A lightweight hiking shoe, waterproof, suitable for longer hikes, and under 180 euros: someone searching for a product this way is describing a specific need. An AI assistant can compare suitable offers and help with the selection. To do so, it must be able to identify which products actually meet the requirements.
For retailers, this brings the quality of their product information into sharper focus. An attractive image sparks interest. Compelling copy communicates benefits. Whether a product is actually suitable, however, often depends on specific details: material, dimensions, intended use, available variants, or delivery time.
The more tasks AI takes on in preparing a purchase, the more important one simple question becomes: Are your products described in a way that allows a reliable recommendation to be made?
AI Is Becoming a Relevant Gateway to the Product Range
AI-powered product search has long been a topic for retailers. In July 2026, the purchase conversion rate of visitors who reached US retail websites through AI services was 60 percent higher than that of visitors from other channels. This is shown by recent Adobe data.1
For retailers, this is a reason to examine this route to their own product range more closely. What information do potential customers find when they research with the help of AI? Can they understand the product benefits? And is the information still accurate when a recommendation turns into concrete purchase interest?
It is worth looking beyond visibility alone. A product can appear in an answer and still be unsuitable—for example, because a particular size is unavailable or an accessory does not fit the existing device. The quality of the information on which the selection is based is therefore also crucial.
From Product Recommendations to Shopping Instructions
The willingness to give AI more responsibility already exists: 29 percent of online shoppers would like to delegate purchasing decisions or purchases to AI within the next five years.2
This makes the question of how well a customer’s request can be matched with the available offering increasingly important. One possible instruction might be: “Find a compatible replacement battery for my device that can be delivered by Friday at the latest.”
To support this request effectively, several details must align: the exact device model, confirmed compatibility, available stock, and reliable delivery information. A general description such as “high-performance battery for many devices” is of little help here.
Preparing for AI-powered shopping processes therefore begins with a task retailers can address directly: providing complete, unambiguous, and up-to-date information that is essential to purchasing decisions.
Which Product Information Is Particularly Important Now
Clearly Identifiable Products and Variants
Model names, item numbers, and variants must be clearly linked. If a jacket is available in blue, that does not automatically apply to the same jacket in black. Price, size, and delivery time should also be clearly assigned to the respective variant.
Attributes That Answer Customer Questions
Which details are relevant depends on the product range. For a desk, width, depth, and height adjustability may matter. For a monitor, connections and resolution are crucial. For spare parts, it must be clear which models they are suitable for.
Questions directed to your customer service team are a useful starting point: what do buyers regularly want to know before making a decision? This is precisely the information that should be reflected in your product data.
Current Prices and Availability
A recommendation must still be suitable at the time of purchase. Therefore, check how quickly changes to prices, stock levels, and delivery times reach the respective channels. Responsibilities and update processes are just as important as the initial data maintenance.
Consistent Information
The product page, catalog export, and connected data sources should communicate the same essential information. If dimensions differ between two places, the selection becomes unnecessarily uncertain. Such inconsistencies should be systematically recorded and corrected at their source.
Clear Descriptions and Specific Attributes Belong Together
Good product data must fulfill two tasks: help people make decisions and provide unambiguous information for further processing.
For the hiking shoe, a clear description can explain which types of hikes it is intended for. Specific attributes should also be maintained separately. This makes it possible, for example, to distinguish whether “lightweight” is a general marketing claim or can be put into context through a specified weight.
Limitations also belong in a helpful description. What uses is the product not intended for? What additional accessories are required? What conditions must be met? Answering such questions early makes an informed selection easier.
Trust Requires Decisions That Can Be Understood
As automation increases, clear rules become more important. Seventy-six percent of consumers want clear guidelines on when an AI assistant takes action.3
For the design of an AI-powered shopping process, this means recommendations should be understandable, price changes should be visible, and purchasing decisions should receive clear approval. If important information is missing, that uncertainty should remain apparent.
Particularly when it comes to compatibility or safety-related product attributes, a plausible-sounding assumption must not replace confirmed information. In such cases, a helpful follow-up question can be more valuable than a premature recommendation.
How to Prepare Your Product Range Step by Step
Start with a manageable product category and collect typical customer questions. Then check whether they can be answered using the existing product information.
Prioritize the gaps you identify according to their importance for the purchasing decision. Missing compatibility information or contradictory dimensions usually deserve attention sooner than additional marketing copy. Also establish who will add the information and keep it up to date in the future.
Then test the same questions again: can suitable products be found? Are the variants correct? Can recommendations be justified using specific attributes?
This creates a verifiable foundation for better product search—in your own store and in preparation for AI-powered shopping processes.
How effectively can your product range be explored using real customer questions? Talk to Quasiris about your product search and the next practical steps.
Find out which AI features support your product search, better understand customer needs, and make suitable offers discoverable in our overview of the AI Features of the Quasiris Search Cloud.
💡 How Good Is Your Search Really?
Irrelevant results, high zero-results rates, and labor-intensive manual ranking rules often indicate that a search solution is not yet making full use of its potential.
Our free Search Review provides an initial assessment of the quality of your existing search and potential opportunities for optimization.
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- Transparency notice: This image was generated using artificial intelligence and is for illustrative purposes.*
Sources
1 Adobe Digital Insights. (2026). AI traffic trends report: August 2026. Adobe. https://business.adobe.com/assets/pdfs/resources/sdk/ai-traffic-trends-report-august-2026/ai-traffic-trends-report-2026-08-19.pdf
2 DHL eCommerce. (2026). 2026 e-commerce trends report. https://www.dhl.com/content/dam/dhl/local/global/dhl-ecommerce/documents/pdf/g0-ec-trends-report-2026.pdf
3 Capgemini Research Institute. (2026). What matters to today’s consumer 2026. Capgemini. https://www.capgemini.com/insights/research-library/what-matters-to-todays-consumer-2026/
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