Is Your Brand Showing Up in AI Search? 10 Things to Audit Today.

As you read this, an AI engine is describing your brand to a buyer you will probably never meet.
It is happening in a conversation you cannot see. And the AI engine is deciding whether you make the buyer’s shortlist. Do you know what it’s saying?

G2’s Answer Economy report, based on a survey of B2B software buyers, found that 51% of B2B software buyers now start their research with an AI chatbot more often than with Google, up from 29% a year earlier. In the same study, 69% chose a different vendor than they had originally planned based on what the AI tool told them.

The shortlist is no longer something buyers assemble by reading your site. It is something an AI hands them, fully formed, before you get a vote.

Here is the part that worries demand gen leaders: The channel that now decides a major chunk of consideration produces no sessions, no clicks, and no line in the report you open every Monday.

So most teams assume silence means safety. It doesn’t. It means you could be losing deals in a room you have never walked into.

This article shares 10 checks that you can score and run this week. Score yourself and then scroll down to learn more about each item on the checklist, plus tips for how to improve!

AI search audit for brand visibility: 10 checks

1. Have you run your own buying prompts and read who shows up?

Open ChatGPT, Perplexity, Gemini, and Google’s AI Mode.

Type what your buyers type: “best [your category] for mid-market,” “[competitor] alternatives,” “[you] vs [competitor].”

Then read the answer like a buyer, not a marketer.

Do you show up? First, or buried after three rivals? Is the description accurate, or does it deliver outdated positioning?

This is the closest you’ll get to watching a shortlist form in real time. As many as 41% of buyers use AI chatbots to weigh vendor strengths and weaknesses, as per G2’s research.

Keep this in mind, though: The same prompt yields different vendors on different days. SparkToro, for example, has documented real volatility in AI recommendations. Don’t celebrate one good answer or panic at one bad one. Run each prompt several times across several days and read the pattern.

2. Are you measuring AI visibility as a separate metric?

“Organic traffic” on Google Analytics isn’t the only metric of brand visibility.

AI crawlers, the bots from OpenAI, Anthropic, Google, and Perplexity that read the web to build answers, generate enormous activity that produces no session, no click, and often no referral at all.

AI visibility is a distinct discipline with its own vocabulary: answer engine optimization (AEO), measured through mention rate, citation rate, and share of voice.

This is now core infrastructure to AI search visibility. G2 has partnered with Profound to give software vendors exactly this kind of AEO measurement. If you can’t produce a citation rate or share-of-voice number for your brand, you aren’t measuring the channel that’s deciding your pipeline.

3. Have you confronted the crawl-to-click gap?

Here’s the mindset shift that matters most. AI engines crawl your category’s content constantly, then resolve the buyer’s question inside the chat, where you can’t see it.

The reading is massive, while the click-through is a trickle. AI engines crawl sites orders of magnitude more often than they send a single visitor back.

This is a version of what analysts call the “great decoupling”. This means impressions rise while clicks fall. Google shows the same shape. SparkToro’s 2026 study found 68% of US searches now end without a click, and AI Overviews (which appear on roughly half of searches by some measures) cut click-through by about 60% when present.

So if your definition of success is still “sessions,” you’re tracking a number that may shrink, while your real influence may be growing. The deals still happen. You just can’t see the discovery anymore.

As G2’s Chief Innovation Officer, Tim Sanders, put it: “The Yellow Pages compressed the market into the big book. Google compressed it into the first page of results. Now, AI chatbots are compressing it into a single answer.”

4. Are you auditing every major engine, or just ChatGPT?

Engines build answers from different sources and disagree constantly. One analysis of 680 million citations found that Reddit made up about 47% of Perplexity’s top citations, but only about 11% of ChatGPT’s. A separate study found that only 11% of domains are cited by both ChatGPT and Perplexity.

You can be the confident first pick in ChatGPT and absent from Perplexity. ChatGPT is still dominant for B2B software research at 63%, but the field (Gemini, Perplexity, Copilot, AI Overviews, AI Mode, Grok) is fragmenting fast, and conversion differs by platform. A single-engine audit offers only a partial picture.

5. Can AI engines actually read your site?

You can have the best product page on the internet, and if the crawler can’t parse it, you don’t exist in the answer.

Three checks:

• First, are you being crawled at all? AI-traffic analytics show which bots hit your domain, and if OpenAI’s or Anthropic’s crawlers aren’t reaching your key pages, nothing downstream works.
• Second, is your content built for machine extraction? AI systems favor question-based headings, answers up front, concise paragraphs, comparison tables, and schema markup.
• Third, is it current? AI systems show a measurable freshness bias, so stale pricing and legacy screenshots weaken your citation suitability.

The test: View a key page to the text a crawler sees and ask whether a machine could extract a clean, accurate, and self-contained answer.

6. Is your story consistent everywhere AI is reading?

AI models learn what you do by reconciling everything written about you into one synthesis. When your sources contradict each other, the model picks one (maybe the wrong one) or hedges, and a hedge is not a recommendation.

