How GenAI is rewriting the rules of product search

From ChatGPT to Gemini, from Rufus to Sparky, conversational AI is fundamentally transforming how consumers research and discover products online. The age of typing keywords into search boxes is evolving into natural conversations with AI assistants that provide answers to user prompts. Here's what we know so far about how answer engines are impacting the purchase process.

The hidden shift in shopping behavior

With the advent of answer engines, consumers are increasingly starting product research with conversational questions rather than keywords. The evidence is striking: studies show as much as 60% of searches leading to zero-clicks as users get answers directly from AI summaries, with Gartner predicting a 25% decline in traditional search volume by 2026.

Here's what's fascinating: answer engines typically operate higher in the purchase funnel, before shoppers even know they need a specific product. According to recent eMarketer analysis, 34% of non-shopping conversations still result in product recommendations. You're reaching people who don't yet know they're in-market.

AI assistants now provide curated recommendations instead of overwhelming search result pages. Shopping journeys compress as AI synthesizes information from multiple sources instantly. Trust has shifted from scanning reviews manually to relying on AI's interpretation of collective feedback.

This behavioral shift demands a complete rethinking of how brands approach product discovery.

The new AI-powered shopper journey – Understanding the challenge

Search now just got a lot more complicated. In addition to optimizing for traditional search with SEO, answer engines (AEO) are quickly becoming commonly used tools that also require your consideration. And this means rethinking the shopper journey. Additionally, less than 10% of page 1 search results show up in answer engine responses, as they are evaluating your content differently compared to traditional search engines.

Awareness: The invisible battle
Today's shoppers are having full conversations with AI, yet they get only 3-5 personalized recommendations instantly. Here's the kicker: 50% of AI prompts are just information requests, but as stated previously, you're still competing for product visibility before shoppers even realize they're shopping.

Consideration: The 10-second decision
Remember comparing products across multiple searches and multiple tabs? AI eliminates that, synthesizing thousands of data points and explaining exactly why one product fits better. Your product descriptions aren't just competing for a spot in search results anymore, they're also competing to be the answer AI delivers directly to shoppers.

Purchase: The vanishing checkout
Features like "Shop with Rufus" let customers buy directly within their AI conversation. No clicking through, no cart to abandon. Once AI makes its recommendation, there's no second chance. The decision is already made.

Post-Purchase: The instant impact
Customer feedback used to trickle in weekly. Now AI ingests reviews in real time, immediately adjusting future recommendations. One formula change that disappoints customers, one surge in negative reviews, and you invisibly drop from AI recommendations overnight. The feedback loop that took weeks now happens in seconds.

The 9 Essential moves for AI discovery success

While it's early and much is still to learn, there are several best practices for category teams to take into consideration:

The quick preview:

1. Be AI-visible – Ensure every PDP has complete, accurate data and proper schema markup so models can easily surface it. Because LLMs pull information from many sources, brands must make product content, benefits, and differentiators fully machine-readable and consistently syndicated across all retailers.
2. Go beyond product titles – Because LLMs can interpret intent and maintain shopper-specific context over longer interactions, optimizing titles for keywords is no longer enough. Product descriptions need to address customer pain points and clearly explain why a particular shopper would choose the product. In the past, a strong title might have been sufficient to rank. Now detailed and structured attributes are required, such as product type, materials, scent or flavor, size, usage benefits, or key features like "built-in applicator" or "quick-dissolve formula." These specifics help models understand and match the product to the shopper's needs.
3. Think like a shopper, not a machine – Write content that answers the real questions customers ask. For example "what's the best espresso machine that makes great lattes for my small kitchen?"
4. Leverage social proof – Maintain healthy review volume and positive sentiment; LLMs read them as authority signals. Generative Al tools frequently summarize or reference User-Generated Content (UGC), customer reviews, and earned media like expert reviews or editorial product roundups.
5. Focus on conversational SEO – Prioritize natural, conversational phrasing over rigid keywords. For example, content should support queries like "which snack bar will keep me full between meals" rather than a keyword-driven phrase like "high-protein snack bar." A good rule of thumb is to write the way someone would ask a friend or an expert. Content should be rich enough for the model to infer intent and surface the right product recommendations.
6. Redefine visibility – As retail media merges with Al search, paid and organic placements must work together. While the new AEO media model is still developing, indications suggest a blurring of the line between paid and organic placements. When shoppers ask conversational questions, the Al is likely to blend paid and organic listings into a single recommendation set.
7. Create smarter ads – Use data to highlight meaningful product differentiators, such as fast-absorbing texture, long-lasting hydration, or a lightweight, non-greasy formula. Al-driven shopping environments reward relevance, authority, and clarity over volume. For brands, this means every ad impression should do more than promote; it should clearly show why your product is the one customers can trust.
8. Integrate incrementality performance data – Combine retail media, organic and Al insights to measure true iROAS. As Al reshapes discovery and purchase behavior, brands need a single connected performance platform that can measure true incrementality as new data signals and formats emerge.
9. Partner with retailers – Collaborate with retailers like Amazon to ensure your SKUs are included in both Al ecosystems.

These aren't just tactics - they are fundamental shifts in how commerce works. Read the full whitepaper for specifics on how to apply these recommendations on each stage of the shopper journey.

‍

Get the Complete AEO Playbook → Download our whitepaper