AI v.s. traditional search: How product discovery is changing

AI-powered search engines are transforming how we find information, moving from link-based results to direct, conversational answers. Unlike traditional search engines like Google, AI-driven tools summarize multiple sources, changing user behavior and search strategies. This article explores the key differences and their impact on online search.

AI search v.s. traditional search – key differences

Conversational answers vs. list of links: Traditional search engines like Google and Bing return a ranked list of webpage links for users to click through. In contrast, AI-driven search tools (e.g. ChatGPT’s search mode or Perplexity) generates a direct answer or summary by analyzing multiple results behind the scenes.

For example, ChatGPT’s search will synthesize information from about a dozen web results to answer a query, rather than just showing ten blue links. This means users get a single, conversational response instead of a full SERP (search engine results page).

Use of search index and sources: Beyond just how results are presented, another key distinction lies in how AI and traditional search engines select and rank their sources. AI search engines often leverage existing search indexes by applying their own filtering on sources. ChatGPT’s web search uses Bing’s search index, yet it custom selects which sources to trust and summarize, leading to a different ordering of information than Google.

In practice, ChatGPT’s results for a query might resemble Bing’s results more closely than Google’s, but with a unique mix of sources chosen for the answer. Authoritative content sites are favored over less-established pages in these AI summaries, so the content drawn from might not mirror the exact ranking of a Google SERP.

Contextual and detailed responses: AI tools excel at understanding natural language and context. They can interpret longer, conversational queries and follow-up questions, providing nuanced answers. Traditional search engines also try to interpret intent, but AI chat systems can maintain context across multiple questions.

For instance, you can ask an AI search, “What’s the best laptop for graphic design under $1000?” and then follow up with “Does it support 32GB RAM?” – the AI will remember the context. This gives AI searches a personalized, interactive “assistant” feel rather than the one-and-done query style of traditional search.

Fewer ads and purely organic results: Another major difference is how AI search engines handle monetization, particularly in terms of advertisements and organic rankings. In a typical Google or Bing search, paid ads and sponsored product listings often occupy the top of results. By contrast, many AI-driven search experiences currently show minimal or no ads.

ChatGPT’s search interface, for example, doesn’t display ads at all – it’s purely organic content. Microsoft’s Bing Chat and Google’s experimental Search Generative Experience do include ads, but they integrate them more subtly into the AI-generated answer (labeled as “sponsored” content).

Overall, users of AI search are less exposed to paid results, and businesses cannot simply pay to “be at the top” of an AI answer the way they can with search ads. This raises the stakes for organic SEO and quality content in AI results.

Different content emphasis: Traditional search often directs users to a mix of content – product pages, category pages, review sites, etc. AI search tools tend to prefer in-depth informational content (guides, reviews, comparisons) when formulating answers.

For example, one study found that ChatGPT’s search mode consistently pulled information from long-form “Top 10” style articles and authoritative review sites, rather than brand product pages. In tests, a company’s own product page that ranked top 3 on Google did not get picked up by ChatGPT at all; instead, ChatGPT summarized content from lengthy third-party articles.

This indicates AI search may be more skeptical of promotional content and leans toward neutral, well-rounded sources. Businesses need to recognize that an AI might mention their product via a review on another site, rather than pulling from the brand’s official page.

User interaction model: With Google, users typically scan results, click one or several links, and refine their query if needed. With AI-driven search, the interaction is more conversational – users often pose a question and then might ask follow-up questions to refine the answer, instead of performing a brand new search. The AI acts like a virtual shopping assistant or guide.

This difference means users retrieve information with fewer clicks and more dialogue. They might rely on the AI to do the comparison shopping or product research steps for them (asking the AI to explain or compare products) rather than clicking multiple websites themselves.

Changing user behavior and search intent in the AI era

As AI search becomes more integrated into daily internet use, it is also reshaping how people search for and engage with information. Users are adopting a more conversational style when searching via AI. Queries tend to be longer and more detailed – the average ChatGPT prompt is 23 words (often a full question or task description), whereas the average traditional search query is about 4–5 words.

