Generative AI in Ecommerce: From Implementation to Impact

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.

For customer support

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.

Content Creation and Automation

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:

• Contentstack. A headless CMS platform, Contenstack helps ecommerce companies manage their website and app content more easily by allowing them to update product details, promotions, and more — automatically. This creates a more consistent shopping experience across different devices and channels.
  
  
• Akeneo.  Primarily a PIM platform, Akeneo also offers supplier data manager (SDM), which acts as a central place for retailers to collect files and collaborate with suppliers. One of its many methods for importing files in Akeneo PIM, the Advanced Option, uses AI to enable retailers to clean and transform complex product files.
   
• Adcreative.ai. This tool allows retailers to generate ad and social creatives, ad packages, video ads, texts and headlines, and more. You can also analyze competitors’ top-performing ads across platforms, and your own to continue iterating.
  
  
• Designs.ai. Designs.ai is an integrated Agency-as-a-Service platform that uses AI technology to help create, edit, and scale content, including social copy, videos, images, and more.

”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

Attribute Enrichment

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: 

• Faux: faux fur, faux leather
• Satin: satin boxer, silky, silk, shiny, shine
• Woven: fabric, knit
• Cotton: cotton underwear, 100 cotton, superchill cotton
• Corduroy: corduroy, corderoy, cordoroy, cord, cords
• Stretch: stretchy, flex, elastic waist
• Wool: wool coat, wool blend, 100% wool, merino wool blend
• Fleece: fleece lined, fleece and love, ott fleece, fleece jogger, cloud fleece, reverse fleece, lumberjane fleece

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. 

Enhanced Search and Discovery, Via Autosuggest

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.

Advanced Merchandising Capabilities

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:  

Attribute-based slotting

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: 

• Brand preservation. Premium brands’ can ensure non-sale items appear in top results to maintain their high-end image.
  
  
• Vendor agreements. Meet vendor requirements by using attribute-based slotting to show specific products, like Apple accessories, while letting AI select the best options within those constraints.
  
  
• Marketplace product launches. Use attribute-based slotting to prioritize new items, enhancing the success of grouped product launches.
  
  
• Collections pages. Specify desired attributes for items on a Collections page, like "blue rugs," and let AI choose the best product for each shopper’s preferences.
  
  
• Private label or high-margin products. Slot own-brand or high-margin items in specific positions to drive key business metrics.

In sum, attribute-based slotting benefits consumers by displaying the most attractive products within defined constraints, enhancing their shopping experience.

AI-generated rules

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.

Dynamic Collections

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:

• Enhancing ecommerce SEO coverage.
• Improving operational efficiency.
• Reducing dependency on complete product data. 

Demand Forecasting

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.

Sentiment Analysis and Customer Feedback

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.

Virtual Style Assistants

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:

Data Quality and Integrity

The effectiveness of GenAI depends heavily on the quality and quantity of your data for training the models. To ensure success:

• Audit your existing data infrastructure and cleansing processes.
• Implement robust data governance practices.
• Start with smaller, controlled pilots to validate data quality.
• Regularly monitor and update training data to maintain relevance.

Customer Privacy and Trust

As you leverage more customer data for personalization, also do the following:

• Be transparent about AI usage and data collection.
• Implement strong security measures and compliance protocols.
• Give customers control over their data preferences.
• Focus on delivering genuine value in exchange for data sharing.

Change Management and Team Adoption

Getting your team comfortable with AI tools takes time. Here are some tips to consider:

• Provide comprehensive training and support.
• Start with high-impact, low-effort use cases.
• Document and share success stories internally.
• Create feedback loops between AI systems and merchandising teams.

Cost-Benefit Analysis

To justify GenAI investments, do the following:

• Define clear KPIs aligned with business objectives.
• Track ROI metrics rigorously.
• Compare AI-driven results against traditional methods.
• Scale successful implementations gradually.

Technical Integration

And finally, when implementing GenAI solutions, you should:

• Choose vendors with proven ecommerce expertise.
• Ensure compatibility with existing systems.
• Plan for scalability from the start.
• Maintain human oversight of AI-driven processes.

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.

Generative AI in Every Situation?

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: 

• Map your current search and discovery process to identify genuine gaps where AI could add value (vs. where existing tools work well).
• Survey your merchandising team to find repetitive tasks that could be automated without sacrificing quality.
• Collect and analyze customer feedback specifically around product findability and personalization needs.
• Start with high-impact, measurable use cases that directly tie to revenue or efficiency gains.

The Future of Product Discovery is Within Reach

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.