March 31, 2026
stokkete/Depositphotos.com
How Important Is it For Retailers To Engage With AI Models Proactively?
In an extensive report issued by end-to-end platform and network solutions company Rithum, data points surrounding today’s consumer and their engagement with AI tools during the purchase journey were examined.
One central theme presented was that, especially as AI moves from its nascent stage in retail integration into an early form of maturity, it is vital for brands and businesses to control their own narratives by proactively engaging with LLM platforms.
“LLMs are powerful tools to attract more customers — as long as your product content, pricing, inventory data, and other details are accurate and consistent across where LLMs look. If you don’t control the data story about your products, then AI will make one up for you — whether it’s right or not,” the report authors began.
Stating that the emergence and solidification of AI in the purchasing journey represents a “new kind of ecommerce land grab,” the Rithum report suggested that logo recognition — and even customer loyalty — may be waning in overall importance compared to this new and disruptive force.
“A new brand can displace a household name when its product data gives AI better information, and the retailers losing ground often have no idea it’s happening. A brand can drop from a top citation to near-invisible in a matter of weeks when a competitor publishes a more structured product page, a Reddit
thread surfaces full of complaints, or a pricing inconsistency makes AI stop trusting the data,” the report read.
“By the time the pattern shows up in revenue data, the LLM has already formed a recommendation habit. A shopper who’s bought from an AI-recommended competitor once has now taught the algorithm what they buy — and the next recommendation builds on the last. The brand that wasn’t in the first recommendation is increasingly unlikely to appear in the ones that follow,” it added.
Other interesting data points brought forth by the report:
- LLMs appear to be creating a new product research and recommendations ecosystem beyond traditional search methods: About one-fifth (19%) of users currently buy from brands they’d never heard about previously, about 13% consider themselves more likely to switch retailers or products following engagement with an LLM, and about one-third (32%) spend less time browsing other websites when working with an LLM.
- Brand websites may be losing traction overall: Over half of shoppers (53%) polled indicated that they currently trust AI tools equally as much as brand websites. When double-checking AI data, search engines are the most common option (28%), followed by online reviews (19%), friends and family (17%), and one’s own personal experience (17%). Brand websites come in at just 5%.
- AI usage for shopping purposes roughly increases alongside income lines: The percentage of respondents who answered that they’d used an AI tool for shopping moved upward, roughly in lines with increased income. Approximately 56% of those earning less than $30,000 annually said they did so, 70% of those earning $30,000 to $49,999, 68% of those earning between $50,000 and $69,000, 77% of those earning $70,000 to $99,999, 84% of those earning $100,000 to $149,999 annually, and 81% of those earning above $150,000 per year.
“AI offers up information about your brand no matter what. The question is: Did you design your story, or did you leave it to chance? By the time a bad recommendation lands, you’ve already lost credibility,” the report concluded.
Quinnipiac, Walmart Data Suggests Trust in (Some) Agentic AI Streams May Not Be as Solid as Suggested
The report also comes as Quinnipiac released its own major study into American trust (or lack thereof) surrounding AI tools. And while that poll did not exclusively name AI-assisted shopping as a measurable activity, it did note that a majority of those polled (51%) had used an LLM to research topics of interest (which may include shopping).
However, one note of contrast between Rithum’s findings and Quinnipiacs, at least loosely: While Rithum suggested that trust in AI was rising, the Quinnipiac numbers didn’t show as strong of a case in this regard. A full 76% of Americans polled stated that businesses were not being transparent enough in their AI usage, and nearly the same cohort (74%) said that government was failing in its duty to properly regulate AI.
“Americans are not rejecting AI outright, but they are sending a warning. Too much uncertainty, too little trust, too little regulation, and too much fear about jobs,” said Dr. Chetan Jaiswal of the Quinnipiac University School of Computing and Engineering.
