AI Product Discovery: How Agentic Systems Improve Product Search and Conversions

AI product discovery is how shoppers these days find their preferred items faster—using systems that understand natural-language queries, personalize results, and continuously improve product data so listings match customer intent.

AI agents interpret what shoppers mean, and make your catalog AI-ready so your products surface reliably across all channels.

Shoppers are still using on-site search, but they’re increasingly asking conversational questions across AI surfaces like ChatGPT, Perplexity, and Google’s AI Overviews and clicking through to the best match.

Research highlighted 1,200% jump in traffic from generative AI sources to US retail websites, demonstrating that AI-driven discovery is increasingly becoming the origin of product discovery.

Source: Adobe

Agentic AI continuously maintains and improves your product data—benchmarking, enriching, and activating catalog context so your product stays discoverable across every demand channel.

Agentic commerce platforms like fabric NEON are built to monitor, optimize, and activate product data for AI-era discovery.

In this article, we’ll review how shoppers are finding products in the AI era, how AI product discovery works, what makes discovery agentic, and how to get your catalog ready for agentic discovery.

How shoppers find products in the AI era

• Discovery happens across multiple channels:
  • On-site search still plays a major role, but shoppers are increasingly using descriptive phrases such as “comfortable shoes for standing all day” instead of short category keywords. Retail search experiences are expected to understand intent rather than simply match terms.
  • AI assistants are becoming a starting point for searches. A study found that 38% of shoppers have used generative AI while shopping online, and 40% rely on it for product recommendations, indicating that shoppers trust AI tools.
  • Answer engines such as ChatGPT, Perplexity, and Google AI Overviews increasingly surface curated product suggestions directly within responses. Instead of browsing multiple websites, shoppers receive a summary of options first, then visit retailers to validate their choices.
  • Social commerce platforms like TikTok and Instagram are now functioning as discovery engines powered by recommendation algorithms. Products appear based on interest signals, creator content, and behavioral predictions rather than on traditional browsing.

These shifts reflect the change in shopping behavior. Shoppers describe their problems, preferences, and use cases in natural language, expecting systems to automatically interpret context.

Research begins with AI interfaces, continues across marketplaces or social platforms, and ends only on a retailer’s site after options have already been narrowed down.

This creates a visibility challenge: your products must be interpretable by AI systems that process catalog data, attributes, and context before recommending items. If crucial product details are missing or inconsistent, your products will fail to appear in AI recommendations.

As discovery expands beyond a single channel, success depends on maintaining product data that performs across all the channels your shoppers use. Platforms built around agentic commerce principles focus on continuously enriching and activating the catalog so your products remain visible across all channels and emerge on discovery surfaces.

How AI product discovery works: 3 core capabilities

Natural language understanding

Shoppers no longer search using rigid keywords or category terms. Instead, they describe their needs, context, and preferences in everyday language.

AI discovery systems interpret conversational queries such as:

• “Comfortable shoes for standing all day.”
• “Gifts for coffee lovers under $50.”
• “Lightweight jacket for travel.”

Rather than matching keywords literally, AI understands relationships between the concepts. A shopper searching for a couch can still see products labeled as “sofa,” while sneakers and athletic shoes are treated as equivalent signals.

Modern discovery systems can also process complex, multi-attribute queries without requiring filters or navigation steps. When a shopper searches for “waterproof hiking boots for wide feet,” the system simultaneously understands:

• Product category
• Performance feature
• Use case
• Sizing requirement

Contextual personalization

Understanding language is only the first step. Effective product discovery also adapts results based on shopper context. AI evaluates signals such as:

• Past purchases and browsing history.
• Previously abandoned carts.
• Product preferences inferred from clicks and engagement.
• Real-time session behavior.

Over time, systems learn from interaction patterns. If a shopper consistently engages with eco-friendly products, sustainability attributes are prioritized automatically.

