Once upon a time, finding a product online was a predictable yet potentially tedious routine: type keywords, click results, and browse websites. With generative AI search, everything has changed with ecommerce product discovery.
An increasing number of shoppers are heading towards the likes of ChatGPT and Perplexity. They ask open-ended questions, upload an image of what they’re looking for, or even take a screenshot of an item on their screen to find it instantly. This new form of product discovery is catching many online businesses off-guard, especially teams whose marketing funnels were built around predictable, keyword-driven search behavior.
In this article, we unpack what generative AI search really is, how it differs from traditional search, and how to adapt your ecommerce strategy accordingly. Before continuing, make sure to first read our broad analysis on the impact of AI on the ecommerce buyer journey.
What is generative AI search?
Generative AI search refers to search experiences powered by large language models (LLMs) and other advanced AI systems that go beyond matching keywords to web pages. Instead, these systems can understand natural language, synthesize information from multiple sources, and generate a cohesive, conversational answer to complex queries.
Let’s define it in ecommerce terms: this means a shopper doesn’t need to know the “right” product keywords — they can simply describe what they need, however they like. For example:
- They could upload a photo of a jacket they saw on Instagram and ask where to buy it.
- They could circle a handbag in a celebrity photo on their phone and immediately get similar product listings.
- Instead of searching for “waterproof hiking boots,” they might ask, “What are the best lightweight hiking shoes for wet, rocky trails in the Tongariro Alpine Crossing in NZ?”
Platforms like Google, ChatGPT, and Perplexity, are integrating these capabilities directly into their search experiences. A single, detailed query or a quick photo upload in any one of these tools will result in a tailored summary of recommendations, guides, and shopping links.
Quick takeaways for your business (and your customers)
- Search is becoming more conversational: brands need content that answers natural questions, not just keyword queries.
- Discovery is multimodal: visual, voice, and text searches blend seamlessly.
- Intent is richer and more specific: AI can connect nuanced needs with the right products, if your content and metadata are ready for it.
Traditional search vs generative AI search
While both aim to connect shoppers with the right products, traditional search and generative AI search differ in how they interpret queries, present results, and guide the discovery process.
| Area | Traditional Search | Generative AI Search |
|---|---|---|
| Query style | Short, keyword-focused phrases (“waterproof boots”) | Natural, conversational queries, referred to as prompts (“What’s the best footwear for hiking in wet autumn weather?”) |
| Results format | Ranked list of links and ads | AI-generated summaries, direct answers, product recommendations, and rich media |
| Understanding of intent | Matches keywords without deep context | Interprets context, preferences, and implied needs |
| Discovery path | Linear: search → click → product page | Conversational and adaptive: search → refine → recommendations → purchase |
| Input types | Mainly text | Text, voice, image, and multimodal inputs |
| Commercial visibility | Focus on keyword rankings | Focus on inclusion in AI-generated responses and recommendations |
According to a Botify study, AI Overviews (AIOs) are becoming a dominant feature, appearing in up to 47% of Google searches. However it is important to note that AIOs are designed to appear when people are looking for answers and explanations, like asking “What shoes should I wear for the Tongariro Alpine Crossing?”. (By the way, we highly recommend doing this if you visiting New Zealand)
What are the differences in search optimization methods between traditional and Gen AI search? Check out this guide on Generative Engine Optimization.
The new product discovery journey: rethinking the path to purchase
Generative AI search transforms the traditional ecommerce journey from a linear funnel into a dynamic, intent-driven exploration. Instead of typing generic keywords, shoppers now ask detailed questions, and AI delivers them personalized, context-aware recommendations instantly.
For example, a query like “How to keep suits from wrinkling on a long-haul flight” doesn’t just return blog articles. It leads to product suggestions such as garment bags, wrinkle-resistant clothing, and travel accessories tailored to the user’s needs.
This means that product discovery is:
- More direct: Shoppers reach the right products faster.
- More exploratory: AI surfaces a wider range of relevant options, encouraging browsing and consideration (as well as more up-selling / cross-selling opportunities for retailers).
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Personalization and multimodal search
The Google Search we know does a pretty good job of taking text, audio, and even images for search. However, generative ai search tools have already integrated these as well as document inputs to build a richer context.