About 70% of prompts entered into ChatGPT are unique or rarely seen in traditional search engines. This means people are not only asking new types of questions but also phrasing them in ways they wouldn’t with Google. Instead of simply searching “durable sofa,” they might ask: "I have a small apartment, two kids, and a dog—what kind of sofa fabric is most durable?"

This more complex, conversational query reveals a specific intent (finding a durable sofa) along with personal context, which AI can interpret more effectively than a keyword-based search engine.

How AI search is reshaping intent and behavior Since AI provides direct answers, users no longer need to break their searches into multiple queries. Someone shopping for a new smartphone, for example, would typically conduct several separate Google searches—“best smartphones 2025,” “smartphone camera comparison,” “top phones under $1,000.”

With AI, that same user is likely to phrase a single, highly specific question: “What are the top 3 smartphones for photography under $1,000?” Instead of scanning multiple search results, they expect AI to summarize specs, analyze reviews, and present a final recommendation. This shift in behavior means businesses must optimize their content not just for keywords but for how users naturally phrase their questions.

Blending search intent as a new challenge for businesses Unlike traditional search, where queries fit neatly into categories like navigational, informational, or transactional, AI-driven searches often blend multiple intents at once. One study found that only 30% of ChatGPT queries fit standard intent types, while most were hybrid or entirely new.

The trust factor in AI recommendations AI search plays a growing role in consumer decision-making. If ChatGPT or Bing Chat confidently recommends Brand X, users may treat it as an expert endorsement.

There’s evidence that when users trust ChatGPT, they also trust the brands it recommends. This impacts behavior by:

• Shortening the research phase – Users may go straight to Brand X’s website instead of comparing options.
• Strengthening brand influence – AI-driven recommendations carry more weight than traditional search rankings.
• Eliminating visibility for non-mentioned brands – If AI doesn’t list your product, you lose that customer’s attention entirely.

Unlike Google search, where users browse multiple results, AI searchers often rely on the first recommendation they see.

Who is adopting AI search the fastest? AI-powered search is growing fastest among younger, tech-savvy users. Early data suggests that students and early adopters are leading the shift, frequently using AI for learning, research, and problem-solving. Many also turn to AI as a shopping assistant, relying on recommendations rather than traditional search results.

Older users, by contrast, tend to stick with Google and conventional search methods. For businesses targeting younger consumers or tech enthusiasts, ensuring visibility in AI-driven recommendations is becoming just as important as ranking in traditional search engines.

The Future of AI-Driven Search Behavior AI search encourages an “ask and receive” mindset—users expect fast, direct answers instead of browsing multiple links. Search intent is now:

• More complex – Blending personal context, preferences, and needs.
• More action-oriented – Prioritizing quick, actionable recommendations.
• More conversational – Allowing natural language queries over strict keywords.

To stay relevant, businesses must optimize for AI-driven discovery—or risk becoming invisible in this evolving search landscape.

AI-focused SEO and content optimization strategies

• Publish high-quality, authoritative content: AI search bots will favor content that is informative, well-researched, and authoritative. Since ChatGPT and similar tools generate answers by pulling from multiple sources, they tend to quote sites that have depth and credibility.
  
  Websites that provide comprehensive guides, detailed product comparisons, and in-depth articles are more likely to be referenced in AI generated responses. In practice, this means supplementing product pages with rich content (blogs, buying guides, how-to articles, etc.).
  
  For example, an online camera store could maintain a blog with articles like “How to Choose a Camera for Wildlife Photography” or “Mirrorless vs DSLR for Beginners – Complete Guide.” Such content can position your site as an authority that an AI might pull into a conversation about “what camera should I buy?”.
• Optimize for conversational queries: Instead of focusing solely on terse keywords, start incorporating the natural language questions your customers might ask. AI search understands long-tail, conversational queries better than traditional search engines. Use tools like forums, Q&A sites, or Google’s “People Also Ask” to research actual questions people have about your products. Then, create content that directly answers those questions.
  
  For instance, when selling eco-friendly clothing, have an FAQ or blog post answering “What are the best sustainable materials for summer wear?” Optimizing content around question phrases and long-tail queries ensures that when someone asks an AI assistant that question, your answer is more likely to be included.
  
