AI Search Visibility Benchmarks 2026

TL;DR: AI Search Visibility Benchmarks 2026 for Founders

Table of Contents

AI Search Visibility Benchmarks 2026 in 2026 show you how often ChatGPT, Gemini, and Perplexity name your brand when buyers ask for help. You can stop guessing and start fixing the gap that costs you buyers.

AI shortlists reward clarity, not fame. In Arobis AI's CRM test, Copper, a smaller brand, hit 100% shortlist frequency while Salesforce reached 94%. Well-known ClickUp made only 11% of project management shortlists.
The numbers to beat: MaxAEO data puts the median brand at about 31% of non-branded AI answers. The top quartile reaches 58% or more. Targets also shift by industry: about 44% counts as top quartile in finance, while publishing needs about 72%.
AI platforms disagree with each other. In the UK, any two AI engines share only 63 to 70 of their top 100 brands. If you check ChatGPT alone, you miss about a third of the picture.
What you get: a simple mention-rate formula, rules for picking the right peer group, and a 90-day plan sized for small teams. Underdogs can compete here, as this guide on how small teams beat giants in AI search shows.

This week, write 20 non-branded buying prompts in your customers' own words. Run them on three engines and see which side of 31% you land on. Then check your results against these AI visibility mistakes before you plan your next 90 days.

Check out latest news, stats and trends that you might like:

Dark Social Traffic Statistics (2026) | STARTUP EDITION

When the dashboard looks amazing but ChatGPT still thinks your startup is a typo for your competitor. Unsplash

AI Search Visibility Benchmarks 2026 finally give founders a number for a question most of us have been guessing at: how often does an AI assistant actually NAME your brand when a buyer asks for help? Across MaxAEO’s aggregate tracking, the MEDIAN brand earns a mention in roughly 31% of relevant, non-branded AI answers, while top-quartile brands reach about 58% or more. That spread decides whether your company lands on the shortlist a buyer builds before visiting a single website. In the B2B SaaS shortlists tested by Arobis AI, the gap between the most and least recommended CRM vendors was a brutal 94 POINTS.

If your brand shows up in one out of four AI answers, is that healthy or a warning sign? The answer depends on three decisive variables: your industry, the AI engine your buyers actually use, and whether you measure non-branded prompts or prompts that already contain your name. Comparing yourself with a generic “AI visibility average” is like comparing a bakery’s margins with a software company’s margins. This guide compares AI visibility across industries, software categories, AI platforms, and markets (UK, US, and what it means for EU founders). Then it shows exactly what to do when your result sits below, near, or above the benchmark.

I am Violetta Bonenkamp, also known as Mean CEO. I run ventures in parallel: CADChain, a deeptech company that builds IP protection for CAD and 3D files, and Fe/male Switch, a startup game and online incubator for women founders. Both depend on being discovered by people who have never heard of us, and in 2026 a growing slice of that discovery happens inside ChatGPT, Gemini, Perplexity, Claude, Copilot, and Google AI Mode. So I read these benchmarks the way a founder reads a bank statement. What does this number mean for runway, pipeline, and the next 90 days? Let’s break it down.

What do the AI Search Visibility Benchmarks 2026 show at a glance?

Here is the answer before the analysis. The table below pulls together the most decision-relevant numbers published in 2026. Where a source did not publish a quartile, I say so instead of filling the gap with a guess. The “median of listed vendors” figures for the three SaaS categories are my own calculation from the Arobis AI tables, so treat them as an analytical lens, not an official statistic.

