Hello all.
I admit, I didn't expect much. In fact, I was fully prepared to dismiss it as just another AI gimmick—a shiny distraction from real user research. But after experimenting with using AI-generated personas (or "synthetic personas," as they're being called), I found myself in a strange new territory: genuinely impressed.
If you're skeptical, trust me—I get it. Personas traditionally demand significant legwork: user interviews, surveys, digging into analytics. Cutting corners here can feel uncomfortable, even reckless. Yet, despite my reservations, the AI method delivered insights deeper and richer than I'd anticipated.
But let's slow down for a second. What exactly am I talking about?
The method is straightforward:
On paper, this is a dream. But it also feels a bit... risky, doesn't it? After all, aren't we skipping the very essence of UX: truly understanding real people?
That's exactly why I approached it cautiously.
Here's what surprised me though—while the AI didn't independently generate personas I hadn't considered, it asked incredibly insightful questions about my target audience that prompted me to explore additional user segments. As we discussed each potential persona, it suggested ways to add more depth and nuance to my initial ideas.
During a recent client project, this questioning approach led me to expand beyond my basic buyer personas. The AI's prompts helped me flesh out variations I might have glossed over—like the specific concerns of nervous first-time buyers. Because creating additional personas was so effortless, I ended up developing a much richer, more nuanced set of user profiles than I'd originally planned.
In short, I got a more detailed picture of my users—and it took just a fraction of the usual research time.
Here's the honest answer: not entirely.
AI is scouring web data, but it's doing so without human empathy, intuition, or nuance. It's excellent at synthesizing vast amounts of data but struggles with subtleties like hidden motivations or evolving emotional contexts.
Still, as a starting point, it's impressive. Think of synthetic personas as a sophisticated first draft, but still in need of human validation to avoid slipping into clever illusions.
People don't always express their true needs and desires online. What appears in digital spaces is just a fraction of human experience. This limitation becomes even more pronounced when dealing with niche products or services - there simply might not be enough relevant online data for AI to generate meaningful personas. Without sufficient training data, these synthetic personas risk becoming oversimplified or misaligned with actual user needs.
That's why validation becomes crucial. One of the most effective ways to ensure your synthetic personas reflect reality is to share them with actual end users for feedback. Do these AI-generated profiles genuinely represent their experiences? If not, how do they differ? This real-world validation can highlight gaps or misconceptions in the synthetic personas while providing valuable additional insights.
Even if direct access to end users proves challenging, there's still room for validation. Internal stakeholders who regularly interact with your target audience—such as customer service representatives, sales teams, or account managers—can provide valuable feedback on how well these synthetic personas align with their day-to-day observations of real users. Their practical experience can help refine and ground these AI-generated profiles in reality.
There's another critical downside: synthetic personas create a barrier between you and your users. One of the most rewarding aspects of UX is direct interaction with real people. Face-to-face conversations, interviews, and observational research not only give you authentic insights but also help you build genuine empathy.
When you rely solely on AI-generated personas, you miss out on that human connection. Your understanding becomes theoretical rather than experiential. This lack of direct interaction can subtly but significantly impact your ability to truly empathize with users' genuine struggles, needs, and aspirations.
In short, sy
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