Consumers are open to AI recommending how they pay for something. They are not open to AI deciding for them.
PYMNTS Intelligence's latest Pay Later Ecosystem Report found 61% of U.S. consumers would consider an AI shopping assistant's Buy Now, Pay Later recommendation in at least one common purchase category. The survey covered 2,034 U.S. adults, and 39% said they'd already used AI for some kind of payment-related activity in the past three months.
The Interest Spans Every Generation
This isn't a younger-shopper trend the way most AI adoption stories go. Gen Z leads at 80% open to an AI-recommended financing option, followed by millennials at 78% and bridge millennials at 69%.
Gen X isn't far behind, with 60% open to the idea. Even baby boomers, typically the most AI-skeptical generation in consumer surveys, showed real interest at 36%.
That spread matters for how BNPL providers think about the feature. A tool that appeals almost exclusively to Gen Z is a niche product. A tool with meaningful interest across five generations is closer to a mainstream expectation.
It also complicates the usual argument against building these features: that older, more cautious consumers won't trust an algorithm with their finances. More than a third of baby boomers saying they're open to it suggests the resistance to AI in financial decisions is softer than providers may have assumed, at least when the AI is recommending rather than deciding.
Electronics Lead, But Not By Much
No single category dominates. Electronics drew the most interest at 17%, with furniture, apparel, travel and everyday essentials each landing at 13%. Home services and repairs came in at 12%, while medical or dental expenses and auto-related costs each reached 11%.
The spread across both planned purchases and the kind of unexpected bills that show up without warning suggests consumers see AI-recommended financing as useful well beyond big-ticket retail moments.
Consumers Have Already Decided What Control Looks Like
The report's clearest finding isn't the appetite for AI recommendations, it's how tightly bounded consumers want that appetite to be.
Requiring approval before a plan gets finalized and selecting the most affordable option tied as the top conditions, each chosen by 24% of respondents. Avoiding a new credit account and setting a maximum purchase amount followed closely, each at 21%.
Only 2% would let AI make the decision with no limits at all.
Protecting credit scores mattered most of all, with 59% calling it very or extremely important. Fifty-six percent prioritized the lowest total cost over time, and 54% wanted the most affordable monthly payment. When it came to what actually builds trust in the tool, requiring approval before purchase ranked first at 28%, ahead of simply confirming the total cost upfront at 25%.
The picture that emerges looks less like a fully autonomous financial advisor and more like a well-informed store clerk: someone who lays out the options clearly, then waits for the customer to say yes.
What This Means for Miami
South Florida's fintech sector, which includes a dense cluster of consumer lending, BNPL and neobank startups, has an unusually direct read on this data. Miami's retail and e-commerce base, with deep ties to Latin American consumers who already have high familiarity with installment financing, may be an especially receptive market for AI-assisted Pay Later tools built the way this survey describes.
The practical takeaway for local fintech builders is specific. Consumers aren't asking for a smarter algorithm making financing decisions on their behalf, they're asking for transparency and a hard approval step before anything gets finalized. Miami-based BNPL and fintech companies designing AI features around comparison and disclosure, rather than autonomous decision-making, are building toward what this survey shows consumers will actually trust and use.
The generational data adds a second consideration for local product teams. A Miami fintech company assuming this feature only needs to work for younger, digitally fluent users would be building for roughly half the addressable interest shown here. Designing the approval and disclosure steps to work as well for a 60-year-old Gen X shopper as a 22-year-old Gen Z one may be the difference between a feature people actually adopt and one that quietly underperforms its own survey data.
