With AI upon us, companies have recently been turning their attention to synthetic user testing AI-driven research that replaces UX research. There, questions are answered by AI-generated customers, human tasks performed by AI agents.However, its not just for desk research or discovery that AI is used for; its an actual usability testing with AI personas that mimic human behavior of actual customers within the actual product. Its like UX research, just well, without the users.If this sounds worrying, confusing, and outlandish, it is but this doesnt stop companies from adopting AI research to drive business decisions. Although, unsurprisingly, the undertaking can be dangerous, risky, and expensive and usually diminishes user value.This article is part of our ongoing series on UX. You can find more details on design patterns and UX strategy in Smart Interface Design Patterns with live UX training coming up soon. Free preview.Fast, Cheap, Easy And ImaginaryErika Hall famously noted that design is only as human-centered as the business model allows. If a company is heavily driven by hunches, assumptions, and strong opinions, there will be little to no interest in properly-done UX research in the first place.But unlike UX research, AI research (conveniently called synthetic testing) is fast, cheap, and easy to re-run. It doesnt raise uncomfortable questions, and it doesnt flag wrong assumptions. It doesnt require user recruitment, much time, or long-winded debates.And: it can manage thousands of AI personas at once. By studying AI-generated output, we can discover common journeys, navigation patterns, and common expectations. We can anticipate how people behave and what they would do.Well, thats the big promise. And thats where we start running into big problems.LLMs Are People PleasersGood UX research has roots in what actually happened, not what might have happened or what might happen in the future.By nature, LLMs are trained to provide the most plausible or most likely output based on patterns captured in its training data. These patterns, however, emerge from expected behaviors by statistically average profiles extracted from content on the web. But these people dont exist, they never have.By default, user segments are not scoped and not curated. They dont represent the customer base of any product. So to be useful, we must eloquently prompt AI by explaining who users are, what they do, and how they behave. Otherwise, the output wont match user needs and wont apply to our users.When producing user insights, LLMs cant generate unexpected things beyond what were already asking about.In comparison, researchers are only able to define whats relevant as the process unfolds. In actual user testing, insights can help shift priorities or radically reimagine the problem were trying to solve, as well as potential business outcomes.Real insights come from unexpected behavior, from reading behavioral clues and emotions, from observing a person doing the opposite of what they said. We cant replicate it with LLMs.AI User Research Isnt Better Than NothingPavel Samsonov articulates that things that sound like customers might say them are worthless. But things that customers actually have said, done, or experienced carry inherent value (although they could be exaggerated). We just need to interpret them correctly.AI user research isnt better than nothing or more effective. It creates an illusion of customer experiences that never happened and are at best good guesses but at worst misleading and non-applicable. Relying on AI-generated insights alone isnt much different than reading tea leaves.The Cost Of Mechanical DecisionsWe often hear about the breakthrough of automation and knowledge generation with AI. Yet we often forget that automation often comes at a cost: the cost of mechanical decisions that are typically indiscriminate, favor uniformity, and erode quality.As Maria Rosala and Kate Moran write, the problem with AI research is that it most certainly will be misrepresentative, and without real research, you won't catch and correct those inaccuracies. Making decisions without talking to real customers is dangerous, harmful, and expensive.Beyond that, synthetic testing assumes that people fit in well-defined boxes, which is rarely true. Human behavior is shaped by our experiences, situations, habits that cant be replicated by text generation alone. AI strengthens biases, supports hunches, and amplifies stereotypes.Triangulate Insights Instead Of Verifying ThemOf course AI can provide useful starting points to explore early in the process. But inherently it also invites false impressions and unverified conclusions presented with an incredible level of confidence and certainty.Starting with human research conducted with real customers using a real product is just much more reliable. After doing so, we can still apply AI to see if we perhaps missed something critical in user interviews. AI can enhance but not replace UX research.Also, when we do use AI for desk research, it can be tempting to try to validate AI insights with actual user testing. However, once we plant a seed of insight in our head, its easy to recognize its signs everywhere even if it really isnt there.Instead, we study actual customers, then triangulate data: track clusters or most heavily trafficked parts of the product. It might be that analytics and AI desk research confirm your hypothesis. That would give you a much stronger standing to move forward in the process. Wrapping UpI might sound like a broken record, but I keep wondering why we feel the urgency to replace UX work with automated AI tools. Good design requires a good amount of critical thinking, observation, and planning.To me personally, cleaning up after AI-generated output takes way more time than doing the actual work. There is an incredible value in talking to people who actually use your product.I would always choose one day with a real customer instead of one hour with 1,000 synthetic users pretending to be humans.Useful ResourcesSynthetic Users, by Maria Rosala, Kate MoranSynthetic Users: The Next Revolution in UX Research?, by Carolina GuimaresAI Users Are Neither AI Nor Users, by Debbie LevittPlanning Research with Generative AI, by Maria RosalaSynthetic Testing, by Stphanie Walter, Nikki Anderson, MAThe Dark Side of Synthetic AI Research, by Greg NudelmanNew: How To Measure UX And Design ImpactMeet Measure UX & Design Impact (8h), a new practical guide for designers and UX leads to measure and show your UX impact on business. Use the code IMPACT to save 20% off today. Jump to the details. 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