What started as an AI hype has now turned into genuine interest, as companies begin to implement AI and create viable plans to leverage the technology to hit KPIs. With so many legacy vendors continuing to claim AI capabilities, it can be hard to separate what’s real from what’s marketing hype, what’s native AI from what’s bolted-on, and what’s a value-driven use case from what’s smoke-and-mirrors.
By applying a scrutinizing eye, you’ll see there are compelling use cases for Generative AI (GenAI) in ecommerce that are already yielding measurable ROI, particularly in product discovery. According to a recent McKinsey report, 50% of fashion executives see consumer product discovery as the key use case for Generative AI in 2025.
Join us as we outline clear GenAI benefits, how to navigate associated challenges, and cover use cases that bring sustained value to your ecommerce business and customers, helping you select the right partner(s) to work with for long-term growth.
The tangible impact of GenAI on ecommerce operations is already evident across multiple areas:
In many ways, the future of product discovery is already upon us, and GenAI plays a key role.
Forward-looking ecommerce retailers are integrating GenAI into existing product discovery functionality to meet buyers’ complex needs. This is happening by combining GenAI capabilities with customer behavioral data for better personalization, connecting the tech to their product catalog to improve search infrastructure, and more initiatives.
And leading product discovery vendors are helping ecommerce companies drive value using GenAI: helping them harness the technology to create “sticky” and revenue-generating experiences.
Check out these customer-facing and back-end examples of how Generative AI can generate business success:
Falling under “conversational commerce,” solutions like Constructor’s AI Shopping Assistant (ASA) are AI-based programs designed to respond to online queries in a natural, human-like way. They’re often integrated into websites, apps, and customer service systems to provide immediate assistance and support.
Advancements in GenAI have significantly improved these programs, especially in ecommerce.
AI shopping assistants are conversational product discovery tools that blend GenAI with user-level personalization technologies to optimize for ecommerce KPIs.
Depending on the tool, shoppers can pose detailed questions in the search bar or even engage in conversations. Shoppers then receive recommendations tailored to their unique preferences, history, and intent — and reflective of the site’s real-time inventory.
For the query “show outfits for an outdoor wedding in 90 degree weather,” the AI shopping assistant produces clothes, jewelry, shoes, and accessories from the retailer’s catalog.
This type of GenAI tool is ideal for shoppers who come to an ecommerce site with a general idea of what they want, but need help selecting best-fit items. This spans from starting a new hobby to finding a holiday gift for a small child or even looking for something to cook for dinner. Much like a trusted in-store associate, the assistant gives tailored advice and suggestions that instill confidence in purchase decisions.
“Our AI Shopping Assistant gives online shoppers a new, useful way to discover items they need and love — disrupting the current search and product discovery paradigm. We already have good product discovery solutions for people who know what they want and just want to search for it, or people who just want to browse a category, or take a product finder quiz. But in cases where shoppers have a more complex need that they can only explain in natural language, like ‘I need healthy items for a picnic’ or ‘I want a trendy shirt to go out in,’ the current paradigms don’t work. There was no good way to explain that need to the search engines of the past. That’s where our AI Shopping Assistant comes in. ASA makes suggestions based on detailed requests from a shopper — like a trusted, in-store associate would — while also instantly factoring in everything it knows about the shopper at hand.”
- Eli Finkelshteyn, CEO and Co-founder of Constructor (Retail Times)
It’s a win-win for both shoppers and businesses. Shoppers benefit from this, as tailored expertise expedites the journey from goal to purchase. And ecommerce companies drive more engagement and conversions as shoppers both research and shop on the same site.
Ecommerce companies across sectors — such as grocery, apparel, and general retail — have already implemented ASA with high-impact results.
For example, a major U.S.-based grocery retailer uses Constructor’s AI Shopping Assistant to automatically generate recipes that shoppers search for on their site. The ingredients shown are in-stock options personalized to each shopper. (As in, if the recipe calls for milk, and the shopper tends to buy organically, then options for organic milk appear.) Plus, ASA makes it easy for shoppers to add all the ingredients to their cart directly from the recipe page.
While most frequently used to enhance search and “conversational commerce,” ASA has other use cases as well and can be implemented flexibly and easily on ecommerce sites.
