The holiday shopping season is here, and AI will shape how people browse and buy even more than it did last year. Adobe expects AI traffic to US retail sites to grow 130% year over year between November 1 and December 31. That's on top of a season in which AI-referred traffic was already climbing from a small base.
Those shoppers aren't just browsing. Adobe also found 69% of shoppers say they're less likely to return an item they bought with an AI assistant's help. When AI does the research well, the sale sticks.
Peak season discovery is increasingly organic. AI pulls from product pages, search results, social content and third-party coverage to decide which products to recommend. The catch: AI can only recommend what it can find and understand. And for most brands, the clearest answer to a shopper's question is buried somewhere on a product page that was written for people scrolling, not a machine reading.
For one of our global footwear brands, we’re seeing increases in product visibility across both traditional and AI results, coming off the heels (pun intended) of updates we’ve actioned to on-page descriptions, featured bullets and benefit elements across PDPs.
Here's an example. I have flat feet. Like, really flat, Fred Flintstone style.
When I shop for running shoes, I don't search for a model name. I ask for shoes with support for over-pronation, real arch support, a roomy toe box and removable insoles. That's how shoppers talk to AI: they describe a problem and ask what solves it.
That's why ‘best for’ and comparison searches have become so important to owning AI results for products. In one study by Am I Cited, comparison content earned 70% more citations than other content types.
An insurance brand working with Journey Further witnessed this first hand; we supported optimizing on-site explainers and coverage option comparisons, plus off-site expert commentary in relevant periodicals. As a result, the brand saw an increase of over 150% in mention rates across Google AI Overview and AI Mode.
Gifting searches are climbing too – queries like ‘best gifts for my spouse’ or ‘what to get someone who loves hiking’ are heading up and to the right across Google Trends and Keyword Planner.
Much of the comparison information shoppers want already lives off-site: on third-party publishers, Reddit threads and review videos. That earned coverage matters. It's often what convinces AI to trust a brand in the first place.
But that doesn't mean it shouldn't also be on your own website. When the same claims appear on the product page, LLMs can check what third parties say against the source. If a review says a shoe has a wide toe box and the product page doesn't mention it, AI has less reason to recommend it for that need.
The goal is consistency. Earned media and owned product pages should describe the same benefits, for the same needs, in similar language.
When consistency is lacking – LLMs still look to provide an answer, but what it produces in those AI results may be outdated or inaccurate data. Many times, this is misinterpreted by the end user as a mistake or ‘AI hallucination’ when in reality it’s simply bad sources.
In 2026, a product page needs a standard flow of direct information that search engines and LLMs can easily digest. Yes, that may mean giving up some editorial space. But it's exactly what machines and agents are looking for.
With Black Friday later this month, there isn't time to rewrite every page. Focus where it counts.
The sooner updates are implemented, the faster they can help populate AI responses closer to Cyber Weekend and the upcoming December Holidays and end of year sales.
This peak, AI will be doing more of the research before a shopper ever reaches your site. Give it the answers up front, on your own pages, and make sure the rest of the web tells the same story.
My feet may be stuck in the Stone Age, but your product pages shouldn't be.
Those shoppers aren't just browsing. Adobe also found 69% of shoppers say they're less likely to return an item they bought with an AI assistant's help. When AI does the research well, the sale sticks.
Peak season discovery is increasingly organic. AI pulls from product pages, search results, social content and third-party coverage to decide which products to recommend. The catch: AI can only recommend what it can find and understand. And for most brands, the clearest answer to a shopper's question is buried somewhere on a product page that was written for people scrolling, not a machine reading.
For one of our global footwear brands, we’re seeing increases in product visibility across both traditional and AI results, coming off the heels (pun intended) of updates we’ve actioned to on-page descriptions, featured bullets and benefit elements across PDPs.
Shoppers now describe a need, not a product
Here's an example. I have flat feet. Like, really flat, Fred Flintstone style.
When I shop for running shoes, I don't search for a model name. I ask for shoes with support for over-pronation, real arch support, a roomy toe box and removable insoles. That's how shoppers talk to AI: they describe a problem and ask what solves it.
That's why ‘best for’ and comparison searches have become so important to owning AI results for products. In one study by Am I Cited, comparison content earned 70% more citations than other content types.
An insurance brand working with Journey Further witnessed this first hand; we supported optimizing on-site explainers and coverage option comparisons, plus off-site expert commentary in relevant periodicals. As a result, the brand saw an increase of over 150% in mention rates across Google AI Overview and AI Mode.
Gifting searches are climbing too – queries like ‘best gifts for my spouse’ or ‘what to get someone who loves hiking’ are heading up and to the right across Google Trends and Keyword Planner.
Reviews and Reddit can't do the job alone
Much of the comparison information shoppers want already lives off-site: on third-party publishers, Reddit threads and review videos. That earned coverage matters. It's often what convinces AI to trust a brand in the first place.
But that doesn't mean it shouldn't also be on your own website. When the same claims appear on the product page, LLMs can check what third parties say against the source. If a review says a shoe has a wide toe box and the product page doesn't mention it, AI has less reason to recommend it for that need.
The goal is consistency. Earned media and owned product pages should describe the same benefits, for the same needs, in similar language.
When consistency is lacking – LLMs still look to provide an answer, but what it produces in those AI results may be outdated or inaccurate data. Many times, this is misinterpreted by the end user as a mistake or ‘AI hallucination’ when in reality it’s simply bad sources.
What an AI-ready product page looks like
In 2026, a product page needs a standard flow of direct information that search engines and LLMs can easily digest. Yes, that may mean giving up some editorial space. But it's exactly what machines and agents are looking for.
- Put value propositions near the top. They should never be buried at the bottom of the page. Make them part of the featured description, benefits and bullet points
- Write bullets around needs, not just features. ‘Medial support for overpronation’ tells AI who the shoe is for. ‘Dual-density midsole’ doesn't
- Answer the comparison questions. Who is this product best for? How does it differ from the rest of your range?
- Keep the basics structured. List color, sizing and variants clearly, alongside the benefits, so nothing has to be inferred.
- Match what earned media says. Use the same terms reviewers and publishers use for the same benefits
- Never forget Product Schema. Both search engine crawlers and AI agents are looking at this to understand reviews, what variants are available, and if there’s a discount offered
Where to start before Black Friday
With Black Friday later this month, there isn't time to rewrite every page. Focus where it counts.
- Pick your hero products. Start with the items you expect to drive most peak revenue
- Ask AI about them. Run the need-based questions your shoppers ask through ChatGPT, Gemini and Google's AI Mode. Does your product come up? Is the description accurate?
- Check where AI is getting its answers. If it’s citing a third-party review or a Reddit thread instead of your product page, that tells you whether the fix belongs on-site or in earned media
- Close the gaps. Where AI misses a benefit or gets it wrong, move that information up the product page and into the bullets
The sooner updates are implemented, the faster they can help populate AI responses closer to Cyber Weekend and the upcoming December Holidays and end of year sales.
This peak, AI will be doing more of the research before a shopper ever reaches your site. Give it the answers up front, on your own pages, and make sure the rest of the web tells the same story.
My feet may be stuck in the Stone Age, but your product pages shouldn't be.
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