This blog is the first in the series “From Product Data to Purchase: Designing AI-Ready Buying Paths for the Next Generation.” In this post, you’ll learn how AI is changing product discovery and education, from social commerce and AI search to the role of product data in recommendations.
The B2B buying journey is changing. Buyers are discovering products through social media, digital communities, search engines, and AI tools, then using those same channels to research and evaluate their options. For distributors and manufacturers, being findable online is no longer enough. Your product data needs to help buyers—and the AI systems guiding them—understand what you sell, who it is for, and why it fits their needs.
In the first part of our “From Product Data to Purchase: Designing AI-Ready Buying Paths for the Next Generation” series, we explore how AI is changing product discovery and education. From social commerce and search-everywhere behavior to AI recommendations and zero-click search, discover why complete, consistent product information is becoming critical to staying visible throughout the modern B2B buying journey.
Social media is increasingly becoming the platform where products and brands are discovered. This is true across Instagram, Facebook, and TikTok. But it’s also true in industry-specific digital communities like Trade Hounds.
We asked Trade Hounds users where they learn about new products. Social media came in first, followed by word of mouth from another technician, then manufacturer reps, websites, and supply house staff.
70% of respondents said they’re more likely to buy a product after seeing it on the Trade Hounds social media app. 69% said they’ve bought several products after seeing them on social media, and only 18% said they’ve never bought a product after seeing it on social media.
Social commerce was already starting to change sales and marketing before AI entered the picture. Younger generations who grew up in digital communities trust what their peers post on social media over traditional brand advertising. Now, those kids are old enough to have purchasing authority, and their buying expectations look very different from most of your older customers. This was already forcing industries to put product catalogs online and enhance their websites and search engine optimization (SEO).
Marketing strategist Neil Patel describes this shift as moving from “Search Engine Optimization” to “Search Everywhere Optimization.” He reported that people now conduct 45 billion searches a day across the internet. Google only makes up about 8.5 billion of them. The rest happen across platforms like Instagram, TikTok, Amazon, YouTube, AI assistants, app stores, and other digital environments.
That means your product information, content, and brand presence need to be everywhere buyers look, not just on your website. In an AI-driven world, visibility is becoming less about winning one search ranking and more about becoming a trusted source across an entire digital ecosystem.
This is the discovery stage of the buyer journey. Buyers are learning that products exist, building awareness, and forming opinions long before they contact a supplier. But social media is no longer just influencing people. It’s now influencing the systems those people use to make decisions. That’s where AI enters the story.
Now, AI is amplifying the power of social media. Social conversations signal product interest and trending topics. Likes, shares, comments, and reactions aren’t just vanity metrics. They indicate relevance, trustworthiness, and authority. Social media is helping AI determine which products and brands should be recommended. So, improving your brand’s presence across social media platforms is only becoming more important.
Discovery creates awareness. Education helps buyers understand their options. Once a buyer moves from discovering a product to researching and evaluating it, AI needs information to work with. And that brings us to the next challenge: the quality of the product data feeding those systems.
In addition to pulling data about your brand from social media, AI consumes product data from across the internet, not just your website.
G2 found that 51% of B2B buyers now start research inside an AI chatbot. 6sense found that 94% of buyers use LLMs at some point during the buying journey.
We recently launched a trade-specific chatbot in Trade Hounds, and tradespeople have already used it to ask about products over 15,000 times.
According to 6sense’s Buyer Experience Report, today’s buyers don’t contact sellers until they’re already 61% of the way through the buying journey. By the time a salesperson now enters the conversation, much of the buyer’s thinking has already happened.
Product data is now influencing the sale long before a salesperson knows an opportunity exists. Product data now decides which brands are visible at the beginning of the buying journey.
AI systems retrieve and rank information that appears relevant to the question, then generate an answer from the evidence available to them. While each platform uses different models and sources, the practical questions are similar:
In many cases, AI is less concerned with who has the biggest brand and more concerned with who provides the clearest, most consistent, and most verifiable evidence.
NP Digital recently tested 500 keywords and 4,300 prompts across three platforms and found big brands that traditionally rank on the first page of the Google search engine results didn’t necessarily get recommended the most in AI. Ranking number one on Google only gave a brand a 31% chance of being cited in an AI answer, while ranking fourth dropped that chance to 2.6%. Their research found that 75% of AI citations don’t even go to pages ranking in Google's top 10. Up to 90% of the pages cited by platforms like ChatGPT actually rank 21 or lower on Google.
Many companies are accidentally making themselves difficult for AI systems to understand, compare, and recommend. Common disqualifiers include:
AI cannot confidently recommend what it cannot understand, compare, or verify. In the past, bad product data created friction after the buyer found your product. Today, bad product data may prevent the buyer from finding your product at all.
When asked how important product images are when buying, 71% of surveyed Trade Hounds users said very important. Product images do not just help people buy. They help AI understand, classify, and confidently recommend your products. Complete content creates a richer dataset for humans and AI, improving both AI visibility and human discoverability.
