The Human Behind the Machine: Why Synthetic Audiences Can’t Replace Authentic User Interviews

The allure of synthetic audiences is undeniable. Imagine a world where you could conduct a hundred “user interviews” in a single afternoon, receiving perfectly transcribed, neatly summarized insights in exactly the order you wanted and without the logistical headaches of recruitment, scheduling, or no-shows. There’s an old adage that says “you can have it done fast, you can have it done cheap, or you can have it done right, but you only ever get two of those things.” The fast-paced world of tech and product development is no different and this promise of speed, scalability, and cost-effectiveness is a siren song. 

Generative AI can create dynamic, data-driven personas that simulate user behavior with remarkable precision, giving us a powerful new tool in the UX research arsenal. But as with any powerful tool, there are hidden dangers, particularly when wielded as a panacea. While synthetic audiences offer a tempting shortcut, a sole reliance on them risks creating a profound chasm between your product and the very people it’s meant to serve. 

The most significant danger isn’t that this technology is flawed, but that we might forget what it was always meant to augment, not replace: the messy, complex, and deeply human truth found in an authentic conversation.

Relying exclusively on synthetic audiences can be like building a house based on a blueprint that’s missing a few critical details. On paper, everything is there: the structure looks sound, the dimensions are correct, and everything appears to be in its right place. But when a real person tries to live in it, they discover the doors are too narrow for furniture, the windows don’t open all the way, and the floors are uncomfortably slanted.

The problem lies in the data. Synthetic users are, by definition, a reflection of the data they were trained on. If that data is incomplete, biased, or outdated, the resulting personas will be, too. This isn’t just a hypothetical problem; it’s a very real threat to inclusivity and a perfect breeding ground for confirmation bias. 

If your training data is skewed towards a single demographic—say, young, tech-savvy professionals—your synthetic users will only ever reflect that narrow slice of the world. The struggles of an older user trying to navigate a new interface, the unique needs of someone with a disability, a unique industry among your regular customers, or the cultural nuances of a non-Western audience will be completely lost. The AI, in its earnest attempt to please you, might even generate responses that are overly agreeable or simplistic, praising every concept without offering the critical feedback that leads to true innovation.

The worst-case scenario isn’t a minor design flaw; it’s a product that fails spectacularly because it was built for a user who doesn’t actually exist. It’s a customer experience (CX) that feels cold, impersonal, and frustrating because it was optimized for a machine, not a human.

Another hidden danger in this approach lies in the permanence of the synthetic users. They never change their approaches, feelings, emotions, or opinions unless you take the time to change the data points they have been trained on. Feeding the model data from last year will give you a clear interpretation of what users from last year may have wanted, but any number of changes in personal opinions, competitive offerings, new features in unrelated products, etc. could be impacting what they want/need now, and your dataset is likely not capturing that. You need to talk to real customers to find out what is on the front edge of their experiential needs and what drives their engagement for the future.

This is where the quiet, understated power of the authentic user interview comes into its own. While synthetic audiences are fantastic for speed and scale, they cannot replicate the visceral experience of sitting down with a real person. An interview is more than just a Q&A session; it’s a dance of empathy, a space where you can uncover the “why” behind the “what.”

When you’re face-to-face with a user, you get so much more than words on a screen. You see the frustrated frown when they struggle to find a button. You hear the exasperation in their voice as they recount a previous bad experience. You notice the subtle hand gestures as they try to describe a mental model that doesn’t quite fit your product’s architecture. These are the microexpressions, intonations, and body language that no AI can yet fully replicate. This rich, qualitative data is the bedrock of empathy-driven design. It’s how you discover not just a user’s pain points, but their hopes, their fears, and their motivations. It’s how you uncover the latent needs they didn’t even know they had.

A real user might surprise you by using a feature in an unexpected way, revealing an unmet need or a brilliant workaround you never considered. A synthetic user, by contrast, is likely to stick to the script it was given. Authentic interviews lead to serendipitous insights that can change the entire trajectory of a product for the better. They provide the context, depth, and emotional understanding that truly defines an exceptional customer experience.

In the end, this isn’t an “either/or” situation. The most successful teams won’t choose between synthetic audiences and authentic interviews; they’ll use them as powerful complements. Synthetic audiences can be invaluable for early-stage ideation, for testing high-volume scenarios, and for quickly validating assumptions. But once you have that initial data, you must do the work of grounding it in reality. Use the speed of AI to inform where you focus your deep, qualitative research. Let the AI identify the “what,” and then use a real, human conversation to uncover the “why” and the “how.”

The true measure of a great customer experience is not how quickly you can develop it, but how well it serves the people who actually use it. By prioritizing the human element and treating authentic interviews not as a chore but as a privilege, we ensure that our products are not just functional, but genuinely empathetic. After all, you can simulate a person’s click, but you can’t truly simulate their heart.

Let’s talk about how to integrate authentic user insight into your AI-driven product strategy.

Claude “CJ” Jordy

Principal User Experience Designer

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