  This might involve adding Q&A sections to product pages or writing articles in a question-and-answer format. Remember, if the AI can easily extract a concise answer or summary from your content, it’s more likely to feature you.
• Structure your content for AI comprehension: Just as we optimize content for Google’s crawler, we should also format content in ways that AI models can digest. Clear headings, logical sections, and labeled paragraphs help AI parse the content contextually. Breaking up your text with informative subheadings not only improves human readability but also signals to AI what each section is about.
  
  Likewise, using bullet points or numbered lists for steps and features can make it easier for an AI to pull out key points. Think of it this way: if you have a product page, consider including a short bullet list of the product’s top benefits or specs. An AI might directly quote that list when summarizing “the benefits of [Your Product].”
• Keep content fresh and updated: AI models and search indexes prefer up-to-date information, especially for product queries where specs and availability change. Regularly updating your content can increase the likelihood that an AI sees it as relevant and current.
  
  If you have older blog posts or product guides, refresh them with current data and trends. This not only helps traditional SEO but also ensures AI search isn’t quoting outdated info.
  
  Fresh content is vital for maintaining visibility in AI-driven search, as it increases the chance of being referenced in answers. For example, a travel gear store might update its “Best Hiking Backpacks 2024” article to “Best Hiking Backpacks 2025” with new models and keep the publish date recent – improving its chances of being pulled into a “Which hiking backpack should I get?” AI query in 2025.
• Build brand authority and mentions: AI systems don’t inherently know which brands to trust – they infer it from the content of the web. Thus, brand authority (having your brand mentioned on reputable sites, earning quality backlinks, and having expert endorsements) plays a role in AI search visibility.
  
  If your web shop or brand is frequently cited by experts or appears in well-regarded publications, an AI will be more inclined to include it in answers. Public relations and content partnerships are valuable here. It’s important to try and get your products reviewed by industry bloggers, included in “best of” lists on high-authority websites, or covered by news outlets.
  
  As noted earlier, ChatGPT’s answers skew toward trusted review sites and long-form rankings. One actionable approach is to collaborate with niche media outlets to get honest reviews or product comparisons that feature your items.
  
  If, say, you sell electric bikes, getting featured in a “Top 10 E-Bikes of 2025” article on a well-known cycling site not only helps your Google ranking but also means AI searchbots have that content as a source – increasing the chance your bike is mentioned when someone asks an AI for the best ebike.
• Provide real value (avoid pure promotion): Since AI answers tend to omit overtly promotional content, your content strategy should focus on genuinely helping or informing the shopper. Make sure your product descriptions are thorough and factual, include pros and cons honestly, and add helpful usage tips or comparisons on your site.
  
  If an AI is choosing between summarizing a puff-piece product description or a neutral expert review, it will choose the latter. So, align your tone to be informative. For example, include a small comparison table on your site that objectively compares your product to the previous model or a competitor – acknowledging where yours shines. This kind of transparency can make your content more “AI-friendly” and it builds trust with human customers too.

By implementing these AI-focused SEO tactics, you essentially make your content easier for AI systems to understand, trust, and use in their answers. The bonus is that most of these strategies (publishing quality content, answering user questions, structuring information clearly) are also beneficial for traditional SEO.

In other words, optimizing for AI search doesn’t mean abandoning classic SEO best practices – it means augmenting them to speak the language of AI and conversational search.

Structuring product data and metadata for AI Indexing

• Implement schema markup (structured data): Adding schema markup to your product pages is one of the most effective steps for AI-era SEO. Schema.org markup is a standardized format (in JSON-LD or HTML microdata) that labels parts of your page for search engines and AI.
  
  For e-commerce, the Product schema is essential – it includes attributes like product name, description, price, image, availability, brand, and even reviews/ratings. By using Product schema, you’re essentially making it explicit to any crawler or AI that “this text is the product’s name, this number is the price, this is a customer review,” and so on.
  
  This helps AI systems easily extract and present your product info in answer to relevant queries. For instance, if someone asks an AI, “What’s the price and key features of the Nikon D3500 camera?”, an AI could confidently answer with “The Nikon D3500 is priced at around $600 and offers 24.2 MP resolution, 5fps burst shooting, etc.” if it saw those facts marked up on your site.
  