A survey we conducted with electrical contractors in July found their number one pain point in ordering materials was errors. Product data errors do not only affect ecommerce. They also make it harder for companies with dirty data to be discovered or recommended in AI chatbots.
As of 2025, Google’s Shopping Graph contained more than 50 billion product listings, with more than 2 billion listings refreshed every hour. No buyer can manually review billions of products, but an AI system can rapidly filter and rank them. If necessary attributes are absent or buried in a PDF, the system may not accurately identify your product as a match.
If buyers can’t find it, understand it, or compare it, you may never get the sale. Product information is no longer supporting a sales conversation that has already begun. It is helping determine who gets into the conversation at all.
Traditional search asks customers to name the solution. Many purchases begin with a problem. AI removes the burden of translating a real-world problem into the right keywords. Data should not only describe what the product is. Brands need to give AI enough context to understand when the product is the right recommendation.
Buyers also research outside branch business hours. Fewer than 20% of surveyed Trade Hounds users would wait until the next day if they needed a part after a supply house closed. They are asking AI and social media platforms. If you are not there, you will lose the sale.
While digital content and digital platforms help us measure and improve upon many parts of the buyer journey, AI is creating a new challenge known as zero-click. A zero-click interaction happens when a buyer gets the information they need without visiting the website that provided it.
When Google's AI Overviews began answering questions directly in the search results, users no longer had to click into websites to find what they were looking for. 68% of U.S. Google searches ended without a click during the first four months of 2026. AI Mode just passed one billion monthly users, with queries more than doubling every quarter since launch. Google just got its biggest redesign in 25 years. But the impact goes beyond Google.
Buyers are increasingly asking questions in conversational AI interfaces like ChatGPT, Gemini, Claude, and Copilot. The AI may use information from your website, product catalog, documentation, reviews, case studies, social media posts, distributor listings, or industry content to formulate an answer, yet the buyer may never visit any of those sources directly. Your content can influence the recommendation without generating a visit, page view, or form fill.
AI answers are not necessarily eliminating a company’s influence. They’re making that influence harder to see and attribute. That’s because AI search doesn't work like a traditional Google search, where everyone sees roughly the same results. Two people can ask AI the same question and get different answers, different sources, and different recommendations. Even the same person can ask the same question twice and get different answers.
AI generates answers dynamically based on the user, the context, the information it finds, and the model itself. Different AI systems use different retrieval methods, indexes, and sources depending on the query. Research and industry analyses consistently note that source selection varies by platform, prompt, mode, freshness, relevance, authority, and whether the AI is using live web retrieval.
That's why measuring AI visibility is so hard. There is no single ranking to track and no single answer to optimize for. Instead, you're measuring the likelihood that your brand appears across a wide range of possible responses.
This is happening at the same time as economic pressures are increasing expectations for outcome-based marketing. A buyer may discover a product in Trade Hounds, hear about a company on a podcast, see an AI recommendation, discuss it with coworkers, and later purchase through a distributor. That makes attributing financial outcomes a challenge. We’re talking about the buyer journey today like it’s a straight line, but the reality is that it’s becoming less linear.
However, imperfect attribution doesn’t mean we’re left with no way to measure impact. Other industries routinely make consequential decisions using representative samples, directional evidence, and correlation. Nielsen doesn’t monitor every person watching broadcast television. It measures groups of people who serve as representative samples of different MSAs. Political polling works on a similar principle. Pollsters don’t interview every voter. They examine a representative sample, account for its limitations, and use the results to identify patterns.
Marketing can adopt the same mindset. We may not be able to trace every sale back through every social post, podcast, AI answer, peer recommendation, and search, but we can study enough of those journeys to understand what tends to influence the outcome.
Content strategist Amanda Natividad recommends replacing the demand for perfect attribution with a disciplined search for correlation. Compare markets, customer segments, or time periods with different levels of marketing exposure. Then look for repeated relationships between those signals and business outcomes such as branded search, qualified leads, quote requests, product adoption, win rates, and revenue.
Correlation does not prove that one marketing activity caused the sale. But when the same patterns appear across surveys, customer interviews, platform engagement, sales data, and controlled comparisons, the evidence becomes strong enough to guide decisions. Fragmented data, cookies, privacy laws, and AI answer engines make it impossible to account for every click.
The B2B buying journey no longer starts when a buyer visits your website or contacts your sales team. It starts when they begin looking for an answer—and increasingly, that search is happening across social platforms, digital communities, search engines, and AI assistants.
For distributors and manufacturers, product information is no longer simply supporting a sales conversation that has already begun. It is helping determine which products buyers discover, understand, compare, and ultimately consider. Complete, consistent, accessible product data gives both buyers and AI systems the context they need to recognize where a product fits.
But being discovered is only the first step. Once buyers know what they need, they still have to find the right product, confirm availability, evaluate their options, and complete the purchase. In Part 2, we’ll explore how AI is changing sourcing and purchasing—and how distributors can use AI to create faster, more frictionless buying experiences. Read it here.