### AI as a primary layer of product discoveryRecent consumer research shows a structural shift in how shoppers discover and evaluate products online. According to the latest Marketplace Shopping Behavior Report 2026, 58 percent of shoppers now use AI tools to research products, while 37 percent start their shopping journey on marketplaces – a drop of 10 percentage points compared with the previous year. Marketplaces remain the single largest entry point, but their dominance is eroding as attention fragments across search, social and AI assistants.At the same time, AI is clearly positioned as a research layer rather than a full buying channel. Only 17 percent of consumers feel comfortable completing a purchase directly via AI, despite more than a third having already started a purchase journey through an AI assistant. In parallel, other studies indicate that a substantial share of consumers already “arrive informed”: nearly half use AI somewhere in the buying journey, including to interpret reviews and evaluate offers, while a growing minority experiment with generative AI shopping tools to get tailored suggestions and comparisons.This combination of behaviors changes the mechanics of product discovery. Instead of browsing broad category pages or running generic keyword searches, consumers increasingly ask AI systems to pre-filter options by price, use case, compatibility, sustainability or other constraints. Discovery, comparison and shortlisting compress into a smaller number of high‑intent interactions, with AI acting as the decision layer that mediates which products are even considered.### Why this matters for product data and catalog standardsAs AI assistants become the first interpreter of product information, the quality and structure of product data move from operational hygiene to a strategic lever. Traditional product feeds were optimized for search engines and marketplace search: consistent titles, basic attributes, SEO‑friendly descriptions. In an AI‑mediated environment, the same feeds must support systems that parse, summarize and cross-compare across many sources simultaneously.Three consumer behaviors amplify the pressure on data quality:- A majority of shoppers use AI for research, which means models are continuously aggregating and normalizing product information from multiple channels.- Over half of shoppers say they often compare the same product on multiple marketplaces, typically browsing around three platforms before buying.- Price inconsistencies and conflicting product information across channels are cited as key reasons for losing trust, especially when reviews are missing or sparse.For brands and retailers, any inconsistency between feed variants, marketplace listings and direct‑to‑consumer catalogs is no longer just a UX issue; it actively degrades how AI systems rank, summarize and recommend their products. If one source lists a different material composition, dimensions or warranty terms, the assistant must either reconcile the conflict or downgrade confidence in the product altogether. That makes standardized, machine‑readable catalogs a prerequisite for visibility in AI answers.From the perspective of catalog governance, this pushes the market toward:- Stricter attribute taxonomies and shared definitions across channels.- Normalized units, classifications and compatibility data to support structured reasoning.- Systematic enrichment of “long‑tail” attributes that previously seemed optional but are critical for AI‑driven comparison (e.g. sustainability indicators, detailed technical specs, use‑case tags).### The evolving role of product feedsIn this context, product feeds are shifting from export artifacts into the core representation of the assortment. Where previously a feed could be minimally compliant for each marketplace or ad network, AI‑driven discovery assumes that every representation of the product is a faithful, structured abstraction of the same source of truth.Several changes follow from this:- Semantic depth over surface keywords. AI models rely less on exact keyword matches and more on semantic relationships. Feeds that capture precise functions, scenarios and constraints help assistants map products to highly specific user prompts (“a compact dishwasher for a family of three with low water consumption” rather than just “dishwasher”).- Consistency across endpoints. Because assistants integrate information from brand sites, marketplaces, review platforms and comparison tools, discrepancies between feeds become directly visible. This affects perceived reliability and can surface as “mixed” or cautious recommendations.- Continuous synchronization. Given how often prices, stock and variants change, static or infrequently updated feeds increase the risk that AI presents outdated or incorrect information. Real-time or near-real-time synchronization between PIM, ecommerce platform and external feeds becomes essential not only for conversion, but for maintaining the model’s trust in the data.In practical terms, this elevates APIs and event‑driven integrations over batch CSV exports. The more current and granular the feed, the easier it is for AI systems to answer detailed, time‑sensitive questions without falling back on generic or conservative suggestions. To understand the different formats for these feeds, read more about the [product feed](/blog/product_feed/).### Product detail pages in an AI‑mediated journeyIf AI now handles the first round of discovery, the role of the product detail page (PDP) also changes. By the time a user lands on a PDP, they have often narrowed down a shortlist through an assistant and are looking to verify specific aspects: exact specs, trade‑offs, visual confirmation, and social proof.Research into consumer behavior shows that three in five shoppers hesitate to buy if a product has no reviews, and that inconsistent information across channels erodes trust during comparison. Combined with the use of AI to interpret reviews and summarize sentiment, this places new requirements on PDP content:- Completeness and structure. Missing attributes do not just frustrate users; they create gaps in the model’s ability to answer questions. Rich, structured fields for materials, dimensions, compatibility, care instructions and use cases improve both AI responses and human decision‑making.