What a difference a year makes. Last November, Perplexity’s “Buy with Pro” one-click checkout signaled a new era where AI wouldn’t just answer questions - it would close transactions.
Google, never one to be left behind, has since reshaped its search results into shopping feeds, pushing organic listings further down (or off) the page. A quick search for “black alligator cowboy boots” today returns a wall of carousels, product grids, and deals and not one organic website listing – clear proof that search is no longer about finding websites that match a user’s query to relevant content, but rather about instant commerce.
This shift marks the beginning of a sea-change: AI engines are learning to find, rank, and present products directly. Which raises the bigger question - how do machines actually find your products?
AI Search Adoption is Surging
Source: Adobe Analytics, March 2025
Generative AI is rapidly becoming a mainstream shopping tool. Adobe’s survey of 5,000 U.S. consumers highlights just how quickly behaviors are shifting:
- 39% have already used generative AI for online shopping — with 53% planning to do so this year.
- Top AI shopping tasks include:
- research (55%),
- product recommendations (47%), and
- unique product discovery (35%)
- Visitors from AI sources show 8% higher engagement, browse 12% more pages, and bounce 23% less than other traffic sources.
- 92% of shoppers who used AI said it enhanced their experience; 87% said they are more likely to use AI for larger or more complex purchases.
This explosive growth shows how quickly AI-driven shopping is moving from research and consideration toward direct purchase - making AI visibility critical for distributors and brands.
Generative AI can certainly seem like a magical black box, but behind the scenes AI is parsing search intent, analyzing product data, comparing options, and deciding which listings to surface - and in what order. Its goal is simple: deliver the most relevant results to buyers and drive higher satisfaction and conversions. So how do ecommerce businesses rank in this brave new world?
AI engines work differently than traditional search engines. With SEO, product detail pages built from catalog data - titles, descriptions, and specifications - are optimized with keywords and tags so search engines can index and rank them in results. But AI search doesn’t just index fields; it interprets meaning. That’s why GEO demands more - long-tail keywords, bullet points, questions and answers, reviews, and use cases - so AI can infer which products best match a natural language request.
How AI Search "Thinks Differently"
Traditional search engines look for exact keywords and phrases in your product pages, but AI search goes further. It tries to understand the relationships between words and phrases, almost like measuring how close they are on a giant map of meaning.* That’s why AI can tell that “navy” and “dark blue” are nearly the same, or that “lightweight” suggests comfort and ease. By recognizing these semantic connections, AI can surface products that fit a shopper’s intent even if the words don’t match exactly.
This is why richer product content - like use cases, attributes, and Q&A - is so valuable: it gives AI more clues to interpret meaning and match your products to real-world buyer requests.
*Go here for a more technical conversation about Embeddings, Vector Databases, and Semantic Search.
How to Rank in AI Search
AI doesn’t just read words - it reads meaning. That’s why businesses must go beyond keywords and basic SEO hygiene to stay visible and persuasive in the AI economy. To rank in this new environment, ecommerce teams need to expand their product content in very specific ways:
- Enrich your catalog feeds for AI with structured, detailed product content—titles, descriptions, specs, attributes, and images—that AI models can parse and recommend.
- Add granular attributes such as compatibility, material, dimensions, and unique features so AI engines can confidently infer when your product fits shopper intent.
- Layer in context and trust signals like bullet points, Q&A, reviews, and use cases that help AI connect products to real buyer scenarios and recommend with confidence.
- Give your product content a unique brand voice and tone to generate differentiated, persuasive copy that stands apart from generic supplier data and earns higher ranking in AI search results.
Adding richer content isn’t rocket science - it’s data science - and the only real challenge is doing it with structure, governance, and brand consistency across tens of thousands of products. That’s where CatalogIQ™ comes in.
CatalogIQ – Your Catalog, With a Brain!
CatalogIQ is the only AI platform that creates, scores, and enriches product catalogs end-to-end. It gives distributors the foundation they need to optimize for AI-driven search. Messy supplier feeds, duplicates, thin content, and poor discovery are constant obstacles. CatalogIQ solves these pain points by transforming raw inputs into structured, enriched, and branded product data — making catalogs truly AI-ready at scale:
- Catalog Assembly: Supplier data arrives in dozens of inconsistent formats, from PDFs to spreadsheets. CatalogIQ ingests it all and normalizes it into clean, scalable catalogs.
- Taxonomy & Duplication: Duplicates and mismatched categories confuse buyers and bury products. CatalogIQ eliminates duplicates, aligns categories to your taxonomy, and ensures consistent classification.
- Product Content Quality: Supplier copy is often incomplete, inconsistent, or off-brand. CatalogIQ generates complete, conversion-ready content, enriches attributes, and applies your brand voice and tone so every SKU is persuasive—not generic.
- Product Discovery: Buyers search in natural language, not SKU codes. CatalogIQ enriches listings with attributes, bullets, Q&A, reviews, and use cases—structuring content so AI-driven search can connect products to real buyer intent.
But wait - there's more! CatalogIQ doesn’t just enrich content; it provides an audit trail that traces each attribute back to trusted sources, giving distributors confidence in the accuracy and compliance of their catalogs. And because every business channel has its own format requirements, CatalogIQ can output enriched content in any structure or feed — ready for ecommerce platforms, marketplaces, ERP systems, and AI-driven discovery engines.
Now More than Ever, the Catalog Is the Product
AI-powered product discovery starts with high-quality product data — and that’s where CatalogIQ™ gives distributors a critical edge. Unlike generic feed management tools, CatalogIQ is built for distributors, turning messy supplier inputs into structured, enriched, and branded catalogs that AI engines can actually understand and recommend.
On the structured side, CatalogIQ normalizes formats, eliminates duplicates, and standardizes attributes like size, material, compatibility, and availability. Business rules keep everything compliant and consistent across tens of thousands of SKUs, making every product more discoverable — whether indexed by a search engine or surfaced through an AI-powered shopping assistant.
On the content side, CatalogIQ enriches product pages with the context AI needs to interpret meaning: use cases, Q&A, bullets, reviews, and branded descriptions that carry a unique voice and tone. By combining enrichment with governance and an auditable trail of trusted sources, CatalogIQ ensures your catalog isn’t just complete — it’s credible. The result is a smarter, AI-ready catalog that positions distributors to win visibility, trust, and conversion in the AI economy.
What’s your CatalogIQ?
Ready to make your catalog AI-ready? Let’s talk.
Contact Kevin Jackson at kevin.jackson@magnetlabs.ai to schedule a demo of CatalogIQ™ Smart Catalog Builder - MagnetLABS’ AI-powered solution for building, normalizing, and enriching distributor catalogs at scale.
