The way consumers search is changing faster than the industry expected. This holiday season, many shoppers are looking for gifts inside AI platforms, rather than retailer sites or traditional search. They are asking natural questions like:
“Find me a cruelty-free skincare gift for sensitive skin under $100.”
“What are good gift ideas for a three-year-old that are safe and durable?”
“What are the safest, nontoxic treats for my Golden Retriever?”
This shift is already measurable. Adobe Digital Insights reports a 4,700% year-over-year increase in retail visits driven by AI assistants between July 2024 and July 2025. At the same time, click-through rates from SEO have dropped 34% as users bypass the search results page entirely. eMarketer reports 47% of brands have no idea whether they appear in AI-driven discovery at all.
The platforms know this shift is accelerating. Google’s recent decision to add conversational shopping and AI-mode ads just weeks before the holidays shows how quickly consumer behavior is moving. Brands must adjust too.
Despite the complexity behind AI systems, three simple signals determine which products get recommended: trust, relevance, and extractability. These signals are the backbone of how AI decides what to surface, and matter as much as packaging, price, or placement.
1. Trust: The model’s instinct about which information is dependable
AI systems develop a sense of which sources to believe during training. Domains with consistent verification signals gain more weight because the model has learned they usually publish accurate information.
This is why leading retailers, including Ulta, Sephora, Target, Amazon, and Bloomingdale’s, rely on independent verification partners for the claims displayed on their digital shelves. Verified domains act as trust anchors. When a model must choose, it selects the product backed by clearer and more reliable sources.
Trust often determines whether you are included in the answer at all.
2. Relevance: How well your product matches the shopper’s question
AI assistants answer based on meaning, not keywords. When a shopper asks for “eczema-safe moisturizer” or “gluten-free protein bars,” the system retrieves products whose attributes clearly map to those concepts.
Relevance depends on using consistent claims across every channel you sell in—consistency is heavily prioritized. When multiple sources concur, this repeated confirmation strongly reinforces your product is the right choice.
Missing or inconsistent attributes keep your product out of the candidate pool.
3. Extractability: How easy it is for AI to read and use your product data
Even accurate information gets ignored if it’s hard for AI to parse. Clean structure, consistent formatting, and machine readability significantly increase the likelihood your product will be selected.
Brands improve extractability by adding structured markup for details like ingredients, materials, and benefits so retrieval systems can interpret it without ambiguity.
Clear structure anchors the attention of the large language model, giving your product an advantage. Extractability is often the deciding factor when competing products meet the same need.
AI RECOMMENDATIONS SHAPE BEHAVIOR
Algorithms do more than respond to consumers. They influence them.
We see this in language, where content moderation has led millions of people to adopt new vocabulary. The same pattern is emerging in commerce. If AI consistently recommends a certain moisturizer, probiotic, or baby product, shoppers begin to trust those recommendations and carry those preferences into stores.
Optimizing for trust, relevance and extractability goes beyond improving digital performance. It shapes real-world buying behavior.
A PRACTICAL PLAYBOOK FOR THE HOLIDAY WINDOW
Even with peak season here, brands can still make meaningful progress with these four steps:
1. Structure your data for machine and human audiences
• Fix blocked pages or missing product schemas, and use standard formats like JSON-LD that AI can parse reliably.
• Keep consumer-facing PDPs simple while storing deeper technical details, ingredients, and safety information in underlying schemas.
• Clean up formatting and refresh retailer feeds weekly, since AI systems prioritize recency.
Example: A candle brand can keep the PDP simple for shoppers while storing allergen, VOC, and material data in structured markup that AI can read.
2. Align product claims everywhere you sell
• Match titles, claims and benefits across DTC sites, retailer PDPs, and marketplaces.
• Remove conflicting or outdated language that can weaken trust.
Example: If one PDP says “cruelty-free” and another says “not tested on animals,” unify the phrasing so AI sees one consistent claim.
3. Map your data to real shopper intent
• Identify the attributes consumers care about most in your category.
• Encode those attributes in machine readable fields; add supporting evidence where possible.
Example: For baby toys, encode safety standards like ASTM or CPSC in your structured data so AI can confirm the claim.
4. Build machine-readable authority with credible certifications and verification signals
• Encode ingredients, materials, certifications, and testing outcomes in structured fields so AI can verify your claims without guessing.
• Keep claim language consistent across channels to strengthen authority.
• Use references to third-party standards, testing, or retailer badges. AI gives more weight to claims it can trace back to trusted sources.
Example: A sensitive skin serum should encode “fragrance-free,” “eczema-safe,” dermatologist testing details, and any third-party certifications directly in schema.
5. Use a tool that monitors, optimizes, and implements the work end-to-end
• Choose a tool that goes beyond generic visibility tracking, looks at each SKU individually, and helps you implement structured data improvements.
• Prioritize systems that strengthen your authority signals product by product, not just surface-level optimizations.
• Look for tools that measure real outcomes, like increased visibility in AI or higher conversion, so you can measure ROI.
Consumer discovery is changing faster than most brands are prepared for. But there is still time. By reinforcing trust, relevance, and extractability now, brands can stay visible in AI-driven search this season and build a long-term foundation for every channel where AI shapes consumer decisions.
Kimberly Shenk is cofounder and CEO of Novi.
