AI Shopping Assistants Are Changing Product Discovery: Are You Ready?

Half of all consumers now use AI when searching the internet, and 44% of those who have tried AI-powered search say it has become their primary and preferred source.

Most enterprise retailers have spent years optimizing for Google—keywords, rankings, and paid acquisition. But an AI shopping assistant operates on entirely different logic, rewarding structured data, contextual relevance, and real-world validation over traditional SEO signals.

According to Bain & Co., as of September 2025, 62% of US consumers use generative AI, and nearly 49% trust the accuracy of shopping AI for recommendations, highlighting where purchase decisions are made.

Source: Bain & Co.

Products are no longer discovered through backlinks and search results, but through a short list of curated recommendations generated by AI systems that prioritize completeness, accuracy, and trust signals.

In this article, we’ll review how AI shopping assistants assess and recommend products, and how you can evaluate the readiness of your retail operations for AI.

How do AI shopping assistants decide what to recommend? 

AI shopping assistants are changing how products are surfaced, compared, and selected. To compete in this environment, you need to understand not just what these systems are, but how they decide which products appear and which are ignored.

What AI shopping assistants are

AI shopping assistants are conversational tools that take a shopper’s natural-language request, evaluate products across the web, and return a curated set of recommendations, often with a clear rationale.

Instead of browsing pages, shoppers ask questions like: 

• “What are the best waterproof running shoes under $150?”
• “Best gifts for a 10-year-old who is interested in science.”

The assistant interprets the intent, scans available data, and delivers a short list of relevant products. 

Some of the most common AI shopping assistants are:

• ChatGPT shopping features
• Perplexity Pro Shopping Assistant
• Amazon Rufus
• Walmart Sparky
• Google AI Overviews

The global AI shopping assistant market was valued at $4.2 billion in 2025 and is projected to reach $22.1 billion by 2032, with a CAGR growth of 26.8%.

Source: Persistence

How they evaluate products

AI shopping assistants do not rank products based on ad spend or domain authority. Instead, they synthesize multiple signals to determine which products best match a query.

At a high level, they prioritize:

• Structured product data: Attributes like size, material, compatibility, and use case must be clearly defined and complete.
• Content depth and clarity: Descriptions need to reflect how shoppers ask questions, not just how products are marketed.
• Review signals and recency: Review volume, velocity, and sentiment influence trust and ranking.
• Third-party validation: Mentions in expert lists, comparisons, and category roundups increase visibility.

Research shows that 62% of U.S. consumers use generative AI to research, review, and compare products with highly practical intents. For instance:

• 34% use AI to find deals.
• 30% use it to compare products.
• 26% use it for recommendations and gift ideas.

Are AI shopping assistants already affecting your traffic and sales?

AI shopping is already reshaping how traffic flows and how purchase decisions are made. AI shopping assistants are actively intercepting discovery, comparison, and even checkout.

The shift is already underway

AI-driven shopping behavior is accelerating faster than most retailers realize:

• During the 2025 holiday season, traffic from AI-driven referrals to e-commerce brands surged by 752% compared to the previous year.
• While still averaging under 1%, some retailers are already seeing AI contribute up to 25% of their referral traffic.
• Around 17% of the US online shoppers say they plan to start their holiday shopping using AI platforms such as ChatGPT or Perplexity.

What it means if your products are not appearing

AI shopping assistants do not present endless options like Google search results. They return a shortlist—typically 3-4 products per query.

If your product is not included, you are effectively invisible when a shopper is making a decision.

When it comes to AI-discovery:

• There is no “page two.”
• Shoppers rarely refine queries to find brands they have not already seen.
• Recommendations become the default shortlist.

Brands that fail to appear in AI-driven recommendations risk being reduced to background utilities in agent-controlled marketplaces.

How ready is your catalog for AI-driven product discovery?

Area 1: Product data completeness

AI shopping assistants match products to multi-factor, natural-language queries. A shopper might ask for:

• “Waterproof running shoes under $150.”
• “Black open palm half finger boxing gloves.”
• “Travel backpack with laptop compartment and TSA lock.”

If your product is missing even one relevant attribute, it won’t match and, as a result, won’t surface in AI-search results.