  Marking up your content with structured data boosts your chances of being featured in AI-driven results, because you’re feeding the AI well-organized data on a silver platter. Beyond product schema, consider also implementing Review schema to mark up customer ratings and testimonials and FAQ schema for any Q&A content on your site. These add layers of context that AI can use.
• Use descriptive meta tags: While AI search bots primarily read page content, traditional search engines still rely on meta titles and descriptions – and these often carry over into AI-powered snippets (especially if the AI is using a live web result).
  
  Make sure each product page has a clear, descriptive title tag (including the product name and a key detail like model or use case) and a meta description that succinctly summarizes the product and its benefit. For example, a meta title like “Acme Stainless Steel Water Bottle – 1L, Insulated Leak-Proof Thermos” and a description “Acme 1L insulated water bottle keeps drinks cold for 24h. BPA-free, durable stainless steel with leak-proof cap – perfect for hiking or gym.”
  
  Such metadata helps Google/Bing index you properly (so you get picked up in their results that AI might use), and if an AI directly pulls info from your site, those key points are readily available.
• Ensure crawlability and indexing: AI search tools largely depend on content that is accessible via the web and indexed by search engines. It is essential to submit sitemaps to Google Search Console and Bing Webmaster so your latest products are indexed quickly.
  
  Since ChatGPT’s browsing uses Bing’s index, having your site indexed on Bing is just as critical as on Google. If your products aren’t indexed, they simply won’t exist to an AI seeking answers.
  
  Additionally, consider using Google’s Merchant Center (for Google Shopping data) and Bing’s Merchant Center – not only do these feed product listing ads, but Google’s new AI search previews have shown product recommendations with up-to-date pricing, likely drawn from its merchant/schema data. In short, feed the machine with data wherever possible.
• Leverage complete and clean product feeds: If you use marketplaces or shopping platforms that could integrate with AI (for example, Google Shopping, Amazon, etc.), maintain thorough and up-to-date product feeds there. While ChatGPT or Perplexity aren’t directly pulling from Amazon (yet), Google’s generative search might incorporate Google Shopping info for product queries.
  
  Ensuring your feed has high-quality images, accurate stock information, and rich descriptions can indirectly influence AI-driven discovery. It’s about covering all bases where AI might scrape information.
• Metadata for images and other media: AI models are increasingly capable of processing more than just text. Bing’s AI, for example, can show product images in answers when available. Always add descriptive alt text to your product images (helpful for accessibility and SEO) – e.g., <img src="shoe.jpg" alt="Red running shoes by BrandX, model ZoomX Pro">. This way, if an AI search includes an image of your product, it has the context to choose the right image and even describe it.

In essence, structuring your data is about making your site machine-friendly. Use schema to speak in the language of data to AI; use clean metadata to ensure search engines catalog your content correctly. By doing so, you’re not only helping AI-driven search engines understand your products, but you’re also enhancing rich results on traditional search (like getting stars, prices, or FAQ snippets on Google’s results). It’s a win-win for visibility.

Opportunities for paid placements in AI search engines

• AI integrations in Google and Bing: Google’s Search Generative Experience (SGE) and Microsoft’s Bing AI (often accessed via Bing Chat or Windows Copilot) have begun weaving ads into the AI-generated answers. These typically appear as contextual ads labeled “Sponsored” within or alongside the answer.
  
  The important thing for businesses is that these ads are drawn from the existing ad ecosystems. Google SGE can display ads from Google Ads, and Bing’s AI will pull from Microsoft Advertising. This means your current search ads and shopping ads can surface in AI answers, usually when relevant to the query.
  
  For example, if someone asks the Bing AI, “What’s a good price for running shoes and where can I buy them?”, it might show a snippet of an answer plus a sponsored ad for a shoe sale from a retailer – just as Bing search would.
  
  To maximize visibility in AI-driven searches, continue investing in pay-per-click (PPC) campaigns on Google and Bing. Optimize your ad targeting and keywords to match longer, more natural search queries common in AI searches. Using broad matches with smart bidding or adding more descriptive keywords can help. Also, keep product listing ads (PLAs) updated to ensure accurate pricing and availability. Staying active in PPC will help maintain visibility as search engines integrate AI into their results.
• Emerging AI-native ad platforms: New AI search engines like Perplexity.ai are exploring advertising models built specifically for the Q&A format. Perplexity, for instance, has announced an advertising solution integrated into its answer engine, with multiple placement options (e.g. sponsored answers, related questions, etc.) expected to launch by 2025.
  
