How to Build an AI Product Recommendation Engine for eCommerce

The difficult part of product recommendations is deciding which items deserve to appear together. Similarity and compatibility work hand in hand to help customers compare, pick and purchase. Retailers are looking to develop AI product recommendation engine logic that accounts for these relationships and recommends the right product at the right time. There is a growing investment in retail technology to achieve this goal. According to Adobe’s 2026 AI and Digital Trends report, 63% of retail and consumer goods brands use agentic or generative AI to design customer journeys and activate them across different channels. Customers also prefer conversational product discovery. 25% of respondents say they rely on AI-powered platforms for information and purchasing recommendations. Commercial measurement deserves the same attention as model selection. Salesforce’s 2026 commerce research found that only 32% of organizations had fully defined AI success metrics and KPIs. Before choosing an architecture, the team should decide what a better recommendation must achieve. For example, more accessory purchases, higher contribution margin per order, or more completed purchases after product comparison. The Persentra AI case shows how retail teams can put recommendation technology into daily use. The Australia-based company provides privacy-first personalization using anonymous shopper interaction signals. Its existing AI technology needed a retailer-facing dashboard that merchandisers could operate without engineering support for routine changes.

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