Evaluate your product catalog by assessing whether the core attributes are populated across all SKUs or only in the top-performing products. Do you consistently include:

• Size, dimensions, and fit.
• Materials and ingredients.
• Use cases (e.g., travel, outdoor, office).
• Compatibility (devices, environments, systems).
• Occasion or context (seasonal, gifting, professional use).

Incomplete data limits the eligibility of your products even before the ranking process begins.

Area 2: Product description quality

Descriptions written for keyword density do not translate well to AI recommendations. AI assistants prioritize natural language context—content that mirrors how shoppers actually ask questions.

Evaluate your descriptions by assessing whether they answer the actual questions shoppers pose or just repeat SEO phrases. Check if they explain:

• Who the product is for.
• When and how it should be used.
• What problems it solves.

High-value inputs include:

• Ingredient or material breakdowns.
• Certifications (e.g., organic, safety standards).
• Compatibility notes.
• Use-case scenarios.

Area 3: Third-party signals and review velocity

AI shopping assistants heavily weigh external validation. They prioritize products that are:

• Referenced in expert roundups.
• Included in gift guides or comparison lists.
• Supported by recent, high-volume reviews.

Evaluate your external presence by assessing if your products are featured in:

• Category roundups.
• “Best of” lists.
• Editorial recommendations.

Check the review velocity of your products over the last 90 days because freshness matters more than lifetime totals. One study reveals that products with 500+ reviews in the last three months significantly outperform those with older, stagnant review profiles.

Area 4: Crawlability and structured data

AI systems cannot recommend what they cannot access or interpret. Even strong product data becomes irrelevant if it is not machine-readable or accessible to AI crawlers.

Evaluate your technical foundation by asking questions like:

• Is OAI-SearchBot allowed to crawl your site?
• Is your Google Shopping feed complete, accurate, and regularly updated?
• Does your product page include schema markup and structured metadata fields?

ChatGPT shopping queries are often routed through Google Shopping in the background, which means your feed quality directly affects product visibility.

What should retailers prioritize after completing the assessment?

Fix data gaps before adding new channels

Expanding into AI-driven channels without fixing your catalog will not improve performance; instead, it’ll scale your existing issues.

AI shopping assistants rely on the same foundational inputs across platforms. That makes data quality the highest-leverage investment.

Focus on prioritizing:

• Attribute completeness across all products, not just bestsellers.
• Natural-language descriptions aligned with shopper intent.
• Consistent formatting and taxonomy across your catalog.

Treat catalog quality as an ongoing operation, not a one-time project

AI systems favor fresh, accurate, and continuously updated data.

A product catalog that is enriched just once and left static will lose visibility to competitors who regularly maintain and improve their data.

To stay competitive, you need to operationalize catalog quality by building continuous workflows for:

• Attribute enrichment as new SKUs are added.
• Ongoing updates to descriptions, compatibility, and use cases.
• Monitoring visibility performance across AI-driven channels.

fabric NEON is built for continuous product catalog optimization. Our Product Agent enriches and maintains product data at scale, enabling you to monitor product visibility and activate updates across all channels.

Build third-party presence alongside on-catalog improvements

Even the best product data will not matter if the AI system never comes across your product. Product discovery depends on both on-catalog quality and off-catalog signals.

On-catalog data determines how well your products rank, and third-party signals determine whether your products are considered at all.

Prioritize external visibility in:

• Category-specific roundups.
• Expert comparison articles.
• Gift guides and seasonal lists.
• Editorial recommendations and reviews.

These sources are frequently cited by AI assistants when generating recommendations.

AI shopping assistant readiness starts with your product data

Most retailers are still optimizing for a discovery model built around search ranking and clicks. AI shopping assistants operate by prioritizing structured data, contextual relevance, and trusted signals.

Retailers whose products appear in AI-generated recommendations have:

• Structured and complete product data.
• Continuously maintained catalogs.
• Strong third-party presence aligned to real shopper queries.

If your inputs don’t align with how AI systems evaluate products, your visibility will continue to decline. To ensure visibility of your product data:

• Run the four-area readiness assessment across your catalog.
• Prioritize attribute completeness and description quality.
• Audit OAI-SearchBot access and Google Shopping feed accuracy before investing in additional channels.

Assess your product catalog’s AI Search visibility by exploring how Product Agent can help you prepare it for AI-driven discovery.

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