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As everyone rushes to position their products as “AI-powered”, Kristen Berman from Irrational Labs stepped back to ask: do people actually care? And Kristen and her team surveyed 767 software users to find out.
Kristen is co-founder and CEO of Irrational Labs where she’s helped the top companies (think: Google, Intuit, TikTok, LinkedIn) use behavioral science insights to understand user psychologies and build products that customers use, love and pay for. Want more consumer psychology and Kristen? Follow her Product Teardowns newsletter (very engaging short videos!) or email her ([email protected]) to learn about working with her team.
Imagine your team is launching a new generative AI feature. Everyone has worked really hard for the last 6 months to scope, design, build, and test this new feature. You’re excited to finally launch, but now you have a decision to make: How much do you share that you’re using AI? You are considering a marketing landing page that leads with AI-powered or AI-enhanced. Should you do it?
It might seem like the more you emphasize AI, the more innovative and valuable your product will appear. After all, it’s a strategy everyone has used—from Intuit Assist to UserTesting.com. And adding AI to your product's name seems like a surefire way to make your company and your product appear on the cutting edge of innovation.
As it turns out, this isn’t necessarily true. In fact, my team’s latest research has flipped this assumption on its head.
Labeling your product as “AI” might not be the boost you think it is.
It doesn’t necessarily build trust, justify a higher price, or convince your customers it’ll perform better. And if you misuse it, you may end up doing the one thing no marketer or product manager should do—turning your audience off.
The surprising science behind AI labels and what our research revealed
With AI well beyond buzzword status and creeping dangerously close to cliche, the Irrational Labs behavioral science team set out to explore its actual impact on customer perceptions.
The question: Does labeling a product as “AI-powered” inspire trust and excitement, or does it risk blending into the noise of tech jargon?
To answer this, we surveyed 767 participants and conducted a controlled experiment. Of the 767, 73% had a bachelor’s degree or higher, 48% were men, and 60% earned above $60k annually (the average income in the US).
Participants were presented with marketing landing pages for four real-life products featuring AI-driven capabilities.
In one condition, the product descriptions prominently included terms like "AI-powered" or "generative AI.”
In the other condition, those descriptions focused on the features and benefits without directly mentioning AI.
By comparing how participants responded to these two approaches, we sought to uncover how explicit AI-labeling influenced perceptions of product performance, trust, and willingness to pay.
Here are a couple of examples of what this looked like:
What we found:
Performance expectations: Rather than enhancing perceptions, the term “generative AI” significantly lowered expectations of a product’s potential impact. This decline may reflect growing skepticism due to high-profile disappointments—tools that overpromise and under-deliver, such as unreliable chatbots or lackluster generative content.
Willingness to pay (WTP): Labeling a product as AI-driven did little to justify a higher price. Customers were unwilling to pay more unless the tool demonstrated clear, compelling benefits. Simply including “AI” in the description wasn’t enough to convince users to part with their money.1
There was one exception. When Superhuman was labeled with AI, it got a bump above no mention of AI. We’re not sure why. Our best guess is the AI description was helpful to explain why it was called Superhuman!
Trust perception: The impact on trust was largely neutral. For most brands, explicitly mentioning AI didn’t increase customer confidence in the tool’s reliability.
Our takeaways? Explicit AI-labeling is no guarantee of success. It doesn’t increase performance expectations, willingness to pay (in most cases) and trust.
One caveat—we’re testing with “average people.” Our sample is not early adopters. 68% of people in this survey say they have used AI at least a couple of times. Only 15% of people in the study use it daily. Likewise, if you are targeting early adopters (or Bay Area tech workers) these findings may not apply. That said, most companies will need to go beyond the Bay Area to scale.