AI Shopping Goes Mainstream: Implications for Consumers and Marketers

1. AI Tools Are Already Part of the Discovery Process 

 
Findings 

Nearly half of consumers, 49%, say they regularly consult AI tools such as ChatGPT, Claude, or Gemini for shopping ideas or gift recommendations. Another 23% use these tools occasionally, bringing total reported usage to 72%. 

Only 16% say they prefer to research products entirely on their own. Another 12% are not currently using AI for this purpose but remain open to doing so. 

The results suggest that AI-assisted shopping research has moved beyond experimentation for a substantial share of consumers. Regular users alone outnumber occasional users by more than two to one.

2. Product Recommendations Lead AI-Powered Shopping Features 

 
Findings 

Product recommendations are the most commonly used AI-powered shopping feature, selected by 55% of consumers. Chatbots follow at 41%, while 31% have used visual search. 

More personalized applications have also gained traction. Twenty-six percent have used personalized deals, while 23% have used AI-powered size or fit recommendations. 

Voice shopping remains significantly less common at 8%. Meanwhile, 21% say they have not used any of the AI-powered shopping features listed. 

The results show that adoption is strongest where AI helps consumers narrow choices, answer questions, or find relevant products. More autonomous or less familiar applications remain considerably less established. 

3. Consumers Show High Levels of Trust in AI Recommendations 

 
 
Findings 

Forty-four percent of consumers say they trust AI-generated product recommendations more than recommendations from other sources. Another 31% trust them about the same. 

Combined, three-quarters of consumers place at least as much trust in AI-generated recommendations as they do in alternative sources. 

Skepticism remains present but represents a smaller portion of respondents. Fourteen percent say they trust AI recommendations less, while 11% do not trust them at all. 

The balance therefore leans clearly toward acceptance: consumers are not simply using AI despite concerns about credibility; most report a level of trust comparable to or greater than other recommendation sources.  

4. Privacy and Recommendation Quality Will Determine the Next Stage of Adoption 

 
Findings 

Data privacy is the factor most likely to increase consumers’ willingness to use an AI personal shopping assistant, selected by 37% of respondents. 

Better recommendations follow closely at 30%, making privacy and relevance substantially more important than the other factors tested. 

Fourteen percent say they are simply not interested in using an AI personal shopping assistant, while 12% would be encouraged by greater time or money savings. Only 5% cite the ability to override the assistant, and 2% point to word of mouth. 

The findings suggest that resistance to AI shopping assistants is less about the concept itself and more about whether consumers believe the experience is useful enough—and whether they can trust how their information is handled. 

Key Takeaways and Recommendations 

AI is not the future, it is the reality 

With 72% consulting generative AI tools regularly or occasionally for shopping ideas and recommendations, AI now sits alongside traditional search mechanisms, marketplaces, retailers, social media platforms, and other discovery surfaces. 

For marketers, this expands where product consideration begins. Product information must increasingly be structured, accurate, and distinctive enough to surface effectively through AI-mediated discovery, while owned experiences should make it easy for customers to continue the journey once AI directs their attention toward a product or brand. 

The objective should not be to create an “AI campaign” in isolation. Brands should treat AI as another interface through which customers discover, evaluate, and eventually purchase products. 

Trust Is an Advantage, but It Must Be Earned Continuously 

Three-quarters of consumers trust AI-generated recommendations as much as or more than other sources. That gives brands significant permission to use AI in the shopping experience, but it should not be interpreted as unconditional trust. 

The same consumers who accept AI recommendations identify privacy as the largest opportunity for improving AI shopping assistants. Trust in the output does not necessarily equal comfort with the data required to produce it. 

Brands should make the value exchange clear: what information is being used, why it improves the experience, and what control consumers retain. Strong AI personalization should feel useful enough to justify the data behind it, rather than simply more intrusive because more data is available. 

Better AI Means Better Relevance, Not More AI 

After privacy, better recommendations are the factor most likely to increase willingness to use an AI shopping assistant. This is an important distinction. Adoption will not grow simply because retailers add more AI features. 

The next competitive advantage will come from the quality of the recommendation itself. AI should understand customer preferences, current intent, previous behavior, context, and available products well enough to reduce the distance between a shopper’s need and the right choice. 

This places customer data and decisioning at the center of the AI opportunity. Generative interfaces may change how consumers interact with brands, but the quality of those interactions will still depend on the intelligence underneath them. 

Positionless Marketing Turns AI Insight Into Customer Action 

AI-powered shopping increases both the amount of customer intelligence available to marketers and the speed at which that intelligence becomes relevant. A shopper can move from an AI-generated gift idea to browsing a product, asking a question, and considering a purchase within minutes. 

Traditional workflows built around separate data, analytics, creative, and campaign teams can struggle to keep pace with that journey. By the time an insight moves through multiple handoffs, the customer may already have changed direction. 

Positionless Marketing gives marketers the ability to move from insight to execution more independently: identifying an audience, understanding intent, creating relevant content, selecting an offer, and activating it across channels without waiting on a sequence of specialized teams. 

For AI-powered shopping, this means marketers can respond to the signals AI helps generate while they still matter. The advantage is not simply using more AI; it is combining intelligence, creativity, and execution quickly enough to make every recommendation or interaction more relevant. 

Conclusion 

AI shopping has already crossed an important threshold. Most consumers in this research use AI tools for shopping research, product recommendations have become mainstream, and trust in AI suggestions is relatively high. 

The next stage will be defined less by adoption than by quality. Consumers are signaling that they want recommendations that genuinely improve their decisions and experiences that protect their data while using it intelligently. 

For marketers, the opportunity is to treat AI as part of the customer journey rather than as a standalone technology. Brands should build around assisted discovery, stronger personalization, transparent data practices, and customer control—using AI where it removes friction rather than simply where it can be added. 

Positionless Marketing makes that strategy actionable. When marketers can independently connect customer intelligence with creation and execution, AI becomes more than a recommendation engine or interface. It becomes a way to recognize intent and respond with greater relevance at the moment the customer is ready to act.