An e-commerce without AI in 2025 is leaving between 20% and 40% of potential sales on the table. Not through lack of traffic or lack of product: through lack of relevance. The visitor arrives, doesn’t find what they’re looking for quickly, doesn’t receive suitable recommendations, and leaves.
AI, when properly implemented in an e-commerce, tackles that problem on four fronts.
1. Intelligent search instead of keyword search
The default search in most e-commerce platforms (Shopify, WooCommerce, Prestashop) is exact-match search. If the user searches for "trainers for running" and the products are labelled as "running shoes" or "running trainers", the search returns no results.
AI semantic search understands meaning, not exact words. “Trainers for running”, “running shoes”, “sports shoes for running” and “shoes for running sport” return the same relevant products. For e-commerce with large catalogues, this can increase the search conversion rate by between 30% and 70%.
2. Personalised recommendations in real time
Recommendations of the "Customers who viewed this also viewed" type are useful but limited. Personalised AI recommendations go much further: they draw on the user’s full history, the behaviour of similar users, inventory trends and the time of year.
For anonymous users, recommendations are based on the behaviour of the current session. For identified users (those who have purchased before or have logged in), recommendations are truly personal. The conversion rate of well-personalised recommendations is between 3 and 8 times higher than generic recommendations.
3. Generating product descriptions at scale
An e-commerce site with a thousand products in its catalogue has two options for descriptions: use the manufacturer’s (generic, duplicated across other e-commerce sites, poor for SEO) or write them by hand (prohibitively time-consuming). AI offers a third option: automatic generation of unique descriptions, optimised for SEO and in the brand’s tone.
The process: we define a voice guide, a template with the expected description structure, and the system generates the description of each product from the catalogue attributes. A thousand unique descriptions, all in line with the brand, instead of duplicated generic descriptions. The impact on organic e-commerce SEO is enormous.
4. Churn prediction and reactivation
For e-commerce with a recurring customer base (subscription, frequently used products), churn prediction is one of the most profitable use cases. The model identifies which customers are at risk of not returning before they actually stop buying. A reactivation campaign sent to the at-risk customer at the right moment can recover between 15% and 30% of customers who would otherwise have been lost.
The typical AI stack in e-commerce that we recommend: semantic search with Algolia or a bespoke solution, recommendations with Recommend.io or a proprietary model, description generation with the Claude API, churn prediction with a proprietary model built on CRM data. For bespoke implementations, contact us at BAI Marketing.