Right now, somewhere, a buyer is asking an AI assistant a question you would very much like to influence: "What are the best suppliers for X?" or "Compare vendors for Y." The answer they get will shape a shortlist you may never know existed.
Most executives have never seen what AI tools say about their company. Some assume it mirrors their Google rankings. It does not. AI answers are assembled differently, from different signals, and they can describe you accurately, describe you incorrectly, recommend a competitor, or leave you out entirely.
The good news is that you can measure this. An AI visibility audit takes an afternoon the first time, and it turns an invisible risk into a list of specific problems you can fix.
Why this matters now
B2B buying research has moved upstream. As we covered in AI Search Is Replacing Google for B2B Buyers, buyers increasingly start with AI assistants and answer engines, comparing options and building shortlists before they ever land on a vendor site. That research generates no analytics, no form fills, and no signals you can see. The only way to know how you appear in it is to go look.
Unlike traditional search, where you can check a ranking, AI answers vary by tool, by phrasing, and by day. That makes a one-time spot check misleading and a structured, repeatable audit valuable.
How to run an AI visibility audit
Step 1: Write down the questions your buyers actually ask.
Build a list of 15 to 25 prompts across four types:
- Category questions: "best suppliers of [your category] for [your industry]"
- Comparison questions: "[you] vs [competitor]" and "[competitor] alternatives"
- Problem questions: "how do I solve [the problem your product solves]"
- Direct questions: "what does [your company] do" and "is [your company] good"
Use your buyers' language, not your marketing language. If sales reps hear a phrase on calls every week, it belongs on the list.
Step 2: Ask them across the tools that matter.
Run the list through ChatGPT, Perplexity, Gemini, and Google's AI Overviews at minimum. These assemble answers differently, and your presence in one says little about your presence in another. Use clean sessions where you can, since prior conversation history colors results.
Step 3: Record four things for every answer.
- Presence: were you named at all?
- Accuracy: is the description of what you do and sell correct?
- Position: were you recommended, listed neutrally, or mentioned as an also-ran?
- Sources: when the tool cites sources, whose pages is it citing, yours or a third party's?
A simple spreadsheet with prompts down the side and tools across the top is enough. Score each cell and note anything wrong.
Step 4: Audit your competitors with the same list.
Share of voice matters more than absolute presence. If a competitor is named in eight of ten category answers and you appear in two, that gap is the finding. It also shows you what the cited sources have in common, which tells you what earns citations in your category.
Step 5: Set a re-check cadence.
AI answers move. Quarterly is a reasonable rhythm for most B2B companies, monthly if AI-sourced traffic is already material in your analytics. Keep the same prompt list so results are comparable over time.
How to read the results
The audit will sort your situation into one of four findings, each with a different fix:
You are absent. The tools do not know you well enough to name you. This is usually a content and structure problem: thin product data, little third-party presence, and pages that are hard for AI systems to parse and cite. The fix starts with machine-readable product content and structured data, and extends to earning presence on the third-party pages AI tools already trust.
You are present but described wrong. The tools name you but misstate what you sell or who you serve. Trace the sources they cite. Outdated directory listings, an old about page, and stale third-party profiles are common culprits. Correct the sources and the answers follow, slowly.
You are present but not recommended. You make the list but never the shortlist. Look at what the recommended competitors have that you lack in the cited sources: reviews, comparison content, detailed spec data, case studies with specifics. Recommendation tends to follow evidence.
You are cited through other people's pages. The tools describe you accurately but cite a distributor, a review site, or a directory instead of you. That is workable but fragile, because someone else controls your story. The fix is making your own pages the best available source for facts about your products.
What actually moves the needle
Across audits, the improvements that show up in AI answers share a pattern. Complete, consistent, machine-readable product data. Clear pages that answer real buyer questions directly. Structured data that makes facts extractable. And a credible footprint on the third-party sources AI tools already lean on in your category. The tactics behind each are covered in our guide to LLM optimization, and you can see the approach applied end to end in how a mission-critical B2B manufacturer made its catalog AI-visible.
What does not move the needle: keyword stuffing, AI-written filler content, and pages built for crawlers instead of buyers. The systems assembling these answers reward being genuinely citable, which looks a lot like being genuinely useful.
The bottom line
You cannot manage what you have never looked at. An afternoon with a prompt list and a spreadsheet will tell you whether AI tools are recommending you, misrepresenting you, or skipping you, and each of those findings comes with a clear next step.
If you would rather have the full picture done for you, including the fixes prioritized by impact, that is exactly what our SEO + AI Search Visibility Audit delivers. Either way, go look. Your buyers already are.