Metric or segment	Low end	Median	Top quartile or high end	Best-fit comparison group	Source period
Non-branded brand mention rate, blended across 8 engines	Not published	~31%	~58%+	Any brand, cross-industry	Rolling 90 days into mid-2026 (MaxAEO)
Mention rate, finance brands	Not published	Not published	~44%	Finance	Mid-2026 (MaxAEO)
Mention rate, publishers	Not published	Not published	~72%	Publishing and media	Mid-2026 (MaxAEO)
CRM shortlist frequency (8 listed vendors)	6%	100% (author’s calc)	100%	B2B SaaS, CRM buyers	2026, 18 prompt-runs, Llama 3.3 70B (Arobis AI)
Project management shortlist frequency (10 listed vendors)	11%	75% (author’s calc)	100%	B2B SaaS, PM tools	2026, 18 prompt-runs (Arobis AI)
Marketing automation shortlist frequency (8 listed vendors)	28%	89% (author’s calc)	100%	B2B SaaS, marketing tools	2026, 18 prompt-runs (Arobis AI)
Top-100 brands shared by any two AI platforms, UK	63	Not published	70	UK market	January to July 2026 (Semrush)
Top-100 brands shared by any two AI platforms, US	70	Not published	78	US market	January to July 2026 (Semrush)

Quick read: A B2B startup is broadly on track when its non-branded mention rate sits between 31% and 58% on the main engine its buyers use, provided that the prompt set reflects real buying questions and the result holds on more than ONE platform.

Two things jump out immediately. First, the “median” changes dramatically depending on who is in the sample: a panel of hundreds of tracked brands lands near 31%, while a shortlist of category leaders lands near 100%. Second, AI platforms disagree with each other far more than most founders assume. In the UK, two platforms share only 63 to 70 of their top 100 mentioned brands. That means roughly a third of the brands one engine loves are invisible on another.

What exactly does an AI search visibility benchmark measure?

MaxAEO put it better than anyone this year: “A benchmark is not the same as a metric. Your mention rate is your number; the benchmark is the distribution it sits inside.” A 40% mention rate could mean you are winning or losing badly. It depends entirely on whether your strongest competitor sits at 25% or 70%. So before comparing, let’s define the four metrics that show up across the 2026 reports.

Definitions

Mention rate measures how often your brand appears in AI-generated answers to relevant prompts that do NOT contain your brand name. It tells you whether AI engines consider you part of the category conversation.
Recommendation frequency (the Arobis AI version) measures the share of standardized buying prompt-runs in which a vendor lands on the AI’s shortlist. Arobis states clearly that these frequencies are per standardized prompt set and do not represent share of sales.
Citation share measures how much of the available citation space your site wins. Microsoft defines its Bing version as the percentage of citations attributed to your site out of all citations shown across all sites for the same grounding query (the prompt or search term Copilot uses to fetch results).
AI impressions and AI referral sessions count how often your pages appear inside AI features and how many people actually click through. GA4 referral sessions are the only one of these that connects to revenue.

Formulas

Mention rate (%) = answers that name your brand / total relevant non-branded answers × 100

Recommendation frequency (%) = prompt-runs where you are shortlisted / total prompt-runs × 100

Citation share (%) = citations pointing to your site / all citations shown for the same query × 100

AI referral conversion rate (%) = conversions from AI referral sessions / AI referral sessions × 100

Included and excluded

Included: non-branded category prompts (“best CRM for a 5-person agency”), comparison prompts, and problem prompts your buyers actually type.
Excluded: branded prompts (“is [your brand] good?”), navigational queries, and prompts written by your own team to flatter the result.
Reporting frequency: weekly for tracking, monthly for decisions. MaxAEO runs its panel daily and reports a rolling 90-day window, and Semrush reports fixed monthly prompt sets.
Counting basis: one answer counts once, no matter how many times your brand appears inside it. Record position separately if you care about first-mention status.

Example calculation a founder can reproduce in a spreadsheet

A bootstrapped invoicing SaaS in the Netherlands writes 20 non-branded buying prompts and runs each one on ChatGPT, Gemini, and Perplexity. That gives 60 answers. The brand appears in 15 of them. Its mention rate is 15 / 60 × 100 = 25%. Compared with the blended median of ~31%, this result is below the benchmark, and far below the ~58% top quartile.

Now add one more step that almost nobody does: split the 25% by engine. If the brand gets 10 of 20 on Perplexity but 2 of 20 on ChatGPT, the “25%” is hiding two completely different stories. That split is where the real work starts.