In the same light, GenAI chatbots offer instant, 24/7 assistance using advanced natural language processing (NLP) to handle a wide array of customer inquiries. They personalize responses based on customer history, creating a more engaging experience and freeing human agents to focus on more strategic tasks.
GenAI streamlines content creation for images, product descriptions, marketing copy, ad creatives, social content, and more. It's widely used in social media marketing, with 86% of marketers using GenAI to refine text, 85% to create ad text, and over half to edit or create images.
Beyond ChatGPT, there are retail-specific tools for ecommerce merchandisers:
”AI-driven content generation is set to revolutionize digital teams, allowing brands to form an even deeper connection with their customers. The future success of ecommerce hinges on leveraging AI for hyper-personalization, transforming ordinary shopping into unique journeys. More brands are automating content creation with AI, but you can't risk losing your brand voice for the sake of productivity — businesses need to maintain their brand consistency AND respond faster with content. Contentstack's advanced AI and personalization capabilities enable ecommerce brands to deliver smarter, context-aware content tailored to every interaction, enhancing customer engagement intuitively and effectively."
- Conor Egan, VP of Product at Contentstack
Another use case of GenAI in content creation is within Product Information Management (PIM) software, like Akeneo.
“GenAI attribute enrichment in PIM solutions revolutionizes product data management by transforming raw data into detailed, engaging product descriptions. This process enhances customer experiences by providing comprehensive and informative content, allowing for quicker and more confident purchase decisions. This technology also enables organizations to streamline workflows by automating the enrichment process, reducing manual effort and errors, and accelerating product launches. By generating rich, detailed descriptions, GenAI enables consistent and accurate product information across all channels, freeing up resources for strategic tasks and improving overall business efficiency.”
- Andy Tyra, Chief Product Officer at Akeneo
GenAI can also maximize ecommerce merchandisers’ efficiency to address a common pain point: missing or mislabeled product catalog data.
Product missing key product attributes aren’t easily discoverable, and ecommerce companies may lose revenue. With Attribute Enrichment, merchandisers can bridge the gaps in product catalogs.
This GenAI-native tool enriches product data by creating attributes and categories based on shopper trends and clickstream data, exposing consumers to a broader range of new items and eliminating time-consuming work for merchandisers.
AI-generated attributes (marked with a lightning bolt in the screenshot above) make products more easily discoverable, while allowing merchandisers to focus their time on more strategic initiatives.
A large fashion and apparel brand uses Constructor’s Attribute Enrichment to create a map of synonyms generated from queries. For example, running the attribute “fabric” through Attribute Enrichment led to the following values being automatically created:
Not only are the attributes created usable for filtering, but the AI-powered tool automatically picks up on macroeconomic shopping trends, as can be seen by the populated attribute ‘lumberjane fleece.’
And because these trends change daily, ecommerce sites can move at the speed of social media — without sacrificing precious merchandiser time.
Ecommerce companies can live up to customer expectations by enhancing Search with AI-powered semantic search, image search (where users can upload images to find similar products), and even voice search.
And where Generative AI can shine is with Autosuggest, offering up recipe ideas or other prompts to spark ideas for shoppers.
GenAI can integrate seamlessly within the search bar, sparking ideas for casual shoppers.
This enables customers to find products more easily, even with minimal input or when starting from scratch.
No matter the use case, AI isn’t meant to replace merchandisers. It simply works as a force multiplier that can help them optimize less visible areas so they can focus on strategic work.
This is especially true of the emerging technology’s role powering many advanced merchandising capabilities, such as:
Traditional product slotting allows merchandisers to pin products to specific positions based on SKU, which works well for promoting individual items but lacks flexibility for broader categories.
Constructor’s attribute-based slotting addresses this by allowing merchandisers to slot products by their attributes. This AI-powered ranking system helps align business objectives with AI insights, ensuring optimal product placement.
Use cases include:
In sum, attribute-based slotting benefits consumers by displaying the most attractive products within defined constraints, enhancing their shopping experience.
Tools like Constructor enable merchandisers to leverage AI to automatically generate rules for boosting, burying, and slotting products, optimizing result sets for their specific KPIs.
