For most e-commerce businesses, search is the entry point to the customer journey. It is the moment when intent is expressed, when a shopper tells you exactly what they want.

In a world where expectations are shaped by Google and Amazon, customers assume search will just work. Yet many retailers fall far too short to meet this expectation and it can cause a real rift between what the customer wants and what they get on the other side of your search results.This often manifests in incomplete, irrelevant, or frustrating, results that drives shoppers to competitors.

Why Search Defines Retail Success

AI-driven search holds great promise, but it only works if the catalog feeding it is enriched with structured, accurate, and complete attributes. Without this foundation, advanced algorithms cannot interpret natural language queries or deliver meaningful results. Search becomes the place where bad data is exposed most clearly, and where retailers lose the most revenue.

From Keywords to Natural Language Queries

Search behavior has changed dramatically in recent years. Shoppers no longer type a single keyword and scroll through dozens of results. Instead, they use natural language queries that mirror the way they speak to people. Queries like:

• “Eco friendly running shoes under $150”
• “Lightweight trail backpack for women”
• “Vegan leather crossbody bag with gold hardware”

For an AI search engine to understand and match these queries, it needs product data that is structured to reflect real-world attributes and benefits. Without enriched attributes, the engine cannot parse meaning, and shoppers see irrelevant results.

Enabling natural language search requires moving beyond basic product names and descriptions. Retailers need taxonomies, attribute hierarchies, and structured metadata that align to how people search. This shift is essential for capturing intent and converting it into sales.

What Product Attributes Are Essential

Not all attributes carry equal weight in search. Executives must prioritize enrichment efforts that directly support discovery and conversion. Attributes are heavily reliant on industry but we'll outline some of the most critical attributes below.

• Material and Composition: Essential for categories like apparel, furniture, and consumer goods where product feel, durability, or sustainability influence decisions. ‍
• Size and Fit: Key for apparel, footwear, and home goods where incorrect sizing drives costly returns. ‍
• Use Case and Occasion: Attributes like “hiking,” “wedding,” or “school” reflect the context of purchase and align with how shoppers phrase their queries. ‍
• Style and Design Elements: Colors, finishes, and design descriptors are critical for personalization and matching visual intent. ‍
• Price Ranges and Filters: Structured price attributes allow queries like “under $150” to work correctly. ‍
• Taxonomy Alignment: Consistent categories across systems ensure AI engines can map relationships between similar products.

When these attributes are missing, incomplete, or inconsistent, AI search systems cannot make meaningful connections, and customers abandon sessions in frustration.

Search Uplift from Attribute Enrichment

Consider a global outdoor retailer that upgraded its product search capabilities as part of a broader AI adoption effort. Prior to enrichment, shoppers searching for “waterproof jackets” often received results that included fleece pullovers or casual coats because the catalog lacked standardized material and weatherproofing attributes.

After a data enrichment initiative filled in missing details and aligned taxonomy, the same queries returned precise matches. The retailer measured a double-digit improvement in search click-through rate and a 12 percent lift in add-to-cart conversions.

This example illustrates a broader truth: AI search tools are not inherently broken. They succeed or fail based on the structure and completeness of the product data they are given.

How to Make Your Catalog Search-Ready

Executives should not think of search as an isolated technology project. It is an organizational capability that depends on disciplined data practices. Preparing a catalog for AI-ready search requires several key steps:

1. Audit Search-Dependent Attributes - Evaluate how many SKUs have complete data for material, size, style, and price. ‍
2. Enrich Missing Attributes - Use automation to fill gaps, supported by human validation for accuracy. ‍
3. Normalize Taxonomy - Align product categories and naming conventions across systems and regions. ‍
4. Apply Schema Markup - Ensure product feeds include structured data that AI engines can interpret. ‍
5. Continuously Monitor and Govern - Establish feedback loops that measure search performance and tie back to data quality.

These steps are not one-time fixes. They are part of an ongoing culture  that ensures AI investments deliver measurable ROI from inception onward.

Search Is Where AI Readiness Is Proven

Search is often the first and most visible test of AI readiness in retail. When results are accurate, relevant, and tailored to shopper intent, customers reward brands with loyalty and conversions. When results fail, they abandon carts and take their business elsewhere.

For senior leaders, the lesson is simple. Do not invest in advanced AI search tools until your product data foundation is in order. The return on AI search depends entirely on the quality of the attributes, taxonomy, and schema beneath it. Clean, enriched data is the difference between irrelevant results and meaningful discovery.

Explore the AI-Readiness for Retail Guide for a comprehensive framework to follow for retail AI-readiness.