For more than two decades, search has been the gateway to online shopping. Consumers typed a few keywords, compared products, read reviews, and decided what to buy. Retailers built entire digital strategies around that behavior, investing in search rankings, paid ads, and website optimization.
That buying journey is beginning to look different.
Adobe Analytics reported a 693% year-over-year increase in traffic from AI tools to retail websites during the 2025 holiday season. Salesforce also estimated that AI influenced $262 billion in global holiday sales, with traffic from AI-powered channels converting nine times higher than social media referrals. These figures point to a clear shift in how products are discovered and evaluated before a customer reaches a retailer's website.
A growing number of shoppers now describe what they need instead of searching through pages of results. An AI agent can compare specifications, prices, reviews, delivery windows, and availability in seconds before presenting a shortlist or completing the purchase with the customer's approval.
For retailers, this changes the criteria for visibility. Search rankings and digital experiences still matter, but product data, inventory accuracy, pricing, fulfillment details, and machine-readable catalogs now carry equal weight. If an AI agent cannot interpret product information accurately, that product has fewer opportunities to appear in recommendations.
This marks the rise of agent-led commerce, a model where AI takes on much of the work between customer intent and purchase. For retail organizations, the conversation has expanded beyond attracting shoppers to building data foundations that AI agents can evaluate and act upon.
The Catalysts Behind AI-Assisted Shopping
AI-assisted shopping has gained momentum because consumer behavior, enterprise technology, and digital commerce infrastructure have matured at roughly the same time.
Consumers are choosing AI over traditional search
Product discovery has become more conversational. According to Capgemini's 2025 Consumer Trends Report, 58% of consumers now use generative AI tools instead of traditional search engines for product and service recommendations, up from 25% in 2023. The same study found that 68% want AI to consolidate information from search engines, retailer websites, and social platforms into a single recommendation.
In simple words, this means product discovery is not restricted to search engine results page. Instead, customers expect answers that combine price, specifications, availability, reviews, delivery options, and promotions in one response.
Payment networks are preparing for AI-driven purchases
Finding the right product solves only part of the shopping journey. Completing a secure transaction has been the larger hurdle.
That barrier has started to come down. In 2025, both Visa and Mastercard introduced platforms designed for AI-assisted commerce. Visa's Intelligent Commerce initiative brings together partners including OpenAI, Anthropic, Microsoft, Samsung, Stripe, and Perplexity, while Mastercard launched Agent Pay, allowing AI applications to complete purchases through tokenized and authenticated payment credentials.
These launches indicate that payment providers are preparing for a future where AI applications participate throughout the buying journey.
AI can now interact with enterprise applications
A shopping assistant becomes far more useful when it can access inventory, compare shipping options, verify product availability, and retrieve pricing from multiple business systems.
Recent developments such as Google's Agent2Agent (A2A) and Anthropic's Model Context Protocol (MCP) provide common methods for AI applications to communicate with business software and external tools. As these standards gain wider adoption, retailers have greater opportunities to connect catalog, inventory, pricing, and fulfillment systems without building custom integrations for every AI platform.
Retail data has become the competitive advantage
Every recommendation depends on the quality of the information behind it.
A product with complete specifications, current pricing, accurate inventory, delivery estimates, and consistent catalog information gives AI applications far more confidence than incomplete or conflicting records spread across multiple systems.
This is where many retailers are concentrating their investments.
Recommendations Are Becoming Retail's New Discovery Channel
Recommendation has always existed in retail. It came from sales associates in physical stores, editorial reviews, influencers, comparison websites, and customer ratings. AI introduces another recommendation layer that evaluates thousands of products within seconds using product specifications, availability, pricing, delivery commitments, compatibility, return policies, and previous customer interactions.
Research from Gartner suggests that brands could experience a 25% decline in organic search traffic by 2026 as generative AI becomes a common way to discover information online. That projection carries a broader implication than lower website visits. Product discovery is spreading across interfaces where customers expect direct answers rather than lists of links.
Digital merchandising has traditionally focused on customer experience. And the quality of product information now has a direct connection to product discovery. Product specifications, dimensions, certifications, availability, delivery timelines, warranty coverage, and return policies all contribute to the final recommendation. This information forms the basis of every recommendation an AI assistant produces.
