Will AI Make Us More Impulsive Shoppers?

AI promises to make shopping easier. It can compare products, summarize reviews, narrow the options, and increasingly, help make a purchase. For marketers accustomed to removing every obstacle between interest and checkout, the implication is that easier shopping means more buying.

But does it mean more impulse buying?

AI could encourage impulse buying by simplifying purchases and quickly resolving doubts about price, quality, or suitability. It could also encourage more considered decisions by making research easier and revealing trade-offs. The effect will likely depend on the shopper’s instructions, the product category, and how much purchasing authority the shopper gives the AI agent.

For brands, understanding those differences will be essential to influencing how consumers choose.

How AI Reduces Research and Checkout Friction

Agentic commerce refers to shopping experiences in which AI agents perform tasks on a consumer’s behalf, such as researching products, comparing options, and when authorized, completing purchases when authorized.

These capabilities can reduce two kinds of shopping friction: research friction, the effort required to decide what to buy, and transaction friction, the effort required to complete the purchase.

Digital commerce has spent years reducing transaction friction through saved payment details, one-click checkout, and streamlined purchasing flows. AI agents could take that further, helping shoppers move from a request to a purchase with fewer steps. But AI can also reduce the work of evaluating a purchase. Comparing specifications, sorting through conflicting reviews, calculating the full cost, and assessing unfamiliar brands all take effort. An AI assistant can help a buyer navigate those tasks before they commit.

Consider someone tempted by a product they see on social media. Before buying, they might ask whether it meets their needs, whether a cheaper alternative would suffice, or whether recurring complaints warrant a closer look. The answer could prompt reconsideration, or it could provide exactly the reassurance they need to buy immediately. Access to more information does not necessarily produce restraint. AI could compress evaluation and checkout at the same time, allowing a shopper to compare more options while spending less time reaching a decision.

When Could AI Encourage Impulse Buying?

Razorfish’s recent Agentic Divide research reveals a meaningful distinction between accepting assistance and delegating a purchase. While 74% are comfortable with AI suggesting options, 50% are comfortable letting AI fill a cart for them to review, and 40% are comfortable letting AI complete the purchase. That gap suggests consumers’ willingness to use AI for shopping should not be mistaken for a willingness to surrender control.

This distinction matters when considering impulse buying. A shopper asking an AI tool to compare three products remains actively involved in evaluating the answer. A shopper authorizing an agent to select and purchase a product within a budget has handed over more of the decision. Neither scenario tells us, on its own, whether the purchase is impulsive. A shopper could carefully instruct an agent to make a planned purchase or use an AI comparison to quickly justify an unplanned one. The research points to different levels of comfort with delegation; the behavioral question is what consumers do with the advice they receive.

Category and perceived risk also deserve attention. An unfamiliar beauty product and a major appliance invite different questions. AI might help someone justify an inexpensive treat while prompting a closer examination of an expensive commitment. Rather than assuming agentic commerce will produce a uniform increase in impulse buying, brands should examine the conditions surrounding the decision: what the shopper wants, what they need to know, and what they have authorized the agent to do.

How AI Recommendations Change Discovery

AI creates another point at which brands can enter or leave consideration. In our Agentic Divide research, 94% of U.S. AI users said they would consider an unfamiliar brand if AI recommended it. That openness gives brands a significant opportunity to reach prospective customers during evaluation, including people who might not otherwise have considered them. It also raises the stakes of the recommendation itself: what makes a product worth suggesting for a particular shopper’s needs?

When shoppers ask AI to research options, some evaluation happens before they reach a product page. Brands therefore need to consider whether they appear in an answer and how accurately their products are represented. A recommendation needs a rationale: why this product, for this person, under these conditions? Clear information about suitability, performance, price, and limitations helps support that rationale.

This is where generative engine optimization, or GEO, becomes relevant. GEO aims to improve how a brand and its information appear in AI-generated answers. For commerce brands, a practical starting point is making product information clear, accessible, and consistent enough to support an informed recommendation. That includes specifications, intended uses, limitations, total cost, availability, and return policies. Claims about performance or value should have supporting evidence, while details across a brand’s website and retailer listings should align.

Emotional storytelling and brand building remain important because they create desire, familiarity, and preference. Clear product information helps shoppers and the systems assisting them determine whether a purchase fits their needs. A brand may capture someone’s attention through compelling creative, then earn their confidence through a useful comparison or a clear explanation of compatibility. Marketers need to consider how those elements work together as AI takes on more of the evaluation.

How Brands Can Support AI-Assisted Purchase Decisions

For marketers, the opportunity extends beyond accelerating checkout. It includes understanding what prevents a shopper from feeling ready to buy and supplying the information needed to resolve that uncertainty. What does someone need to know to choose confidently? Which trade-offs or limitations should be explained? What costs or conditions might change the decision? And can shoppers and AI systems find consistent, supported answers across brand and retailer content?

These questions can reveal gaps that conventional conversion optimization misses. A shorter checkout cannot resolve uncertainty about compatibility. A persuasive product description may leave important questions about fees unanswered. Making those answers accessible can help a suitable product earn consideration and reduce the likelihood of a disappointing purchase. Product pages should explain who the product is for and why. Comparison content should address meaningful differences, and pricing and policy information should make the full commitment clear.

AI could increase impulse buying in some circumstances and encourage restraint in others. It could also blur the distinction, allowing shoppers to make researched decisions in the time impulse purchases once took. That makes the evaluation itself important territory for brands: the questions AI helps shoppers answer, the concerns it resolves, and the reasons it surfaces to proceed or reconsider.

As AI takes on more of that work, marketers will need to understand how their products hold up to scrutiny. Earning a recommendation will require clear, credible reasons a product fits the shopper’s needs, alongside the appeal that made them want it in the first place.