Generative AI search is increasingly expanding its multimodal capability to allow for more intuitive, natural interactions, where a customer might upload a photo of a laptop and ask for matching accessories such as a stand and protective cases.
Personalization extends beyond basic preferences. AI factors in lifestyle, environment, and previous prompts and behavior to curate product suggestions that feel genuinely relevant. Not only that, it explains it to you so that you understand the reasoning behind its recommendations.
Generative AI search impact on the marketing funnel
Traditional marketing funnels assumed a predictable sequence: awareness → consideration → decision → purchase. AI-driven search breaks this mold by introducing “micro-moments”: small, intent-rich interactions that don’t fit into neat stages.
Instead of moving step-by-step, users can directly jump from discovery to decision or revisit multiple stages fluidly. These micro-moments demand marketers rethink how they capture attention and nurture intent.
Here are some implications for ecommerce marketing:
- Higher-quality traffic: Users arriving via AI search tend to spend more time and show stronger purchase intent.
- Increased query volume: AI Overview-enabled searches have already grown by 10%. This may be due to the push from browsing platforms like Google and Perplexity, or it could also be signaling rising adoption and engagement.
- New touchpoints: Brands must ensure visibility in AI-generated responses, not just traditional search results.
Impact of AI agents on search: new and emerging customers
Recently, there has been a lot of discussion around AI agents. Earlier this year, Sam Altman first mentioned the potential of AI agents to “join the workforce” and materially change the output of companies.
Now, we are starting to see a diverse set of applications for AI agents becoming reality, including its use for autonomous shopping. Early adopters are already testing the use of AI agents to find products, compare prices, add items to carts, and even complete purchases. In some instances, they have been highly successful, in other instances not so much.
The key bottlenecks are trust (user comfort) and infrastructure. People need to feel comfortable handing over their credit cards to an agent, and stores need seamless API connections. AJ Gergich, Botify’s Global VP of Consulting Service, believes this won’t hold mainstream adoption back for long. “I predict mainstream adoption (of AI agents) by Black Friday 2026. That’s when the tech gets reliable enough for average consumers and retailers to have proper AI integration built out.”
The introduction of AI agents as potential customers flips ecommerce on its head: in the future, you will be selling not just to humans, but to machines with distinct needs and behaviors.
How to prepare your ecommerce strategy for generative AI search
So then lies the question, do you need to overhaul your entire marketing strategy to adapt to the impact of AI in search? Not necessarily. Here are the key areas you should focus on:
1. Prioritize structured data
Make your content and product information machine-readable. Comprehensive product information ensures your products can answer specific queries. Google’s AI pulls from the most detailed, accurate sources available. Include every relevant product attribute in your schema, such as materials, dimensions, care instructions, compatibility information, and technical specifications. This keeps your content ready for multimodal search.
2. Align content with AI intent
Old SEO habits (stuffing in specs and keywords) won’t cut it. AI responds to intent-based language: “fits easily under airplane seats” will surface in more relevant contexts than “depth 5 inches.”
3. Optimize for live retrieval
Static AI training data means it won’t know about your flash sale unless you tell it. Push real-time updates through tools like IndexNow. For dynamic sales like Black Friday, consider pre-rendering flat HTML versions of your pages so AI can parse them without relying on JavaScript.
4. Test what AI sees
Disable JavaScript or use bot-simulation tools to see your site the way AI does. If critical content do not appear, you need to fix it or risk being invisible to the fastest-growing segment of shoppers. Valido Testing can help you test the product discovery path, from search to conversion.
5. Track visibility, not just clicks
Website clicks alone are yesterday’s metric. Instead, try measuring:
- Brand mentions in AI-generated answers
- Citation frequency
- Share of visibility vs competitors
- Revenue attributed to AI-driven discovery
Conclusion
The adoption of generative ai in search is not just helping people get better answers. This new era of search is opening up new opportunities for ecommerce businesses to expand.
The growing ways people search are providing retailers with richer signals of context and intent than ever before. We can now understand the “why” and not just the “what,” so we can anticipate what a user will need next.
The ecommerce brands that succeed in this new era will be the ones that treat AI as both an influencer and a buyer, thus preparing their content, data, and infrastructure accordingly.
Need help to test, audit and optimize your search experience? Get started with Valido AI today.
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E-commerce & UX consultant, helping businesses optimize digital experiences and unlock growth with AI-powered tools and strategies.