Further, recent Walmart data suggested that ChatGPT conversion rates as compared to in-house e-comm conversion rates are abysmal, with the blue-and-yellow brand pulling back to refocus efforts on its own Sparky agent, and a more curated, customized linking of ChatGPT interest to its own sales channel.
BrainTrust
Carlos Arámbula
Principal, Growth Genie Partners
Bhargav Trivedi
Solutions Architect, Bloomreach
Recent Discussions
Discussion Questions
How important is it, in your view, for retailers to engage proactively with major AI models? Is the importance currently overstated, and what’s the motive if so?
Do you believe Americans currently, broadly speaking, trust or mistrust LLMs when it comes to the shopping journey?
Are the conversion and engagement rates currently being headlined realistic? Are you inclined to believe Walmart’s numbers?
Poll
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AI is already transforming how consumers discover products. Although its direct impact on conversions remains limited, its growing influence on visibility and purchase consideration makes proactive engagement a present-day customer service priority rather than a future concern. Retailers should begin investing in structured data, consistency, and overall AI readiness to ensure their products are accurately represented within AI-driven recommendation systems. While the urgency is real, the short-term impact is often overstated by platforms and vendors who benefit from accelerating adoption, even as transaction capabilities remain underdeveloped.
Consumers today demonstrate a form of behavioral trust in AI—they rely on it for product discovery and decision-making—yet remain psychologically cautious. Findings from Quinnipiac University highlight ongoing concerns about transparency and regulation, even as usage continues to grow. This creates a dynamic in which AI significantly shapes consumer consideration, despite the absence of full trust.
The article’s data linking income levels to AI shopping usage, combined with AI’s limited impact on conversion, supports the credibility of the figures reported by Walmart. Walmart’s response should not be viewed as a retreat from AI, but rather as a strategic recalibration—prioritizing control over the transaction layer while continuing to leverage AI for upstream demand generation.
Retailers can’t treat LLMs as just another channel; they are rapidly becoming the decision layer in commerce. Proactive engagement is less about hype and more about controlling structured product data, pricing consistency, and content quality, because that is what models rely on to rank and recommend.
That said, the urgency is somewhat overstated today since discovery is shifting faster than actual conversion behavior. Consumers are experimenting with AI, but trust remains conditional and tied to context.
LLMs drive intent, but profitability matters equally, as rising AI costs demand careful ROI evaluation alongside conversion gains for long term sustainability.
Retailers engaging proactively with major AI models is quickly shifting from a future-looking experiment to a present-day necessity. As the RetailWire discussion highlights, AI is already influencing product discovery and consideration, even if its direct impact on conversions is still emerging. That alone makes proactive engagement important — because if AI models are shaping what customers see first, they are already influencing purchase decisions.
More broadly, customer behavior data makes the direction clear. Recent research shows 56% of U.S. consumers used generative AI during the 2025 holiday season, with many using it specifically for product comparisons, pricing research, and purchase decisions. Additionally, 61% of consumers have already used AI tools like ChatGPT for shopping, and adoption continues to grow rapidly. That signals that consumers themselves are pulling retailers into this space — particularly for complex or considered purchases, where AI helps narrow choices and reduce decision friction.
Trust in AI remains mixed — but it is improving. Adobe data shows 64% of shoppers using AI are satisfied with recommendations, and more than 55% actively click on AI-generated links, suggesting growing confidence in AI-guided shopping journeys. At the same time, broader surveys indicate Americans still want guardrails and oversight, reflecting cautious adoption rather than blind trust. This aligns with what we’re seeing in retail: consumers are willing to use AI for discovery and research, but they still want human validation for final decisions, especially for higher-value purchases.
As for conversion and engagement rates — some skepticism is warranted. Early metrics from retailers like Walmart are encouraging, but still represent early-stage adoption. Even research evaluating AI shopping performance shows that while models are improving, there remains a meaningful gap between AI performance and consumer expectations, particularly in complex shopping scenarios. In other words, the engagement numbers may be real, but long-term conversion impact is still evolving.