Merchandisers then receive granular, directional feedback, allowing them to review and override these rules via the Rule Performance feature within Constructor’s Merchant Controls & Intelligence suite. This functionality is effective even for pages with limited data.
Constructor also applies GenAI to create dynamic Collections, or personalized landing pages automatically generated for each shopper based on their behavior and intent on-site.
Each time a customer interacts with website or app content, they’re essentially “voting” for products they want to see. An AI-native search engine like Constructor’s takes note, dynamically assembling and re-ranking products to reflect that shopper’s interests and intent.
Creating collections, or personalized landing pages, can be time-consuming for ecommerce teams. Traditional methods require manual SKU selections or conditional logic, demanding a deep understanding of the catalog.
AI-generated Collections simplify this process by allowing merchandisers to describe types of products, occasions, or styles and then use Generative AI to populate the Collection with relevant items.
After adding the prompt “most attractive products for hosting thanksgiving dinner,” a merchandiser for an online grocer can automatically populate hundreds of relevant products in less than a minute.
Aside from speeding up the landing page creation process, this feature has other benefits that include:
GenAI technology excels in analyzing historical data sets to identify previously unseen patterns — everything from social media trends to colorways for apparel.
This allows ecommerce companies to identify key drivers like seasonal trends and market shifts so they can understand demand better and optimize inventory, production schedules, and distribution plans.
Popular, AI-based demand forecasting solutions include Blue Yonder, Kinaxis, and Anaplan.
Thanks to NLP, machine learning (ML), and advanced AI capabilities, GenAI can perform sentiment analysis by identifying hidden emotions and perceptions behind text. This helps ecommerce companies improve customer experience by synthesizing insights from product reviews, online surveys, support tickets, chatbot interactions, social media comments, and more. For example, GenAI can generate appropriate follow-up responses to product reviews, speeding up customer support.
Tools like Akkio, BazaarVoice, and Yotpo can be used for sentiment analysis, while models like ChatGPT and Claude can quickly detect actionable trends from customer feedback. With these insights, ecommerce brands can make informed decisions about their social media presence, product development, and other areas to enhance customer experience.
GenAI tools like virtual styling assistants and virtual try-ons are becoming popular, allowing customers to visualize how products (like clothes or furniture) would look on them before purchasing.
How to Navigate Potential GenAI Ecommerce Challenges
While Generative AI presents exciting opportunities for ecommerce, implementing it successfully requires careful consideration and planning. Here are key challenges to navigate and practical steps to address them:
The effectiveness of GenAI depends heavily on the quality and quantity of your data for training the models. To ensure success:
As you leverage more customer data for personalization, also do the following:
Getting your team comfortable with AI tools takes time. Here are some tips to consider:
To justify GenAI investments, do the following:
And finally, when implementing GenAI solutions, you should:
The key is taking an iterative, measured approach rather than trying to transform everything at once. By starting with focused use cases that directly impact revenue and customer experience, you can build confidence and expertise while delivering tangible business value.
There’s great potential and value in applying GenAI to improve product discovery. But it’s important to recognize it’s not the only useful tool in your toolbox.
As our CEO Eli Finkelshteyn explained in a webinar with AWS: “‘What’s my Generative AI strategy?’ is the wrong way of looking at it. It should be more along the lines of: ‘What’s my strategy?’ and then ‘Can Generative AI help with it?’ Like with any tool, it’s a means to an end.”
He expounds on this in an interview with The Ecomm Manager, too: “Ecommerce companies need to look at: What problem or pain point are we trying to solve? How can we use [Generative AI], other AI, or even non-AI-based technology to offer a better way? When you can blend what’s new and cool with what’s genuinely useful — creating something that shoppers will use repeatedly and that drives value for the business — then you’ve set yourself up for success.”
So, before hopping on the bandwagon, first determine if / where GenAI can meaningfully enhance an experience for customers or your ecommerce team:
Looking to learn more about the role of GenAI and other emerging technology in the future of product discovery? See how to thoughtfully harness and stay ahead of developments in AI to deliver hyper-personalized, engaging customer experiences that future-proof your business and increase revenue.