  Initially, access to advertising on these platforms may be limited to select partners, but they will likely open up. Businesses should watch these developments – being an early adopter can grant a first-mover advantage.
  
  Early case studies suggest that ads in AI environments can perform extremely well: Microsoft reported that its AI-driven Copilot ads had a 69% higher click-through rate and 76% higher conversion rate than traditional search ads. This is possibly due to lower competition and highly relevant contextual placement.
  
  Thus, when a platform like Perplexity opens its ad program broadly, testing budget there could yield efficient results (often new ad networks have lower costs per click due to less competition).
• Sponsored answers and product placements: It’s conceivable that in the future, AI assistants could offer more interactive shopping experiences – e.g. “Which of these options would you like to buy? Here’s a link.” Already, Bing’s AI can sometimes present a product carousel for shopping queries. Keep an eye on features like shoppable AI results or chatbot integrations on ecommerce platforms. Amazon, for example, might integrate more AI into its search, and you’d want to be prepared for any changes in Amazon Ads if so.
• Prepare for paid inclusion (speculative): Some industry watchers speculate that eventually, open-domain AI like ChatGPT could include sponsored content (imagine ChatGPT recommending a “featured” product that’s actually a paid placement). OpenAI hasn’t done this yet, and currently ChatGPT’s recommendations are organic. However, as these tools look to monetize, businesses might see offers for sponsored slots. Stay informed via digital marketing news – knowing early means you can budget and experiment early.

In summary, paid search isn’t going away – it’s adapting. Your strategy should adapt too. Continue your core PPC on search engines, but also be willing to divert some ad spend into these new AI search opportunities as they arise. Track performance separately if possible (some ad platforms are still figuring out how to report AI vs. traditional placements).

The key is to ensure that whether a customer is using traditional search or an AI tool, your paid visibility is maximized in relevant moments.

Ecommerce traffic and conversions in an AI-driven search world

One big question for online retailers is: How will AI-driven search affect my website traffic and sales? The truthful answer is it’s a double-edged sword with both challenges and opportunities.

On one hand, AI search can reduce direct traffic to websites. Because the AI often provides the information a user needs upfront, the user might not click through to as many sites as they would have from a traditional search results page.

For example, if a user asks, “What’s the best budget smartphone I can buy?” and the AI responds with “The XYZ Phone is a top recommendation for budget shoppers, offering features A, B, C,” the user might feel they have their answer without ever visiting a blog or review site that Google might have sent them to.

As a result, content sites and even brand sites could see fewer visitors coming from certain queries that now get answered in-line by AI. An SEO report noted that ChatGPT doesn’t really drive clicks to websites – users who turn to ChatGPT are often specifically looking to avoid the typical “click around multiple sites” experience, preferring a direct answer. This trend could mean a dip in organic search traffic for some ecommerce sites, particularly for informational queries or top-of-funnel research queries that the AI can handle on its own.

However, when an AI does include your brand or product in its answer, it can act as a highly qualified referral source. ChatGPT’s new browsing mode has been observed to send traffic to thousands of unique domains as it cites sources. If your site is referenced as a source or your product as a recommendation, the user now has high trust and interest when they click through. It’s not casual browsing – they came because the AI effectively told them, “This is a good one.”

That means conversion rates for AI-referred traffic can be strong. In essence, AI might send less traffic but with higher intent. For example, if an AI answers “What are some good running shoes under $100?” and includes a specific model your site sells with a reference, a user clicking that is likely already convinced it’s a top pick, making them closer to purchase than the average visitor.

Conversion impact: The trust conferred by AI recommendations can shorten the conversion funnel. It’s somewhat analogous to a friend or influencer giving you a suggestion – you’re more inclined to go straight to purchase with minimal comparison. When ChatGPT “promotes your brand directly,” it’s as if you received an endorsement from an authoritative source .