My perspective is that the customer insights make the answer fairly clear. Consumers are increasingly using AI to help make purchase decisions — especially when purchases are more complex, involve research, or require comparisons. Retailers don’t need to decide whether this shift is coming; customers are already making that decision for them.
That means retailers should proactively ensure their products, content, and brand positioning are accurately represented in AI environments — whether through structured data, enriched product content, or partnerships with AI-driven discovery platforms. The importance may occasionally be overstated in terms of immediate ROI, but strategically, the direction is unmistakable.
In short, AI shopping assistants are becoming another “Digital Front Door.” And as with search, marketplaces, and mobile before it, retailers that engage early will shape discovery — while those that wait risk becoming invisible.
AI is quickly becoming a new layer of product discovery and it is changing how customers find and choose products. When AI tools start recommending products, visibility is no longer controlled only by brand strength or marketing spend. It is increasingly shaped by the quality, consistency, and structure of product data.
The priority for retailers and brands is to take control of their data narrative. Accurate product content, pricing, and availability must be consistent across all channels where AI systems pull information.
At the same time, trust remains a key factor. Retailers that combine strong data discipline with transparent customer experiences will be better positioned as AI becomes a more influential part of the purchase journey.
Retailers should engage proactively with AI, but the real focus should be on fundamentals like clean product data, structured catalogs, and API exposure. AI models rely on this, not marketing hype. It’s less about immediate impact and more about readiness. Those who fix the basics now will benefit as AI-driven discovery matures.
This is part of the wider democratization of retail. In the past, winning share of eyeballs meant having the biggest budgets to fund things like the best shelf placement or engineering strong search results. Now, the brands and retailers that win – at least in the agentic sphere – aren’t necessarily the ones with the highest spend: they’re the ones that are most creative, have the cleanest data feeds, and are able to most effectively engage with AI. Does this mean all big brands are under threat? No, because a lot of discovery is still made via traditional retail channels. And, over time, AI models will likely monetize discovery mechanisms – so will just become another advertising and media play.
This recalls for me the classic Mary Meeker slide comparing where customers were spending time and where advertisers were spending money. It took years for the ad dollars to better align with where shoppers were spending time. It’s a process. Prompt ready product descriptions are keywords of AI search. Call up the writers of the Sears catalog product descriptions. That’s what AI search is looking for. Details and context not words for indexing.
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AI is already transforming how consumers discover products. Although its direct impact on conversions remains limited, its growing influence on visibility and purchase consideration makes proactive engagement a present-day customer service priority rather than a future concern. Retailers should begin investing in structured data, consistency, and overall AI readiness to ensure their products are accurately represented within AI-driven recommendation systems. While the urgency is real, the short-term impact is often overstated by platforms and vendors who benefit from accelerating adoption, even as transaction capabilities remain underdeveloped.
Consumers today demonstrate a form of behavioral trust in AI—they rely on it for product discovery and decision-making—yet remain psychologically cautious. Findings from Quinnipiac University highlight ongoing concerns about transparency and regulation, even as usage continues to grow. This creates a dynamic in which AI significantly shapes consumer consideration, despite the absence of full trust.
The article’s data linking income levels to AI shopping usage, combined with AI’s limited impact on conversion, supports the credibility of the figures reported by Walmart. Walmart’s response should not be viewed as a retreat from AI, but rather as a strategic recalibration—prioritizing control over the transaction layer while continuing to leverage AI for upstream demand generation.
Retailers can’t treat LLMs as just another channel; they are rapidly becoming the decision layer in commerce. Proactive engagement is less about hype and more about controlling structured product data, pricing consistency, and content quality, because that is what models rely on to rank and recommend.
That said, the urgency is somewhat overstated today since discovery is shifting faster than actual conversion behavior. Consumers are experimenting with AI, but trust remains conditional and tied to context.
LLMs drive intent, but profitability matters equally, as rising AI costs demand careful ROI evaluation alongside conversion gains for long term sustainability.