- Machine‑friendly formatting. Bullet‑point specs, tabled attributes and clearly segmented sections help models extract information more accurately than long, unstructured text blocks.- Review depth and metadata. Volume of reviews remains important, but so does the presence of quantitative and categorical data (ratings by feature, use‑case tags, pros/cons) that AI can aggregate and present back to the user. To make sure you have it all correct, check our guide on [how to create a product description for your website](/blog/how_to_create_a_description_for_a_product_on_a_website/).Under these conditions, generic or templated PDPs rapidly lose effectiveness. Content must be specific enough that an assistant can confidently say why a given product is suitable (or not) for a particular scenario, rather than returning vague, non‑committal summaries.### Speed of assortment expansion and automationThe growing role of AI in discovery does not reduce the pressure to expand assortment quickly; if anything, it intensifies it. As consumers ask more granular questions, the probability increases that niche variants, bundles or configurations are needed to match specific constraints. Yet every new SKU multiplies the demand for structured data, accurate descriptions and aligned feeds across channels.Manual content production is the main bottleneck in this equation. The need to create, localize and maintain high-quality product information for thousands of SKUs cannot be met at scale using purely human workflows. This is where no‑code tooling and AI‑driven automation become central to content infrastructure:- Template‑driven content generation can ensure that core attributes and compliance information are present for every SKU, while still allowing differentiation where it matters.- AI‑assisted enrichment can infer missing attributes from existing data, manufacturer documentation or similar products, flagging uncertainties for human review.- Workflow automation can orchestrate the sequence from master data ingestion to feed generation, validation and distribution across marketplaces, social commerce surfaces and emerging AI shopping tools. It all starts with the right [product feed](/blog/product_feed/).The key constraint is governance: automated content must still adhere to brand, legal and regulatory requirements, and any hallucinated or incorrect attribute can propagate widely through AI systems that rely on that data. As a result, human oversight tends to move from hands-on writing to configuration, review and exception handling. If you want to dive deeper into the creation of product cards, take a look at our article, [How to upload product cards](/blog/how-to_upload_product_cards/).### No‑code, AI and the new interface to consumersA parallel shift is happening on the front end of ecommerce. As discovery moves from search boxes and category trees to conversational interfaces, retailers and brands need ways to expose their catalogs to these interfaces without custom development for every new AI channel.No-code and low-code tools are emerging as a bridge between backend product infrastructure and AI‑native experiences:- Conversational discovery on owned channels (e.g. chat interfaces on sites or in apps) can be configured to query existing product APIs and PIM systems, using a combination of natural language understanding and rules.- AI‑powered onsite search and recommendation layers can be trained on the same canonical product data used for external feeds, ensuring that consumers get consistent answers whether they ask a third‑party assistant or the retailer’s own interface.- Visual and multimodal discovery (image‑based search, voice queries) can be plugged into catalogs without rebuilding the entire stack, as long as the underlying data model is robust and well‑structured. Need more information how CSV's can help? Then review our article on the [CSV Format](/blog/csv-format-how-to-structure-product-data-for-smooth-integration/).From an infrastructure perspective, the core requirement is convergence: instead of separate content pipelines for site, marketplace and marketing, there is growing pressure to maintain a single, structured product graph that all AI experiences – internal and external – can interrogate.### Implications for ecommerce strategyThe fact that a majority of consumers now use AI tools for product research, while fewer begin on marketplaces than a year ago, signals a rebalancing of power in ecommerce. Traffic and influence shift from individual platforms to the intermediating intelligence layer that sits between consumers and catalogs.For operators, this has several strategic implications:- Visibility depends less on bid strategies and category rankings, and more on how intelligible and trustworthy product data appears to AI systems.- Investing in product information management, taxonomy and content operations yields a direct competitive advantage in AI‑mediated environments.- Fragmentation of discovery channels – marketplaces, search, social, AI assistants – makes consistency across all product representations critical for maintaining trust and conversion.- Automation and no‑code capabilities are no longer optional efficiency plays; they are necessary to keep catalog quality and speed of change aligned with how fast consumer queries and expectations evolve.In this landscape, the central asset is not any single storefront, but the depth, structure and reliability of the product data that all discovery channels consume. As AI continues to take on more of the research workload, ecommerce and SaaS providers that treat product content as core infrastructure – rather than a downstream marketing artifact – will be best positioned to align with the new, AI‑driven patterns of consumer behavior.---The trends highlighted in this analysis underscore the critical importance of a robust product information management (PIM) system. As AI increasingly mediates product discovery, the quality and consistency of product data become paramount. NotPIM offers a no-code solution to centralize, enrich, and harmonize product information from various sources, ensuring that brands and retailers can provide AI systems with the accurate, structured data they need to drive visibility and sales. By leveraging NotPIM, businesses can adapt to the evolving landscape of AI-driven commerce and maintain a competitive edge.