We can expect that users coming from AI assistants might convert faster, or at least sign up or show strong interest, because the AI filtered out a lot of other options for them. Businesses should monitor if their analytics show new traffic sources like “bing (organic)” with unusual patterns or referrals from something like “chat.openai.com” (in cases where ChatGPT might indirectly cause a click). Early evidence suggests emerging trends in AI-driven search traffic. For example, tech and education websites saw a noticeable increase in visitors after being referenced by ChatGPT in late 2024.

On the flip side, if your products are not getting visibility in AI answers, there’s a risk of lost opportunities. An AI might only mention 2-3 products where Google would list 10 links. If you’re not one of those 2-3, the user might never know about you from that interaction. This makes it even more important to be among the top recommendations either via organic authority or eventually through paid inclusion.

Ecommerce conversion flows may also adjust. We might see more users doing research via AI (asking a series of detailed questions), then going to a store site only at the final stage to check out. This means by the time they reach your site, they might expect to find the exact product already, rather than using your site’s search or navigation extensively.

Ensure that landing pages (especially those that AI might link to, like a product page or a relevant blog post) are optimized to immediately provide what the user needs next – be it an easy “Add to Cart” button, stock availability, or additional details. Because if an AI sends a potential buyer your way, you want to remove any friction for conversion.

In terms of overall traffic distribution, Google and Bing will still drive the lion’s share of visits for most online shops for now. AI search is growing but not completely replacing traditional search. Google had about 6.5 billion unique visitors in a recent month vs. ChatGPT’s ~566 million – so classic search is still huge.

The impact of AI will likely be felt gradually: certain types of queries (those asking for advice, recommendations, or complex info) may shift to AI, whereas straightforward “navigation” queries (like “Nike official site”) or explicit shopping searches (“buy iPhone 15 online”) will still go through regular search or direct site visits.

What this means for businesses is that organic search may bring fewer clicks, but the visitors will likely be more engaged. Watch your web analytics for changes in traffic patterns and conversion rates. It’s possible you’ll notice a higher conversion rate overall even if raw traffic dips for some query categories, due to this self-selection of AI-primed visitors.

Adapt your expectations and KPIs – for instance, focus on metrics like how often your brand is mentioned or recommended (brand visibility) in addition to traditional metrics like click-throughs. The SEO game is no longer just about getting the click on Google, but also about being the answer in an AI-driven dialog.

Actionable recommendations for optimizing your web shop (AI and traditional SEO)

• Continue mastering traditional SEO fundamentals: Make sure your website follows SEO best practices – fast load times, mobile-friendly design, relevant keywords in content, logical site structure, and quality backlinks. Traditional search isn’t going anywhere, and high ranking on Google and Bing is often a prerequisite to being picked up by AI (since AI uses those indexes).
  
  For product listings, optimize titles and descriptions with keywords shoppers use (e.g. include “mens running shoes” if that’s what you sell, along with model names). Tools like WriteText.ai help continuously optimize content by starting with lower-competition keywords and gradually targeting high-traffic search terms, driving long-term visibility. These efforts will keep your organic traffic healthy and feed better data to AI systems scanning the web.
• Incorporate conversational keywords and content: Expand your keyword research to include questions and phrases users might speak or ask an AI. Then integrate these naturally into your site. This could mean adding an FAQ section on each product page addressing things like “Who is this product best for?” or “How does this product compare to [alternative]?”.
  
  By answering these questions on your site, you increase the chance that an AI will draw on your page when the same question is posed by a user. Make your content match the search intent behind conversational queries.
• Leverage structured data markup: Implement schema markup, especially Product, FAQ, and Review schemas on your site. This structured data helps both Google/Bing and AI understand and index your content. For example, use Product schema to mark up name, price, availability, SKU, etc., so that your product info is machine-readable. Add FAQ schema to common questions (e.g., in a Q&A about your shipping or product usage) – this can get your Q&A content directly featured in Google’s results and also available for AI to quote.
  
  If you have product reviews or ratings on your site, mark them up with Review schema; ChatGPT and others “value” social proof, so having structured review info may boost your attractiveness to AI answers. Always test your structured data with Google’s Rich Results Test to ensure it’s error-free.
• Optimize for featured snippets and rich results: Many strategies to get a featured snippet (position zero on Google) or a rich result align with optimizing for AI. Summarize answers at the top of your content, use bullet-point lists for “best of” or steps, and include tables or comparison charts where relevant.
  
