Generative AI in Ecommerce: How AI Is Shaping the Next Generation of Product Search

Search for “generative AI in ecommerce,” and the volume of articles, research, and predictions makes it clear: Brands can’t afford to ignore the impact of generative artificial intelligence (GenAI). 

But GenAI isn’t a single product with a single purpose. From chatbots that boost customer engagement to content creation for marketing and sales, AI is making inroads across ecommerce operations. 

Consider product search (and research): According to Salsify’s “2025 Consumer Research Report,” 68% of shoppers spend an hour or less on product research. If brands can capture customer interest with contextually relevant search results and sustain engagement through the research process, they’re better positioned to turn curiosity into conversion.

As noted by EMARKETER, “58% of global consumers have already replaced traditional search engines with GenAI tools,” and 68% of these consumers want search options that “aggregate product data across search engines, social media, and retailer sites.”

Here’s how ecommerce brands can effectively leverage GenAI to power their product search potential. 

From Keywords to Context: The Impact of GenAI on Product Search

Traditional search is simple: Users input keywords and hope for the best. While search tools have gotten better at narrowing the scope of results — for example, a customer searching for “window cleaning services” should see local results first — searches remain hit-or-miss. 

This sets the stage for diminishing search returns. If initial search terms don’t deliver relevant results, users typically try again. Each time the process repeats, however, frustration mounts. 

As noted by Search Engine Land, 54% of customers say they’re looking through more search results now than five years ago, 26% find it frustrating to comb through these results, and 22% struggle to find the right search terms.

GenAI takes a different approach. Instead of simply matching keywords to high-ranking results, artificial intelligence integrates contextual factors to understand search intent better.

How GenAI Product Searches Work

Consider a buyer searching for a summer wedding bridesmaid dress. Using a traditional search, “summer wedding bridesmaid dress” returns a list of sites that have effectively optimized their product pages to rank well for these keywords. 

While these pages have been checked for both accurate content and overall site quality by Google or other search engines, this doesn’t guarantee that the results are relevant. Searches might display product pages for winter or bridal dresses because they reference or contain links to summer dress options.

GenAI, meanwhile, uses machine learning (ML) algorithms to analyze word associations and provide net-new content. This sets generative tools apart from their first-generation AI counterparts, which excel at discovering new connections but can’t extrapolate based on them.

In the case of the summer wedding dress, GenAI product search dives into multiple data sources and comes up with common themes. For example, summer dresses often feature brighter colors and lighter materials. GenAI can also parse the context of a bridesmaid rather than a wedding dress and ensure that any options returned are appropriate for the role.

What the Next Generation of Product Search Means for Buyers — and Businesses

For buyers, GenAI searches offer better answers in less time. For businesses, next-generation searches come with the opportunity to stand out from the crowd — if they get AI right.

Here are five ways AI is changing product search.

1. Searches Are Shifting From Keywords to Phrases

Keywords are general; phrases are specific. Consider the summer dress example above. While GenAI returns relevant answers based on keywords, it can do even more with phrases. 

For example, our prospective bridesmaid might say, “Find me a bridesmaid dress for a summer wedding in Nashville, Tennessee. Make sure it has short sleeves, no ruffles, and is any color but green. The dress can cost no more than $500.”

Using this data, AI tools search hundreds or thousands of sites and return any matching results. If no results are found, GenAI can suggest similar alternatives.

2. Searches Are Becoming More Personalized

AI-driven product searches also provide more personalized results. 

With user permission, generative tools can include information about previous purchasing behavior and product preferences to deliver tailored results. 

One example is a consumer who prefers to buy in bulk and pay the lowest price for products. Equipped with this data, AI tools can eliminate search results that are above a certain price threshold and can seek out generic alternatives to brand-name options.

3. Searches Are Evolving To Include Multimedia

Another advantage of generative AI in ecommerce is the ability to go beyond text. 

Using large language models (LLMs) and natural language processing (NLP) frameworks, GenAI can interpret other inputs such as voice, image, or video.

In practice, this could take the form of a product search chatbot that uses conversation to provide recommendations. Customers could also upload an image or video and ask AI to find a specific product shown or return examples of similar products. 

4. Searches Are Leveraging Contextual Clues

Context is core to human experience. Conversations with other people are possible because human beings can simultaneously interpret content and context. 

Sarcasm is a good example, such as a conversation between two locals who live in a winter city. On a particularly cold day, one says to the other, “Glad it warmed up out here.” Based on content alone, the statement is serious but inaccurate. Accounting for context, the sentence is a joke.

Evolving generative AI tools are capable of parsing context around content to provide more accurate search results. Consider a customer looking for a new pair of running shoes. This is the content. Using this information, AI could return a list of well-reviewed shoes.

Accounting for context changes the outcome. AI finds that this is the fifth time in a month the customer has asked for shoe recommendations. Paired with several less-than-favorable reviews they’ve left on footwear websites; it’s clear they’ve already tried and failed to find a good match.

Equipped with this contextual data, AI can eliminate shoes the customer has already viewed and use the information in their reviews to narrow the search field.

5. Searches Are Relying on Complete Product Data

In the same way that GenAI tools can parse customer preferences, they can analyze product pages for completeness and accuracy. As a result, high-quality product detail pages (PDPs) are more likely to show up in AI-driven search results.

It’s also worth noting that 53% of shoppers have abandoned a sale due to incomplete or poorly written product details and that 54% have opted out of a purchase due to inconsistent product data across websites, according to Salsify’s “2025 Consumer Research” report. 

Better PDPs increase the chances of getting noticed by GenAI search tools and reduce the risk of abandoned carts. 

Best Practices for GenAI Success

While GenAI lays the foundation for the next generation of product search, it’s not a fire-and-forget function. 

More Data, the Better

First, brands need to train AI-driven search tools with data — and the more data, the better. Product text, images, and videos, along with contextually relevant data about how products are used by consumers, help improve result accuracy and relevance.

Track KPIs

Next, businesses need to track and measure key performance indicators (KPIs). These KPIs help brands understand how GenAI product search tools are being used and identify where there’s room for improvement. Common KPIs include the total number of search tool interactions, the volume of AI-connected conversion rates, and the average order value (AOV) of sales that start with an AI product search.

Review and Evaluate Search Results

Finally, ecommerce companies must review and evaluate search results. While next-generation AI tools can parse context and provide relevant results, their output ultimately depends on data. Inaccurate or outdated sources can produce searches that are confidently incorrect. Regular testing helps ensure efficacy.

GenAI in ecommerce is the future of accurate, personalized, and contextually relevant results. Make sure your brand is ready for the next generation of product searches.