Part 2 in a series about AI Search Strategies for Wholesale Distributors

There’s a difference between being found and being recommended. For most of search engine history, those two things were close enough that distributors didn’t need to think much about the distinction. You optimized your pages, built your rankings, and buyers found you in the list.

That gap has widened significantly, and for wholesale distributors, it matters more than almost any other change in digital marketing in the past decade.

What Does It Actually Mean to Be “Recommended” by AI?

When a procurement manager opens ChatGPT today and types something like “best distributors for industrial safety equipment in the Southeast,” they are not getting a list of search results. They’re getting a synthesized recommendation from an AI that has formed an opinion based on everything it has absorbed about the companies in that space.

That is categorically different from ranking on Google. Google indexed your pages and let buyers decide. AI tools evaluate your company, weigh it against competitors, and make a judgment call. They act as a layer of middleware between your future customers and your brand. They’ll summarize, compare, and steer buyers before they ever visit a single website.

The new goal isn’t to rank highly in Google search results. It’s to be the company AI recommends when a buyer describes their situation and asks for guidance.

Why AI Is Actually Filtering Your Traffic and How That Becomes an Opportunity

Here’s a reframe that’s worth sitting with: AI search isn’t just stealing your traffic. It’s filtering it.

The buyers who click through from an AI recommendation have already been through a pre-qualification process. The AI has assessed their need, matched it to your capabilities, and sent them to you with specific intent. That’s a warmer lead than someone who clicked a Google result because your page title sounded relevant.

The challenge (read, opportunity) is that most distributors aren’t doing anything to influence what AI knows about them or when it decides to recommend them. Most distributors’ web content was written to capture keyword traffic, not to teach ChatGPT when and why they are the right fit for a specific buyer situation. That’s the gap to close.

How AI Decides When to Recommend Your Company

AI recommendation isn’t random. It’s driven by what AI has learned about your company from the content you and others have published, and critically, by how well that content maps to the specific situations buyers describe in their prompts.

Today’s AI prompts are nothing like the 2-3 word keyword queries of traditional search. A buyer might type: “I’m evaluating MRO distributors for our three manufacturing plants in Ohio. We need same-day availability on safety consumables, EDI ordering capability, and a rep who knows the products. What should I look for and who should I talk to?”

That’s a 40-word, highly contextual prompt. AI will match it against everything it knows about distributors in that category. If your website and your content have never addressed same-day availability, EDI capability, or what to look for when evaluating an MRO supplier, you won’t be in the conversation, regardless of how well you rank on Google.

What Information AI Needs to Recommend Your Company

Think of it this way: Now more than ever, your web presence needs to function like a well-trained sales rep who can answer the questions buyers actually ask during an evaluation. That means going well beyond product specs, catalog pages, and yes, even your services pages to address:

• Who you serve: specific industries, company sizes, geographies, and applications
• What situations you’re the right fit for, and equally important, when you’re not the right fit
• What makes you different from the alternatives, not in vague marketing language, but in factual, verifiable terms
• What outcomes your customers have achieved, using specifics, not generalities

AI needs this information explicitly stated and consistently repeated across your web presence. It doesn’t infer. It doesn’t give you credit for things you haven’t said. If your site has never explained that you specialize in food-grade lubricants for processing facilities, AI won’t recommend you when a food processor asks for a lubricant supplier, even if that’s exactly what you do.

The Content That Gets Distributors Recommended

The content types that earn AI recommendations in distribution share a common trait: they answer specific, high-intent questions that buyers ask during the evaluation phase of a purchase decision. Here are the three that matter most.

Content That Answers Evaluation-Stage Questions

Buyers don’t just search for products. They search for guidance on how to choose between options, how to evaluate suppliers, and what questions to ask. If your content only covers what you sell and not how to decide or what to ask, you’re missing the conversations AI is most actively synthesizing.

Content that performs well in AI recommendations tends to address questions like: “What should I look for when choosing a hydraulic component distributor?” or “What’s the difference between a stocking and a non-stocking distributor and when does it matter?” These aren’t product pages. They’re educational pages that demonstrate expertise and help buyers make better decisions, which is exactly what AI is trying to do on the buyer’s behalf.

