Generative AI (GenAI) is rapidly evolving, with its financial potential continually increasing. In the apparel, fashion, and luxury sector alone, McKinsey reports that GenAI could add up to $275 billion in operating profits by 2028.
Ecommerce retailers and brands worldwide are already leveraging GenAI to streamline operations, secure a competitive advantage, and boost profitability. Leading companies are strategically using AI to improve the customer experience.
If you haven’t embraced the technology yet, now is the perfect time.
There are many practical GenAI solutions for ecommerce that are easy to implement, especially with the right vendor. Product Discovery, for example, is a low-cost, high-ROI area where you can start.
GenAI isn’t just about automating repetitive tasks to create time for merchandisers to be more strategic. It’s about creating systems to serve customers better — quicker.
Here are some practical applications of GenAI in ecommerce:
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
GenAI can also enhance content creation on the back-end of ecommerce platforms.
AI-native product discovery tools like Constructor automatically enrich product information from third-party vendors and suppliers with relevant categories, attributes, and metadata — using Attribute Enrichment.
As part of a holistic suite of tools, Attribute Enrichment learns from interactions performed in Search, Browse, Recommendations, and other product discovery solutions, enabling better prioritization of product data enrichment.
An example of this is seen with a large U.S.-based fashion and apparel brand. They use Constructor’s Attribute Enrichment to create a map of synonyms generated from queries. Running the attribute “fabric” through Attribute Enrichment led to the following values being automatically created for each category:
These enriched attributes improved product filtering, allowing customers to find their favorite brands and clothing items quicker. They also allowed the brand to capitalize on macroeconomic shopping trends more quickly, as can be seen by the populated attribute ‘lumberjane fleece.’
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
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.
Constructor’s ASA allows shoppers to express their product needs in natural language, providing personalized product and content recommendations based on preferences, history, intent, and real-time inventory.
“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)
ASA is a win-win, making the customer shopping experience more enjoyable and quick while simultaneously improving conversions and brand loyalty for ecommerce companies.
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 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.
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.
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.
With the prompt “show the customer the most attractive products for hosting a summer bbq,” a merchandiser for an online grocer can automatically populate hundreds of relevant products.
Aside from speeding up the landing page creation process, this feature has other benefits that include:
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.
What started as a simple search bar has evolved significantly thanks to GenAI. In addition to chatbot functionalities, GenAI-powered ASA also enhances the search experience via:
ASA uses intent-based suggestions to improve autocomplete functionality. For example, a query like “office clothes” could prompt suggestions such as “office clothes for summer,” “office clothes for women – business casual,” and “office clothes comfort fit.”
These suggestions go beyond product catalog data, leveraging insights from product images and reviews to provide relevant, personalized options.
Use ASA to help autocomplete fulfill shopper intent more accurately, building value to shoppers and your business.
ASA also acts as a personal shopping assistant, offering intent-based recommendations for complex needs like:
AI Shopping Assistant (ASA) provides a smarter way to handle "zero-result" searches.
If a shopper searches for "men’s paisley shirts" but your site doesn’t carry them, the AI engine can quickly analyze the shopper’s request and history to respond: “We don’t currently carry men’s paisley shirts. But we do have other prints that might catch your eye. Check out our floral and vintage patterns and new arrivals in men’s casual shirts.”
ASA dynamically generates follow-up recommendations, removing the need for manual merchandising.
Your ecommerce site likely features engaging content like blog posts, guides, and articles. ASA can enhance this content by automatically generating product recommendations.
For instance, a blog post on "Why Eating Superfoods For Breakfast Kick Starts Your Day" can be complemented with product suggestions like chia seed yogurt, quinoa porridge, and acai berry bowls without manual input. Images and recommendations are dynamically tailored to the reader's preferences, ensuring personalized and relevant content.
Use ASA to drive more conversions from content. The tool generates contextual and personalized product recommendations.
Last February, the Federal Trade Commission warned businesses to avoid making false or unsubstantiated claims about AI capabilities. However, many companies ignore this advice, leading to a practice known as AI washing.
AI washing, similar to “greenwashing,” involves companies exaggerating or misrepresenting themselves through misleading marketing or false claims. With AI washing, vendors inflate claims, use vague AI buzzwords, or label basic components as AI — misleading buyers about the product's true capabilities.
To avoid being deceived, educate yourself and ask vendors critical questions before making any commitments:
And then there comes the question from the early days of the internet that rears its head around new technology — like GenAI — every few years: What should you build and what should you buy? Should you build your own large language models (LLMs), and should you build the technology to power them yourself?
While it may be fun at first, the industry is changing too rapidly to confidently go it alone.
“GenAI is a fast-moving field, and there's a lot of potential value for the fastest movers who know how to capitalize on and leverage the currently available technology. [AI Shopping Assistant] already exists — and is starting to win awards. … [Attribute Enrichment] is already here, too. It’s A/B tested on many retail sites, driving real revenue increases, and decreasing manual tagging work. What’s still missing is the innovation built upon these foundations: determining ideal use cases for an AI Shopping Assistant, designing the UI, and building user trust, to name a few examples. While some ecommerce companies reinvent the wheel and try to build their own versions, others will innovate on top of what already exists — getting to user value first. We’ve seen this movie before. We know how it ends. Focus on innovation. Don’t reinvent the wheel.”
- Eli Finkelshteyn, CEO and Co-founder of Constructor
Generative AI isn’t a project for the distant future. It’s already here, transforming ecommerce.
As its use cases expand across the industry — from streamlining customer support with chatbots and optimizing supply chain management to facilitating content creation and more — embracing GenAI-powered solutions becomes increasingly compelling. Implementing this technology streamlines operations, boosts productivity, and positions businesses as innovative leaders, while also enhancing customer experience and building brand trust and loyalty.
Say yes to shaping the ecommerce future into one that benefits both businesses and customers alike. Say yes to the future of AI in product discovery.