Retailers engaging proactively with major AI models is quickly shifting from a future-looking experiment to a present-day necessity. As the RetailWire discussion highlights, AI is already influencing product discovery and consideration, even if its direct impact on conversions is still emerging. That alone makes proactive engagement important — because if AI models are shaping what customers see first, they are already influencing purchase decisions.
More broadly, customer behavior data makes the direction clear. Recent research shows 56% of U.S. consumers used generative AI during the 2025 holiday season, with many using it specifically for product comparisons, pricing research, and purchase decisions. Additionally, 61% of consumers have already used AI tools like ChatGPT for shopping, and adoption continues to grow rapidly. That signals that consumers themselves are pulling retailers into this space — particularly for complex or considered purchases, where AI helps narrow choices and reduce decision friction.
Trust in AI remains mixed — but it is improving. Adobe data shows 64% of shoppers using AI are satisfied with recommendations, and more than 55% actively click on AI-generated links, suggesting growing confidence in AI-guided shopping journeys. At the same time, broader surveys indicate Americans still want guardrails and oversight, reflecting cautious adoption rather than blind trust. This aligns with what we’re seeing in retail: consumers are willing to use AI for discovery and research, but they still want human validation for final decisions, especially for higher-value purchases.
As for conversion and engagement rates — some skepticism is warranted. Early metrics from retailers like Walmart are encouraging, but still represent early-stage adoption. Even research evaluating AI shopping performance shows that while models are improving, there remains a meaningful gap between AI performance and consumer expectations, particularly in complex shopping scenarios. In other words, the engagement numbers may be real, but long-term conversion impact is still evolving.
My perspective is that the customer insights make the answer fairly clear. Consumers are increasingly using AI to help make purchase decisions — especially when purchases are more complex, involve research, or require comparisons. Retailers don’t need to decide whether this shift is coming; customers are already making that decision for them.
That means retailers should proactively ensure their products, content, and brand positioning are accurately represented in AI environments — whether through structured data, enriched product content, or partnerships with AI-driven discovery platforms. The importance may occasionally be overstated in terms of immediate ROI, but strategically, the direction is unmistakable.
In short, AI shopping assistants are becoming another “Digital Front Door.” And as with search, marketplaces, and mobile before it, retailers that engage early will shape discovery — while those that wait risk becoming invisible.
AI is quickly becoming a new layer of product discovery and it is changing how customers find and choose products. When AI tools start recommending products, visibility is no longer controlled only by brand strength or marketing spend. It is increasingly shaped by the quality, consistency, and structure of product data.
The priority for retailers and brands is to take control of their data narrative. Accurate product content, pricing, and availability must be consistent across all channels where AI systems pull information.
At the same time, trust remains a key factor. Retailers that combine strong data discipline with transparent customer experiences will be better positioned as AI becomes a more influential part of the purchase journey.
Retailers should engage proactively with AI, but the real focus should be on fundamentals like clean product data, structured catalogs, and API exposure. AI models rely on this, not marketing hype. It’s less about immediate impact and more about readiness. Those who fix the basics now will benefit as AI-driven discovery matures.
This is part of the wider democratization of retail. In the past, winning share of eyeballs meant having the biggest budgets to fund things like the best shelf placement or engineering strong search results. Now, the brands and retailers that win – at least in the agentic sphere – aren’t necessarily the ones with the highest spend: they’re the ones that are most creative, have the cleanest data feeds, and are able to most effectively engage with AI. Does this mean all big brands are under threat? No, because a lot of discovery is still made via traditional retail channels. And, over time, AI models will likely monetize discovery mechanisms – so will just become another advertising and media play.
This recalls for me the classic Mary Meeker slide comparing where customers were spending time and where advertisers were spending money. It took years for the ad dollars to better align with where shoppers were spending time. It’s a process. Prompt ready product descriptions are keywords of AI search. Call up the writers of the Sears catalog product descriptions. That’s what AI search is looking for. Details and context not words for indexing.