  Featured snippet content is often exactly what an AI will use to answer a question because it’s concise and to the point. So by aiming to win snippets on Google, you’re simultaneously aiming to be the source an AI pulls from.
• Encourage and manage customer reviews: Not just on your site with schema, but externally – encourage reviews on Google (for local businesses), Amazon, or industry-specific review sites. For local businesses, having a strong Google Maps/ Google Business Profile presence with lots of positive reviews is key.
  
  ChatGPT’s local search will actually pull top-rated businesses (via Google Maps data) when asked about local services . Ensure your business listings are claimed and updated (address, hours, etc.), and ask satisfied customers to leave a Google review. High average ratings and review counts can propel you to the top when an AI ranks “best [category] in [city]”.
• Get your products featured by authoritative sources: Invest in outreach or PR to have your products included in influential “best of” lists, gift guides, or comparisons on well-known websites. As noted, AI answers lean on high-authority, long-form content.
  
  For example, a laptop brand would benefit hugely from being in PCMag’s “Best Laptops of 2025” list or a Wirecutter review. Those are precisely the kinds of sources an AI trusts. This might mean sending sample products to reviewers, writing guest posts, or partnering with influencers who also publish written content. The more independent discussions of your product exist online, the more likely an AI will “know” about it and deem it worthy of mention.
• Focus on brand building and clarity: In an AI-driven search world, brand recognition can be crucial. If a user asks, “Is Brand X reliable for winter jackets?” you want the AI to have seen enough positive information to respond favorably. Cultivate a strong brand story and ensure your value propositions are clear and repeated across platforms.
  
  When an AI summarizes your brand or product, it should pick up on those value points. This also means monitoring your online reputation – because AI could just as easily mention a negative (e.g., product recall or poor review consensus) as a positive.
  
  Strive for consistently good customer feedback and address issues that arise, so the overall narrative about your products online is positive. Essentially, make it so that an AI “endorsement” of your product would be aligning with the general consensus.
• Experiment with AI-powered ads: Allocate a portion of your marketing budget to try emerging advertising channels in AI search. For example, if Bing’s AI is relevant to you, ensure you’re running some ads on Bing (they might appear in the AI chat answers). Sign up for any beta programs for ads in generative search experiences.
  
  Keep an eye on Perplexity’s advertising platform – if you can join as an early advertiser, you might get cheap clicks and prominent placement. Track results from these experiments. Early adopters often get outsized returns before the competition floods in.
• Monitor analytics and AI Mentions: Update how you track and measure search performance. In addition to Google Analytics for site traffic, look at tools or services that might track brand mentions or referral traffic from AI.
  
  For example, periodically use ChatGPT or Bing Chat to search for your own products (“ChatGPT, what’s the best [your product category]?”) and see if you’re mentioned. Set up Google Alerts or social listening for your brand name plus keywords like “ChatGPT” or “Bard said” to catch any notable AI references. This will help you gauge whether your optimization efforts are working in the AI context.
  
  Also, if your traffic suddenly dips for certain keywords, consider if those queries might be ones being answered directly by AI now – and adjust strategy accordingly (maybe by targeting slightly different queries or ensuring you are the answer).
• Holistic approach – don’t neglect either channel: Finally, maintain a balanced approach. Optimize for AI-driven search and traditional search hand-in-hand. Many principles overlap (clear content, structured data, authoritative sources). Where they diverge – like conversational tone or the absence of ads – adapt accordingly.
  
  By covering both bases, you ensure maximum visibility. For instance, a well-structured product page with great content might get a rich snippet on Google and be the go-to source an AI cites. Meanwhile, your ad campaign can capture those who still click around, and maybe appear in AI answers too. Cover organic and paid, cover AI and non-AI, and you’ll catch customers no matter how they search.

By implementing these recommendations, businesses can future-proof their ecommerce SEO for the evolving search landscape. The key is to make your product information as accessible, trustworthy, and context-rich as possible – so whether a customer is using Google, Bing, ChatGPT, or another AI assistant, your web shop’s listings stand a strong chance of being discovered and recommended.