Your home page, your G2 profile, your LinkedIn, your docs, your pricing, third-party listicles, and community threads all feed the same answer. If your site says “AI-powered revenue platform” while your category listing and old posts say “sales engagement tool,” you’ve handed the model a choice and lost control of your positioning.

As Andy Crestodina of Orbit Media Studios argues, “Marketers have a new job: Train the AI to know all the key aspects of our brands.” Taglines don’t train the AI; consistent, structured truth does.

Your own site is only about a quarter of the citation equation, and the most-cited single domain on any platform rarely tops 5%. AI answers are assembled from a long tail of sources, so consistency across many places beats perfection in one.

7. Do you know your share of voice in the sources AI cites, especially reviews?

Since roughly three-quarters of your citations come from places that aren’t your website, the real question is: Do I show up where the AI is looking?

For B2B software, two source types dominate: peer review platforms and community discussion.

On reviews, the evidence is specific. Independent AI-visibility research has repeatedly found G2 the most-cited software review platform. Radix’s analysis of 10,000+ AI searches found that G2 carried 22.4% influence on software queries, the highest of any source.

The reason is this: AI models face a verification problem and need scalable, trustworthy, machine-readable quality signals. Platforms combining verified buyers and steady review velocity supply that.

As for community, the picture is complex. Reddit is consistently among the most-cited domains, which means in many categories, your product gets characterized by anonymous, upvote-driven threads.

The audit step: Find out which third-party and community sources the engines actually pull from in your category, and whether your brand appears in them at all.

8. Are you treating reviews as an AI-visibility input?

We analyzed 30,000 AI citations and share-of-voice data across 500 categories and found a small but statistically reliable link between review volume and citations: categories holding 10% more reviews saw roughly 2% more citations. So volume genuinely matters.

But the same study is clear that reviews explain less than 2% of the variance in citations. The rest is brand authority, content quality, training data, and cross-web mentions.

So it isn’t quantity or quality. It’s position, meaning where you sit relative to competitors. Holding 200 reviews in a 500-review category is very different from 200 in a 5,000-review category. And quality is what makes volume durable, because the current activity from verified buyers is one of the specific signals that makes a source trustworthy to a model.

Velocity is most visible in new categories. For example, G2’s AI Hub draws on 48,000+ verified AI software reviews submitted between May 2025 and April 2026 across 85 AI categories, many of which barely existed a year earlier. In a category forming that fast, your relative review position can move in a single quarter. This cuts both ways: It’s an opening if you act and a liability if you don’t.

9. Are you tracking sentiment and accuracy?

An answer that names you while repeating an outdated weakness or pinning a competitor’s flaw on you can do more harm than being left out. And because answers are downstream of your sources, that sentiment reflects your reviews, threads, and press.

Most teams that check AI visibility stop at presence. They ask: Am I in, yes or no?

But that’s half the audit.

The other half is about reading the adjectives. Is the language positive, neutral, or negative? How does it compare to rivals in the same answer? Is your pricing current, your category right, and your capability represented?

AI gives buyers the mean synthesized impression of your product, which may lag well behind where it actually is today.

10. Do you have an AI search owner, a baseline, and a review cadence?

The last check is organizational, and it decides whether the other nine ever get fixed. AI-led discovery cuts across SEO, content, PR, product marketing, reviews, and community. Because it touches everything, it tends to belong to no one in most teams.

Consider these questions:

• Is there a single accountable owner whose job is to understand how AI shapes the discovery of your brand and orchestrate the response?
• Do you have a baseline, a documented snapshot of your current visibility, share of voice, sentiment, and accuracy to measure change against?
• Do you have a review cadence, a regular review where these numbers get examined, like pipeline or spend?
  

How to read your score

Add up your ten answers.

• 0–6 points: Invisible. You’re effectively absent from the channel forming your buyers’ shortlists. Start measuring this week, because you can’t manage what you’ve never looked at.
• 7–13 points: Aware but exposed. You’ve seen the problem, but your visibility is partial, and your story is probably inconsistent across the sources AI reads. You’re losing winnable deals in answers you never see.
• 14–17 points: Instrumented. You measure AI visibility as its own discipline, across engines, and know roughly where you stand. The work now is consistency, share of voice, and turning measurement into improvement.
• 18–20 points: Referenceable. You track presence, position, sentiment, and accuracy across engines, with an owner and a cadence. You’ve made the shift from chasing rankings to being the brand AI engines trust enough to recommend. Keep defending it.

The good news? None of these ten gaps need more budget. They just need an owner who treats AI visibility as a number to move.

So take your lowest-scoring check and start there this week, before the prompt volume in your category doubles again and the gap compounds. The answer engine is describing you to a buyer right now, and indifference just hands the narrative to whoever showed up. You now know exactly where you’re losing. The next step is to take accountability and bridge the gaps.

AI agents are transforming into infrastructure from standalone apps. Learn how the category is evolving and how buyers are selecting AI agents in this article.