Retail organizations have spent years improving website performance, digital merchandising, and advertising efficiency. The next phase places equal attention on determining how accurately products can be assessed before they are recommended.
Building Your Commerce Stack for AI-Assisted Shopping
Retailers are already investing in the data foundations needed for AI. The next phase focuses on preparing that foundation for AI-assisted commerce, with the following capabilities emerging as core building blocks of the retail technology stack.
Product Data Is the New Storefront
Leading commerce platforms are shifting from flat product catalogs to connected product intelligence.
Semantic metadata, knowledge graphs, and standardized taxonomies allow AI to interpret relationships across millions of SKUs, identify suitable alternatives during stock shortages, recommend complementary products, and fetch relevant items even when customer queries don't exactly match catalog terminology.
For example, Shopify's new Catalog follows this approach by organizing and enriching merchant product data into standardized product records that AI platforms can search, interpret, filter, and compare consistently. It maps products to a common taxonomy, infers missing attributes where possible, groups similar products, and keeps product information linked with live pricing, inventory, and variants.
Prepare for Protocol-Based Commerce
Open standards are becoming the foundation of AI-assisted shopping. Protocols such as Google's Universal Commerce Protocol (UCP), Model Context Protocol (MCP), Agent2Agent (A2A), and Agent Payments Protocol (AP2) establish common methods for product discovery, tool access, communication, and payments across platforms. Supporting these standards reduces custom integrations while making commerce services accessible across a growing ecosystem of AI shopping applications.
Google's rollout of UCP into AI Search and Gemini, with support from retailers like Shopify, Walmart, and Target, shows the industry is converging on shared protocols rather than proprietary connections.
Keep Product Information Updated Across Every Channel
AI recommendations are generated from the latest product, inventory, pricing, and fulfillment data available at that moment. Even small delays between a business event and a catalog update can surface unavailable products, expired promotions, or inaccurate delivery commitments.
Event-driven pipelines built with CDC, Apache Kafka, and Delta Live Tables distribute these changes continuously across the retail data ecosystem with minimal latency.
Connect AI to Governed Retrieval Layer
Many retailers assume governance ends once data lands in a warehouse. Enterprise AI introduces another layer: retrieval governance. Before a model generates an answer, it decides which tables, documents, policies, or product records to retrieve. If that retrieval step surfaces duplicate catalogs, outdated pricing rules, or conflicting inventory feeds, the response can still be wrong even if the model performs perfectly.
This explains why platforms such as Databricks are adding capabilities like AI Search, external lineage, governed model services, and a new Discover experience, allowing AI applications to retrieve governed data assets with metadata, lineage, permissions, and business context intact.
Design Commerce Around Agent Authentication
Payment authorization is becoming another layer of the retail stack.
Before completing a purchase, merchants need to verify which agent is making the request, who authorized it, what it can purchase, and under which constraints.
Visa's Intelligent Commerce and Mastercard's Agent Pay introduce infrastructure where credentials, spending controls, and merchant policies are embedded into the transaction, giving retailers a standardized way to verify AI-initiated purchases. This shifts authentication from a checkout event to a programmable trust layer for autonomous commerce.
Add Agent Telemetry to Commerce Analytics
Traditional analytics explain what customers clicked, searched, and purchased. AI-assisted shopping introduces another layer of telemetry: how agents retrieve information, evaluate products, and complete transactions.
Forward-looking retailers are beginning to track metrics such as retrieval precision, recommendation acceptance, agent completion rate, protocol latency, tool invocation success, and token authorization failures alongside conversion and revenue. Unlike traditional web analytics, these metrics reveal which stage of the AI commerce pipeline is limiting performance, from product retrieval and protocol calls to payment authorization and order completion.
What's Ahead?
AI-assisted shopping is placing new demands on retail technology. Product intelligence, governed enterprise data, protocol-based integrations, and agent authentication are becoming part of the commerce stack.
At Eucloid, we work with retailers to modernize the data platforms and AI infrastructure that support these capabilities; from building AI-ready product data to implementing governed data architectures on Databricks, or engineering event-driven retail systems.
Talk to us about building the AI-ready commerce infrastructure that will power the next generation of retail.
