![[RTP] Gartner Emily](https://www.retailtouchpoints.com/wp-content/uploads/2026/10/RTP-Gartner-Emily-.png)
Key takeaways
- Consumers are using GenAI primarily for holiday gift inspiration and price comparison, but they still want to complete purchases themselves across traditional retail channels.
- Retailers can improve AI-driven product discovery by strengthening product data, availability, shipping details and fit-related customer feedback.
Generative AI is changing how consumers research holiday gifts, but it isn’t replacing the shopping habits they already have. According to Gartner research, shoppers turn to GenAI tools first for inspiration: finding gift ideas they hadn’t considered, often sorted by the interests or ages of the people on their lists.
But with tighter budgets, from rising costs for consumer goods, gas and everyday living expenses, have left consumers with less to spend on gifts this season. Increasingly, they are turning to AI tools to compare prices, set restock alerts and pinpoint the best time to buy. Their searches are becoming more time-sensitive as well, with shoppers looking for products that can arrive before the holiday.
The result is a discovery phase spread across more surfaces than before. Shoppers move between search engines, marketplaces, brand sites, physical stores and AI assistants, using each one to confirm or rule out ideas. That fragmentation makes the path to purchase harder for retailers to predict. The final step, however, hasn’t shifted to automation.
In this Q&A with RetailTouchPoints.com, Gartner marketing analyst Emily Weiss explains what these shifts mean for retailers during the holiday season. The conversation details how consumers use GenAI for gift inspiration and price comparison, trust in AI gift recommendations varies by generation and how retailers can make products easier to find on AI-powered platforms through better product data, accurate availability and delivery information.
RetailTouchPoints.com: When consumers say they’re willing to use GenAI for holiday gift recommendations, what specific jobs are they hoping AI will do best?
Emily Weiss: In short – inspiration. Help them discover options that they might not have previously been aware of, especially by interest or age group of the person they are shopping for. And also to narrow options down from the beginning of their consideration and research phase. We see in our survey that people have less to spend on gifts this holiday season, giving the rising cost of nearly all consumer goods, gas, and overall living expenses. So they’re also using AI tools to do price comparisons, set alerts for re-stocks of products, and help ensure they are finding the best price and best time to buy.
RetailTouchPoints.com: Your findings suggest AI expands discovery rather than replaces existing channels. What does that mean for retailers trying to orchestrate consistent product discovery across search, marketplaces, brand sites, stores and AI assistants?
Weiss: As much as possible, retailers should focus on connecting retail and brand data to ensure that content is consistent across all touchpoints and understand that, while consumers may be starting their shopping journey with an intent-based search, they still, by and large, want to complete that purchase themselves, whether that is online or in-store. Particularly when it comes to holiday shopping, consumers still want the tactile experience and assurance of having product in hand when they make a purchase. Our survey shows high income and affluent consumers are even more likely to show a preference for in store shopping when it comes to buying holiday gifts.
Who Trusts GenAI for Holiday Gift Recommendations?
RetailTouchPoints.com: Did the survey reveal any meaningful differences by age, income, digital fluency or shopping frequency in willingness to use GenAI for holiday recommendations?
Weiss: In our survey on holiday shopping, Gen Z consumers were just as likely as Millennial consumers to agree that they “trust AI tools or chatbots to recommend suitable holiday gifts for friends or family”. Fewer Gen X and Baby Boomers agreed with the same statement.
RetailTouchPoints.com: If AI users consult more inspiration sources than non-AI users, should retailers interpret that as higher engagement, or as a sign that shoppers still don’t fully trust AI-generated recommendations on their own?
Weiss: I don’t know that it’s either one. AI tools and chatbots are, for consumers, an additional surface on which they can validate or invalidate ideas. They aren’t replacing other surfaces, only serving, from the retailer side, to fragment that early phase of the discovery journey. Ultimately, and especially with something as meaningful as a holiday gift, consumers want to feel they have done the work of finding the best match and version of the thing they are gifting, and they’re figuring out how AI tools can help them accomplish that. But they do not want that shopping process to be fully automated or for it to diminish the joyful aspects of considering what to buy for another person.
How Should Retailers Fit AI Into the Holiday Discovery Journey?
RetailTouchPoints.com: What are the biggest risks for retailers that treat AI shopping tools as a separate channel instead of part of a broader, fragmented discovery journey?
Weiss: Retailers know that consumer shopping journeys have become increasingly less linear, which makes them harder to predict. Because AI shopping tools are still very new in terms of adoption and what consumers are actually finding them useful for, it would be unwise to overlook how they connect to and complement consumers’ overall shopping journey. So really the risk is losing visibility into the complexity of decision-making and what barriers exist on the path to purchase.
RetailTouchPoints.com: What should retailers prioritize right now to make their products more “AI-discoverable” during holiday — better product data, richer content, review strategy, merchandising structure or something else?
Weiss: Gartner has evidence that consumers are increasingly using time-based search terms as a part of their AI-assisted product research and discovery. So for retailers, data around product availability, shipping options and their expected delivery, and fulfillment execution should all be prepared and optimized in order to improve product discovery and rankings. Searches for “like me” parameters – particularly when it comes to clothes and shoe fittings – are also increasingly common as consumers want to narrow down assortment options from the beginning to only see what is available in their size, best fit for their body type, or the sizes of anyone to whom they are gifting apparel. So detailed information from other customers – whether that’s from reviews on retailer websites or content in Reddit forums – based on fit feedback that will also help products appear in AI-powered platforms.
