The question now is which artificial intelligence tools will cement their place as go-tos for consumers and their shopping and spending habits. A landmark study of Iowa corn farmers published decades before the emergence of ChatGPT, Claude and other AI models provides some clues.

The 1962 book-length study showed that on the eve of the Great Depression, a small number of farmers in two Iowa communities began scrapping old planting methods dating back to Native American practices in favor of recently invented hybrid strains. By 1941, after droughts had proved the hybrids resilient, nearly all the farmers had switched. Over roughly a decade, they showed an adoption pattern that followed the shape of the letter S, with slow initial uptake, then rapid acceleration as the majority climbed aboard, then a flattening of the upswing as the remaining holdouts joined in.

AI tools like Google Gemini aren’t popcorn. But they’re starting to show a pattern of acceptance similar to the one described by the influential sociologist Everett Rogers in his Iowa farmers study. Rogers argued that his “diffusion of innovations” model was a signature of how any new idea moves through a population. That’s why we’re talking here about a vegetable. With AI agents that carry out shopping orders autonomously estimated to handle 15% to 25% of all U.S. eCommerce purchases by 2030, according to JPMorganChase, how consumers adopt AI now and in the coming years has big stakes for the payments industry.

An April PYMNTS Intelligence report, “The AI On-Ramp: Data Shows How Everyday Tasks Build Consumer Habits,” suggested that consumer use of AI is following the same S-shaped curve as the Iowa corn farmers, crossing from early adopters (the small group trying hybrid seeds) into the majority. In Rogers’ framework, that transition is a highly consequential moment in a technology’s commercial life because it’s when the niche goes mass market and trials and experiments crystallize into lasting habits. It’s also when technologies that establish themselves as the default (hybrid corn seeds) for key tasks can gain structural advantages.

Seed of an Idea

Rogers argued that an innovation takes off with individuals when it has a clear advantage, is compatible with existing practices, is easy to understand, can be tried at low cost (financial and time- and asset-wise) and produces visible results. One of the leading scholars of how innovations take hold, he famously segmented his farmers into Innovators, Early Adopters, Early Majority, Late Majority and Laggards, which the PYMNTS report partially mirrored. Rogers found that with his farmers, the S-shaped curve took off when 10% to 25% of the population adopted a new technology as a result of “interpersonal networks” (“Hey! This seed works!”) becoming “activated.”

In that vein, the PYMNTS report found that AI has taken off with young consumers. Generation Z adults, the oldest now 29, are firmly in the majority of AI users. Roughly 70% of Gen Z adults are already using the technology for tasks like finding product links, writing resumes and editing personal writing, looking up medical symptoms and health information, learning new skills, crafting social media content, and sourcing financial guidance. Boomers and seniors are another matter. More than 1 in 3 American consumers aged 60 and older don’t use AI tools, consistent with the “laggard” profile in Rogers’ thesis about older farmers resisting hybrid seeds.

The PYMNTS report also revealed that AI tools become an “on-ramp” to wide acceptance when they have certain features. One of them is delivering value immediately, such as allowing a consumer to find relevant product links faster through, say, Google Gemini, than through a conventional Google search or Amazon. That captures Rogers’ theory of relative advantage, meaning the degree to which an innovation is perceived as better than what it replaces. And it mirrors his observation that severe droughts across the Midwest in 1934, 1936 and 1939-40 convinced farmers that drought-resistant hybrid seeds were better than open-field pollinated ones.

Another feature for an on-ramp posited by the PYMNTS report was the ability to try AI without losing too much if it makes a mistake. If ChatGPT shows, say, purple king-sized 300-thread count duvet covers when you want a lavender gray, queen-sized 600 count one, you’ve wasted a bit of time but nothing more.

The PYMNTS report also partially echoed Rogers on another front. It used data to argue that tasks that require no prerequisite knowledge and no specialist context were the ones that spread most broadly. That’s another way of saying they’re compatible with the widest range of existing habits and values. Rogers argued that hybrid corn seeds took off in part because they were “perceived as consistent with the existing values, past experiences, and needs of potential adopters. An idea that is not compatible with the prevalent values and norms of a social system will not be adopted as rapidly as an innovation that is compatible.” Similarly, the PYMNTS report said that the two highest-adoption tasks with generative AI—finding product links and editing personal writing—succeed because they require no demographic preconditions and thus are compatible with broad societal norms. Among AI users of all ages and income levels, 30% use AI models to look for product links and writing tasks.

A ChatGPT prompt that lists peer-reviewed medical studies on Lyme disease can be a lifesaver. An agent that autonomously books a vacation on your behalf according to your specific requirements can be a time- and wallet-saver. Still, beneficial innovations don’t sell themselves. As Rogers wrote in a later edition of his original study, the British Navy took nearly 50 years to adopt rations of oranges, lemons and other citrus fruits. (He blamed competing alternatives, a negative signal from a credible source in Captain Cook’s Pacific voyage reports, and the low status of the British researcher who demonstrated the solution.)

In other words, it’s not the actual quality of an innovation that drives its diffusion. It’s what people perceive, based on what their peers tell them and show them. An AI model can have all the bells and whistles, but as the PYMNTS data showed, consumers just want to find the right product and information.

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