Retail has used AI for years in ways shoppers rarely notice: the products recommended to you, the order of search results, the price you see, and whether an item is in stock. Generative AI adds a more visible layer — shopping assistants you can talk to — and is beginning to change how people discover and buy products.

Recommendations

Recommendation systems are among the most commercially successful uses of machine learning. Amazon described its item-to-item collaborative filtering approach in a 2003 paper: recommend products similar to those a customer has bought or viewed, based on what other customers bought together. Modern systems use deep learning on browsing behavior, purchases, product attributes and context.

Search and discovery

AI improves product search by understanding natural language (“waterproof running shoes for wide feet”), correcting typos, and ranking results by relevance and likelihood of purchase. Visual search lets shoppers upload a photo to find similar items.

Shopping assistants

Retailers have launched conversational assistants. Amazon introduced Rufus, a generative AI shopping assistant, in 2024 to answer product questions, compare items and make recommendations. Such assistants summarize reviews, explain differences between products and guide gift choices.

The next step is agentic commerce: AI agents that search, compare and complete purchases on a user’s behalf — raising questions about payments, authorization and how brands reach customers when an AI does the shopping.

Product content

AI writes and translates product descriptions, generates lifestyle images, tags attributes and summarizes customer reviews — useful for retailers with huge catalogs.

Pricing and promotions

Dynamic pricing models adjust prices based on demand, competition, inventory and timing. They can increase revenue but raise fairness and transparency concerns, especially when prices differ between customers.

Supply chain and inventory

Demand forecasting helps retailers stock the right products in the right places, reducing both stockouts and waste. AI also optimizes warehouse operations and delivery routes.

Stores

  • computer vision for shelf monitoring and checkout-free stores;
  • loss prevention;
  • staff scheduling based on predicted footfall.

Customer service

Chatbots handle order tracking, returns and common questions, escalating complex issues to staff.

Concerns

  • Privacy: recommendation and personalization depend on behavioral data.
  • Fake reviews: AI makes generating fake reviews easier; regulators have banned them in some jurisdictions.
  • Manipulative design: AI-optimized interfaces can push unnecessary purchases.
  • Price discrimination and opaque pricing.
  • Accuracy: AI assistants can describe products incorrectly.

What retailers measure

Retailers judge AI projects by concrete commercial metrics:

  • Conversion rate: the share of visits that end in a purchase.
  • Average order value: whether recommendations lead to bigger baskets.
  • Search success: how often a search leads to a click or purchase instead of a dead end.
  • Return rate: better product information and sizing advice can reduce returns, a major cost in fashion.
  • Forecast accuracy and stockouts in the supply chain.
  • Customer service resolution rate and time to resolve.

A/B testing — showing different versions to random groups of customers — is the standard way to measure whether an AI feature actually improves these numbers.

Tips for shoppers

  • Use AI assistants to compare specifications and summarize reviews, then check key details on the product page.
  • Be skeptical of products with many near-identical glowing reviews.
  • Review personalization and privacy settings in shopping apps.
  • Compare prices across retailers before buying, since prices can change frequently.

Sizing and virtual try-on

Fashion retailers use AI to recommend sizes based on past purchases and returns, and to offer virtual try-on that shows clothes, glasses or makeup on a photo or live camera image of the shopper. Better fit predictions reduce returns, one of the largest costs in online fashion.

What to watch next

  • AI agents completing purchases and how payment networks support them.
  • Advertising inside AI shopping assistants.
  • Regulation of personalized pricing.
  • Retailers’ measured returns from generative AI assistants.

Key takeaways

  • Recommendations, search and pricing have long relied on machine learning.
  • Amazon’s 2003 item-to-item collaborative filtering paper is a classic of recommender systems.
  • Generative AI shopping assistants answer questions and compare products.
  • Agentic commerce could let AI agents shop on behalf of users.
  • Privacy, fake reviews, manipulative design and pricing fairness are key concerns.

Frequently asked questions

Why do I see different prices from others?

Prices can vary by time, location, demand, promotions and sometimes customer segment. Rules on personalized pricing differ between countries.

Are AI-generated review summaries reliable?

They are useful overviews but can miss nuance or be influenced by fake reviews. Read some individual reviews for important purchases.

Can I stop personalized recommendations?

Many services let you adjust personalization and privacy settings or clear browsing history.