine-tune interview guides to focus on what really matters.
In addition, synthetic data enables scenario modeling, helping us anticipate how different consumer segments might respond. This means that when we do conduct real-world interviews, we’re more prepared, more efficient, and more focused on extracting deep insights.
The Truth: Synthetic Data Isn’t Perfect—And It Never Will Be
I won’t sugarcoat it: synthetic data has limitations. It lacks true emotional complexity. AI still struggles to capture sarcasm, hesitation, or deeply personal emotions in the way humans can. Synthetic data can reinforce bias and the quality or the accuracy of the data can be a problem as well. If the training data is biased, the AI-generated responses will be too—which is why human oversight is critical. I cannot stress this enough. Synthetic data will never replace real human research. But that’s not the point. The point is to use synthetic data as a tool, not a substitute.
The Future: AI + Human Researchers Collaboration
I started out doubting synthetic data. I ended up using it to make my research better. But the real future of synthetic data isn’t AI replacing humans—it’s AI working alongside humans to push qualitative research forward. AI for faster, scalable, hypothesis testing. Humans for depth, emotional intelligence, and context. Together, a new era of qualitative research—one that blends AI-driven efficiency with human intuition. And that’s a future I can get behind.
If You’re a Skeptic, Try It for Yourself!
I get it—I was a skeptic too. But we don’t advance research by standing still. We advance it by experimenting, testing, and pushing boundaries. So if you’re still doubting synthetic data, my advice is simple: Try it. See what it can do. Break it. Push its limits. Then decide for yourself.
Because the future of research isn’t AI versus humans OR choosing between AI and humans—it’s about leveraging both. My experience with synthetic data has been transformative. It has accelerated our research, expanded possibilities, and helped us tackle significant challenges in qualitative research. However, it has also reinforced an important lesson: while synthetic data is a powerful tool, it must be used thoughtfully and responsibly. Ethical considerations, transparency, and careful integration with real-world qualitative insights are crucial to ensuring its reliability and value.
The most effective research will always combine AI-driven efficiency with the unique depth of human intuition. As we continue exploring this technology, we must foster meaningful discussions—differentiating between augmentation and replacement, understanding how AI can address data scarcity and enhance insights, and carefully weighing its opportunities and risks. Only by engaging critically and responsibly with these questions can we fully harness the transformative power of not just Generative AI, but all emerging technologies shaping the future of qualitative research. And I, for one, am excited to see where this journey takes us.
Curious about how synthetic data can support your research? Let’s talk. Visit Qualz.ai to see how we’re making AI work for qualitative researchers.