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lsu-experts-break-down-artificial-intelligence-boom-behind-holiday-shopping-trends

LSU Experts Break Down Artificial Intelligence Boom Behind Holiday Shopping Trends

Associate Professor

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 the E. J. Ourso College of Business Behavioral Research Lab, share their insights 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 multiple pages of results, users—especially Gen Z—are skipping 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.  

This translates to seemingly paradoxical situations for providers: they see drops in click-through rates and visitors/leads, yet revenue increases because the visitors are “better.”  

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,” in which consumers allow AI to narrow their choices.   

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 you can now create virtually limitless ways to compare products, making comparisons less taxing (reducing cognitive load and stress), which may, in some cases, increase the likelihood of purchase. These idiosyncratic comparisons and prompts lead to the truly unique journeys Andrew is discussing.   

You no longer have to be beholden to a retailer-specified price range. 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 the distribution of ski brands covered was truly delivering the “best overall fit” for a potential buyer, rather than 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 Black Friday data, shoppers using an AI assistant were 60% more likely to make a 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 instantly compare prices across dozens of retailers, 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 increasingly expecting conversational interfaces, and companies that delay will quickly feel outdated. 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 streaming subscription to watch the games.  However, the football landscape is fragmented across multiple, expensive platforms. 

I asked ChatGPT to generate a series of options. Hulu is $100/month for Live TV, but ChatGPT recommended a combination of ESPN+, Peacock, and Paramount+ for $400/year and identified 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 my recent ski shopping experience, I encountered several pitfalls.  

First, for very specific questions about a specific model, I sometimes received answers for a different ski model in the same brand, or for a different ski altogether, which was not particularly helpful, or specs I knew were just plain wrong.  

Secondly, regarding Andrew’s point about the conversational tone, I asked questions intended to push the limits of what could be considered reliable. For example, I asked the AI to describe the difference in “feel” of the ski for the skier among several 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 where is the AI sourcing this information? I’m not convinced it’s fully accurate.  

It also seems we’re starting to see Google shift toward a more AI-centric approach (e.g., AI summaries and full AI Mode). At the same time, we’re also starting to see AI migrate closer to Google as people use it for product-related chats, and companies like Amazon and Walmart have developed their own AI that is specifically focused on the consumer experience.  

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


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Treat AI as a Teammate—or Risk Falling Behind

AI is shifting from back-office tool to frontline collaborator, "We are witnessing a key inflection point in how organizations work," says LSU professor Andrew Schwarz.

He argues the business case is now clear: AI boosts the quality of ideas and expands who gets to contribute, acting less like software and more like a creative partner. He adds that organizations that embed AI "as a teammate will lead," while those that treat it "as simply a cost-saver risk falling behind."

That shift, he says, reaches deep into org charts and workflows. Schwarz notes that AI can flatten expertise silos, help less-experienced employees operate closer to expert levels, and spark cross-functional thinking that blends technical and commercial insight. Leaders, he said, must "rethink structures, roles and workflows — placing AI at the heart of how teams collaborate, not simply at the edge."

Technology deployment alone won't deliver those gains, "it requires cultural and capability investment," Schwarz said. The priority, in his view, is to "build collaborative ecosystems where human talent and AI capabilities co-create value," invest early to make the "human-plus-AI" model the default, and tap into academic partnerships: "those companies that partner with universities, such as LSU, will have an even greater advantage."

Schwarz also urges guardrails as adoption accelerates. He points to the need for transparency, accountability, fairness, and continuous skill development so the transition "enhances human agency, fosters inclusion, and delivers sustainable value for all stakeholders."

His bottom line is urgent and straightforward: "When AI joins the team, better ideas truly surface. Let's prepare our organizations to make that transition, and lead from the front."


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