Honest Comparison Content

AI actively looks for comparison content when buyers ask about two or more options. If a buyer asks, “How does [Distributor A] compare to [Distributor B] for chemical handling supplies,” AI will find whatever comparison content exists and use it to shape its response.

If you haven’t published your own comparison content, your competitor may have. And their version of that comparison will not be flattering to you. Publishing honest, balanced comparisons (e.g., your stocking program versus alternatives, your service model versus big-box distributors, your technical expertise versus generalist competitors) puts your framing into the training data before someone else’s version gets there first.

Case Studies Specific Enough to Be Cited

AI loves specificity. Vague success stories don’t get cited. A case study that says “we helped a manufacturer improve their MRO program” gives AI nothing to work with. A case study that says “we helped a 400-employee food processing facility in Georgia reduce MRO spend by 22% over 18 months by implementing a vendor-managed inventory program for safety consumables” is content AI can cite when a similar buyer asks a similar question.

The specifics are the citation bait. Quantity served, cost savings achieved, time to implementation, industries, geographies, applications — the more concrete the detail, the more useful it is to an AI forming a recommendation.

How to Know When AI is Recommending You

Here is where distributors often feel stuck. Traditional SEO had clear metrics: rankings, traffic, impressions, clicks, etc. AI recommendation is harder to track because AI responses are inconsistent. The same prompt can return different results in different sessions, and citations are wildly fragmented across hundreds of sources.

The practical starting point is manual testing. Identify 20–30 prompts that reflect how your ideal buyers describe their situations. These prompts would be full-context questions phrased the way a real procurement manager would ask them. Run those prompts in ChatGPT, Perplexity, and Google’s AI Mode. See where you appear, how you’re described, and who’s getting recommended instead of you.

That competitive intelligence is more valuable than any keyword report. It tells you exactly what AI currently believes about your company, where the gaps are in its understanding, and what content you need to create to shift those recommendations in your favor.

The distributors who figure this out now, those who start publishing content that teaches AI when and why to recommend them, will have a compounding advantage that gets harder to close the longer competitors wait.

Frequently Asked Questions

If I’m not showing up in AI recommendations today, how long will it take to change that?

It depends on how aggressively you create and publish the right content, but you should plan for a 9–12-month runway before changes fully appear in AI training data. That timeline is an argument for starting immediately rather than waiting. The content you publish today begins influencing AI responses over the coming months, and each piece compounds on the ones before it. Distributors who start building this content foundation now will be entrenched in AI recommendations by the time competitors wake up to the opportunity.

What’s the difference between what AI recommends and what Google ranks? Do I need two separate content strategies?

No. SEO and AI recommendation optimization overlap roughly 85%. The same content improvements that help AI understand and recommend you should also strengthen your traditional Google rankings. You’re not choosing one strategy over the other. You’re doing both at once by creating content that is genuinely useful, specific, and authoritative rather than keyword-optimized filler.

Should I be worried about a competitor getting recommended instead of me? Can I do anything about that?

Yes, and yes. The most effective thing you can do is audit what AI currently says about you and your competitors. Open ChatGPT and ask it to compare you to your top two or three competitors in your product category. Read that response carefully. It reveals exactly what AI believes about each of you and what gaps in your content are costing you recommendations. From there, the fix is to create content that directly addresses those gaps. Comparison pages you control, case studies with relevant specifics, and buying guides that highlight the situations where you are the clear right choice are all key.

The goal is to give AI the information it needs to recommend you confidently — and to get that information into the training data before your competitor does.

Ready to find out where you stand?

Start with a simple audit: open ChatGPT and ask it to recommend distributors in your product category. Read what it says — and notice what it doesn’t say about you.

That gap is your content roadmap.

If you’re not sure where to begin, I work with wholesale distributors to build exactly the kind of content that closes that gap and teaches AI when and why to send buyers your way.

Book a 20-minute chat. I’d be glad to talk through what an AI-readiness audit looks like for your business.