Returning visitors searching for a “dress” may immediately see styles aligned with their prior purchases and preferred sizes, without having to apply filters again.

Automated product data optimization

The most important capability of agentic discovery is the continuous improvement of product data itself.

Traditional merchandising requires teams to manually enrich catalogs, update attributes, and maintain consistency across channels. Agentic systems, on the other hand, automatically monitor catalog performance and enhance discoverability.

These systems continuously:

• Generate search-ready product descriptions enriched with relevant attributes.
• Apply structured tags aligned with real shopper search behavior.
• Identify missing information that prevents products from appearing in results.
• Maintain catalog completeness across demand channels.

For instance, fabric’s Product Agent can analyze the contents of your catalog and discoverability gaps. If a packable rain jacket lacks the attribute “travel-friendly,” the system can enrich the product data so that it surfaces in searches for travel-friendly jackets or carry-on-friendly outerwear.

What makes discovery systems “agentic”

• Basic AI product search:
  • It responds to queries using better matching than keyword search, often with semantic understanding and relevance scoring.
  • It uses machine learning to learn which results get clicks, then nudges ranking over time.
  • It still depends on your teams to maintain product attributes, taxonomy, synonyms, and completeness.
  • It reacts after the shopper searches, so it can’t correct gaps until problems show up as missed searches or poor conversions.
• Agentic AI product discovery:
  • It proactively optimizes and improves product data for discoverability.
  • It autonomously flags and fills data gaps that prevent products from appearing in real shopper language.
  • It monitors search patterns across channels and updates attributes, synonyms, and content depth to match evolving intent.
  • It supports predictive experiences that surface relevant products earlier through typeahead suggestions and intent-aware ranking.
  • It learns from zero-result searches, then adjusts the catalog so the same query doesn’t fail twice.

For instance, imagine a shopper searching for “vegan leather handbag.”

• In a basic AI search experience, they may get zero results because the catalog is using labels like “faux leather,” and nobody has mapped the synonym.
• In an agentic system, the platform recognizes “vegan leather” as a common intent term, connects it to existing “faux leather” products, and updates the data layer so future searches return the right products.

Agentic systems don’t just return better results for the same inputs; they improve the inputs so more searches succeed everywhere discovery occurs.

Measurable impact: Conversion and revenue improvements

When product discovery gets smarter, shoppers spend less time hunting and more time adding to the basket.

• Shoppers who use on-site search typically arrive with stronger intent and convert at a rate 2.5x that of non-searchers.
• Better query understanding prevents dead ends that push shoppers to bounce.
• Shoppers get relevant results sooner, especially on long, multi-attribute queries (the ones that usually break basic search).
• AOV lifts through intent-based recommendations, with search results and product pages surfacing the “right-next” item based on the query, not just popularity.
• Cross-sell within the search journey becomes more effective when a query like “waterproof hiking boots” automatically suggests compatible add-ons such as merino socks, insoles, or waterproofing spray.
• Traffic from AI and answer engines increases when your product information is structured enough to be cited by systems that summarize options for shoppers.
• Product data improves continuously, unlike one-off cleanups, and it reduces repeated manual work across attributes, titles, and taxonomy alignment.
• Fewer support tickets are raised when your shoppers can reliably locate items using natural-language queries and synonyms, rather than needing exact-match items.

Agentic systems power multi-channel product discovery

AI product discovery now extends far beyond on-site search. Shoppers are increasingly discovering products through conversational assistants, answer engines, and AI-powered research experiences even before visiting the seller’s site.

Agentic systems can continuously optimize your product data for discoverability across every channel. When your product catalog is complete, structured, and context-rich, you can see measurable outcomes, such as a 2.5x higher-intent search conversion rate, revenue lift, and fewer failed searches.

Request an AI Search Assessment today to understand your catalog’s readiness for AI discovery across search, assistants, and answer engines.

Book a demo to see how Product Agent can improve your catalog completeness and search performance across all channels.

---