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Decidedly, I ordered a different cat litter, one made from biodegradable corn, which promised to be less dusty. As I swapped out the litter, my tuxedo cat, Friday, trotted into the room with more gusto than I’ve seen from her in a while. She walked directly into the litter box and started eating the litter with wild abandon, like she had been starving for two days, and a delicious feast of roast duck was sitting before her. And yes, reader, I do feed my cats regularly and give them treats as well. This behavior was very strange. She became so obsessed with eating the litter that I couldn’t keep her out of it, and therefore, had to throw it out. Thankfully I had only put the litter in one of the boxes.

But now I was down a cat box, and it was a workday. And I hate running errands after work. As many of you can probably relate, by the end of the workday, I have zero brain cells left. All of my cognitive power has been spent on work tasks, and there is just nothing left up there but an old soggy sock. And I can’t make meaningful decisions on soggy sock power. It just doesn’t work. So, there I was in my local big box grocery store, staring down the shelf of cat litter. A giant shelf. A massive shelf. A shelf in the middle of a giant store with blaring lights and distracting music and people arguing in the aisles. So, I scan those boxes using what’s left of my soggy sock brain power to try to make the best decision for my asthma-inflamed lungs while trying to remember if I cared about the prices or whether the litter smelled like roses. And in that instant, I completely gave up. I just need to get the damn litter and be on my way.

That is when I remembered I had ChatGPT. I took a picture of the shelf and sent it to ChatGPT with the question: Which of these litters is least dusty? And less than a second later, it gave me the answer. It listed the top two best options and which ones to avoid. But in the state that I was in, I cared only about the top choice and simply bought it. ChatGPT saved me time and brain power. No extensive searching through reviews, ingredient lists, package claims, pricing, or worrying about whether I would have to order it, or if it would be in store. One and done.

This experience got me thinking. AI opens up a new avenue for people to shop: by selective criteria. Before AI, I would have stood there at the shelf, scanning quickly for anything that messaged to less dust and picked that. If messages were difficult to read, like they were on this particular shelf that I encountered, I would have just picked something in a lower price range or relied on a litter I had used in the past. But AI now opens up an avenue of not only quick decision making, but also narrow decisions based on specifications.

So, what does that mean for marketing science? As more people adopt AI as their personal shopping assistant, previous shopping methods which consider brand, packaging, messaging, and even pricing may no longer apply. Moms who have kids with gluten allergies can ask AI which of the products shown are gluten-free. Dads on a budget can ask AI which of the products is least expensive. Shopping can become curated to the person’s specific needs at that particular moment.

As AI becomes more common place, marketing research will need to include questions like: When are consumers using AI to do the shopping? Which categories are consumers using AI to help them with their decision making? What are the most common specifications consumers use when they shop using AI? Understanding when and how consumers are using AI as their personal shoppers will impact marketing research methods in the near future.

Conclusion

As AI tools become increasingly integrated into everyday shopping, traditional marketing levers like price cues, brand loyalty, and packaging claims risk losing influence. Consumers will lean on AI not just for convenience, but for highly personalized, criteria-driven recommendations. This pivot means marketing scientists and brands alike must adapt, understanding not only what specifications matter most to shoppers using AI, but also when and how these digital assistants shape their purchase decisions.

In this new landscape, the game changes from capturing the shopper’s attention on the shelf to ensuring that algorithms recognize and rank the product according to increasingly individualized needs. Ultimately, the rise of AI shopping assistants signals a future where smarter technology empowers consumers, and the brands best equipped to serve both algorithms and end-users will be the ones that thrive.

Audrey Guinn

Statistical Consultant, Advanced Analytics Group

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Audrey utilizes her knowledge in both inferential and Bayesian statistics to solve real-world marketing problems. She has experience in research design, statistical methods, data analysis, and reporting. As a Statistical Consultant, she specializes in market segmentation, SEM, MaxDiff, GG, TURF, and Key Driver analysis. Audrey earned a Ph.D. and Master of Science in Experimental Psychology with an emphasis on emotional decision-making from The University of Texas at Arlington.