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By AI Marketing Entrepreneurship · Published September 29, 2026
Prepared with AI assistance using linked primary sources. Product information reviewed September 29, 2026.
An online store can prepare for AI-assisted holiday shopping by making product information clear, current, and easy to verify. The priority is a product page that answers a real buying question and supports the next step when a shopper arrives.
In its 28 September holiday forecast, Adobe expects traffic from AI services to US retail sites to rise 130% year over year during November and December 2026. Adobe measures this traffic through shoppers clicking a link. The figure is a forecast, applies to US retail traffic, and does not predict an individual store's sales.
A shopper buying for someone else may know the recipient's needs without knowing your product terminology. They might be looking for a washable gift for a new parent or a compact coffee accessory for a small kitchen.
Review customer enquiries, site searches, and return reasons to identify the questions your business actually receives. Group them around suitability, dimensions, materials, delivery, and care. Use that evidence to choose which pages to improve first.
For a hypothetical ceramic mug, useful information includes capacity, dimensions, dishwasher suitability, packaging, and the dispatch policy. Replace broad claims such as “the perfect gift” with details a buyer can use to assess fit.
Put essential information in ordinary page text, close to the relevant product. A shopper should not need to inspect an image or navigate several policy pages to understand a basic specification.
Our recommended page review has five checks:
Use accurate structured product information where your store supports it, and keep it consistent with the page. Technical markup cannot repair contradictory descriptions or an outdated offer.
When several products solve a similar problem, explain the differences in terms a buyer understands. A short comparison of size, intended use, and care requirements can be more helpful than several paragraphs of repeated promotional language.
For example, a retailer selling two bags could describe which fits a laptop and which is suited to a short outing. Only include claims supported by product specifications or actual testing.
This kind of comparison is an editorial recommendation. Adobe's forecast does not establish that a particular page format will earn an AI recommendation. Its value is that it helps the visitor make a better decision wherever the visit originated.
Assign someone to check delivery deadlines, promotional end dates, and stock messages during the holiday period. Make sure expired promises disappear from prominent landing pages and campaign copy.
For businesses outside the United States, use local sales history and delivery constraints when making a plan. A US market forecast can signal a topic worth watching without becoming a forecast for Dutch customers or another regional audience.
Create a baseline for identifiable AI referral visits, then monitor engagement and orders alongside the traffic. Record where referral information is missing or grouped into another channel.
Track the questions visitors still ask after reading the page. Fewer avoidable enquiries may indicate clearer information even before referral traffic changes. A rise in visits without relevant engagement may point to a mismatch between the recommendation and the landing page.
No. The forecast concerns year-over-year AI referral traffic to US retail sites during the holiday season.
No. Google says its AI search features follow established SEO principles, and inclusion is not guaranteed. Clear information improves usefulness without securing a placement.
Choose a small set of important products and answer the questions that most often delay a purchase. Keep the answers accurate as promotions and stock change.
For search eligibility, see Google's guidance on AI features and websites. Begin with five product pages, record what changed, and review their performance after publication.
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