Santa's AI Elf: LSU Experts Break Down Artificial Intelligence Boom Behind Holiday Shopping Trends

Consumers are increasingly turning to artificial intelligence tools for holiday shopping—especially Gen Z shoppers, who are using platforms like ChatGPT and social media not only for gift inspiration but also to find the best prices. 

Andrew Schwarz, professor in the LSU Stephenson Department of Entrepreneurship & Information Systems, and Dan Rice, associate professor and Director of E. J. Ourso College of Business Behavioral Research Lab, share their insight on this emerging trend.

AI is the new front door for search 

Schwarz: We’re seeing a fundamental change in how consumers find information. Instead of browsing through multiple pages of results, users—especially Gen Z—are jumping straight to conversational AI for curated answers. That dramatically shortens the shopping journey. 

 For years, companies optimized for SEO to appear on the first page of Google; now they’ll have to think about how their products surface in AI-generated recommendations. This may lead to a new form of “AIO”—AI Information Optimization—where retailers tailor product descriptions, metadata, and partnerships specifically for AI visibility. The companies that adapt early will have a distinct advantage in capturing consumer attention. 

 Rice: This issue of people being satisfied with the AI results (like a summary at the top of the Google results) and then not clicking on any of the paid or organic links leads to a huge increase in what we call “zero click search” (for obvious reasons).  

 For some providers, this is leading to significant drops in web traffic from search results, which can be disconcerting due to the potential loss of leads. However, to Andrew’s point of shortening the journey, it means that the consumers who do come through are much more likely to buy (quickly) because they are “better” leads.  

What this translates to is some seemingly paradoxical situations for providers where they are seeing drops in click-through rates and visitors/leads, but simultaneously seeing revenue increases because the visitors are “better” visitors.  

There is a rise in personalized shopping journeys. 

 Schwarz: AI essentially acts as a personal shopper—one that can instantly analyze preferences, budget, personality traits, or past behavior to produce tailored gift lists. This shifts power toward “delegated decision-making,” where consumers allow AI to narrow choices for them.   

Younger consumers are already comfortable outsourcing this cognitive load. However, as ads enter the picture, these personalized journeys could be shaped by incentives that aren’t always transparent. That creates a new responsibility for platforms to disclose when suggestions are sponsored and for users to develop a more critical lens when interacting with AI-driven recommendations. 

Rice: This is also a great point. The “tools” marketers use to attract customers are constantly evolving, but this seems in many ways to be the next iteration of the Amazon.com suggestions that you find at the bottom of the product page for something you click on when searching Amazon (“buy all x for $” or “consumers also looked at…,” etc.), based on past histories of search and purchase, etc.  

One of the main differences is that now you can create virtually limitless ways to compare products to make the comparisons less taxing (creating lower cognitive loads and stress), which might mean in some cases a higher likelihood of purchase. These idiosyncratic comparisons and prompts lead to the truly unique journeys Andrew is discussing.   

No longer do you have to be beholden to a retailer-specified price range bracket. You could choose your own, or instead ask an AI to list the products representing the best “value” based on consumer reviews, perhaps by asking to list the top ten products by cost per star rating, etc.  

Advertising is becoming more subtle and conversational 

Schwarz: With ads woven directly into AI responses, the traditional boundary between content and advertising blurs. Instead of banner ads, pop-ups, or clearly labeled sponsored posts, recommendations in a conversational thread may feel more like advice than marketing. This has enormous implications for consumer trust.  

Retailers will likely see higher engagement through these context-aware ad placements, but regulatory scrutiny may also increase as policymakers evaluate how clearly sponsored content is identified. The risk is that advertising becomes invisible—something both platform designers and regulators will need to monitor carefully. 

Rice: This is definitely true. I was recently exploring an AI-based tool for choosing downhill skis, but the tool was subtly provided by a single ski brand. I’m not sure that the distribution of ski brands covered was truly delivering the “best overall fit” for a potential buyer as much as the best possible ski in that brand. At least in that case, it was somewhat disclosed. 

It does, however, become an issue if consumers feel misled, but they’d have to notice it first. Still, the advantages are big for retailers, and the numbers don't lie. According to some preliminary data from Black Friday, shoppers using an AI assistant were 60% more likely to purchase.  

Schwarz: This shift is going to reshape multiple layers of the retail ecosystem: 

• Retailers will need to rethink how they show up in AI-driven environments. Traditional SEO, ad bids, and social media strategies won’t be enough. Partnerships with AI platforms may become as important as being carried by major retailers today.
• Because AI tools can compare prices across dozens of retailers instantly, consumers will become more price-sensitive. Retailers may face increasing pressure to offer competitive pricing or unique value propositions, as AI reduces friction in comparison shopping.
• Retailers who integrate AI into their own websites—chat-based shopping assistants, personalized gift advisors, automated bundling—will gain an edge. Consumers are beginning to expect conversational interfaces, and companies that delay will feel outdated quickly.
• As AI tools influence purchasing decisions, consumers and regulators alike will demand clarity around how recommendations are generated. Retailers will need to navigate this carefully to maintain

What I think we are going to see accelerate as we move forward: 

• AI-powered concierge shopping will become mainstream. Within a couple of years, using AI to generate shopping lists, compare prices, and find deals will be as common as using Amazon today.
• Retailers will create AI-specific marketing strategies. Instead of optimizing for keywords, they’ll optimize for prompts: how consumers might ask for products and how an AI system interprets those requests.
• More platforms will introduce advertising into AI models. ChatGPT is simply the first mover. Once the revenue potential becomes clear, others will follow with their own ad integrations.
• Greater scrutiny from policymakers. As conversational advertising grows, transparency rules and labeling requirements will almost certainly
• A new era of “conversational commerce.” Buying directly through AI—“ChatGPT, order this for me”—will become increasingly common, merging search, recommendation, and transaction into a single seamless experience.

I can speak to this on a personal level.  My college-aged son is interested in college football, and I wanted to get him a subscription to a streaming service to watch the games.  However, the football landscape is fragmented across multiple, expensive platforms. 

So, I asked ChatGPT to come up with a series of options. Hulu is $100/month for LiveTV, but ChatGPT recommended a combination of ESPN+, Peacock, and Paramount Plus for $400/year and told me which conferences would not be covered.  What would have taken me hours only took me a few minutes! 

Rice: On the other hand, AI isn’t infallible, and it can lead to sub-optimal results, hallucinations, and questionable recommendations. From personal experience with my ski shopping recently, I found a number of pitfalls.  

First, for very specific questions about a specific model, I would sometimes get an answer for a different ski model in the same brand or a different ski altogether, which is not particularly helpful, or specs that I knew were just plain wrong.  

Secondly, related to Andrew’s point about the conversational tone, I asked some questions that were meant to push the limits of what could possibly be reliable. For example, I asked the AI what the difference in “feel” of the ski would be to the skier between a few different models and brands.  

While the AI gave very detailed and plausible comparisons that were very much like an in-store discussion with a salesperson or area expert, I’m not sure I fully trust when an AI tells me that you can really feel the power of a ski push you out of a turn, this ski has great edge hold, etc. It sounds great, but from where is the AI drawing this info? I’m not convinced it’s fully accurate.  

It also seems like we’re starting to see a migration of Google to be more like AI (thus their AI summaries and full AI Mode). At the same time, we’re also starting to see AI migrating closer to Google as people start using it for product related chats, and companies like Amazon and Walmart have come up with their own AI’s that are specifically focused on the consumer experience.  

I can’t imagine it will be long before companies like OpenAI and their competitors start “selling influence” for the AI discussions to monetize the influential power their engines will have.   

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