Where do these benchmarks come from, and how much should you trust them?

I have spent years in EU grant programs and accelerators where every number in a pitch deck gets interrogated. So here is the same interrogation applied to the 2026 AI visibility data. The sources differ wildly in sample, engine coverage, and geography, and those differences explain most of the apparent contradictions.

Arobis AI, State of AI Search Visibility 2026: 3 SaaS categories (CRM, project management, marketing automation), 6 buying prompts per category, 18 prompt-runs per category, 54 prompt-runs in total, ONE model tested (Meta’s Llama 3.3 70B). Statistic type: frequency per vendor. Read the Arobis AI State of AI Search Visibility 2026 leaderboard for the full tables.
MaxAEO, AI Visibility Benchmarks 2026: a fixed panel of category prompts run daily against 8 engines (ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, Google AI Mode, AI Overviews), aggregated across hundreds of tracked brands over a rolling 90-day window into mid-2026. Statistic type: median and top quartile. See the MaxAEO mention rate benchmarks by industry.
Semrush AI Visibility Index 2026 (flagship): more than 126 million real US AI search prompts across 22 industries and 4 AI platforms, published June 2026. It identifies a “Universal 36” group of brands that won every month. Details live on the Semrush AI Visibility Index study page.
Semrush UK benchmark (October 13, 2026): seven months of data (January to July 2026), fixed monthly sets of 2.5 million UK and 25 million US prompts, covering ChatGPT, Gemini, and Google AI Mode plus a larger variable dataset of Google AI Overviews results. Coverage via the Semrush 2026 AI Visibility Index UK benchmark press release. Note that this is a paid press release distributed by Business Wire.
Supermetrics measurement guide: documents the new Google Search Console and Bing Webmaster Tools AI reports. See how to measure and report AI search visibility in 2026.
MADX Digital, AI Search 2026 Benchmark Report: B2B SaaS focus, measures share of voice, citations, and brand mentions across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Available as the MADX AI Search 2026 Benchmark Report download.

Why the sources seem to disagree

Arobis AI shows FIVE CRM vendors at 100%, while MaxAEO says the median brand is mentioned only 31% of the time. Both can be true. Arobis reports the vendors that made the shortlist on a single model, so the sample is already filtered toward winners. MaxAEO tracks hundreds of brands of all sizes across eight engines, including the long tail that rarely gets named. For a founder, MaxAEO is the more honest reference for “where do I stand”, and Arobis is the better reference for “what does winning in my category look like”.

Benchmarks are reference points, not universal targets. A healthy result depends on a startup’s stage, model, growth strategy, margins, geography, and access to capital.

Which three AI visibility trends define 2026?

Arobis AI summarizes the year in three shifts: answers are replacing clicks, AI referral traffic is compounding, and recommendation share has decoupled from how famous a brand is. The third one is the most provocative for small companies, and the data backs it up.

Copper hit 100% CRM shortlist frequency, while Salesforce, the most famous CRM brand on the planet, hit 94%. In plain numbers, Salesforce missed 1 of 18 runs and Copper missed none.
Basecamp reached 100% in project management, while Monday.com reached 67%, Notion 39%, and ClickUp only 11% (2 of 18 runs).
Salesforce Marketing Cloud sat at 28% in marketing automation, below Ortto at 61% and Mailchimp at 72%.

Here is why this matters to a founder with a tiny marketing budget. If AI shortlists tracked fame and ad spend, small companies would have no chance. The 2026 data suggests something else is at play: how clearly your product is described as a category answer across the sources models learn from and retrieve. Treat that as a hypothesis, since Arobis tested one model on 54 runs. But it is the most hopeful hypothesis for underdogs I have seen in a while.

One more quiet detail worth your attention: with 18 runs per category, every single run moves a vendor’s score by about 5.6 percentage points. A 6% score means ONE appearance. Small samples make for dramatic headlines, so read single-model studies as directional signals, not verdicts.

How do you choose the right peer group for AI visibility?

A benchmark becomes useful only when the comparison group resembles your company. These are the dimensions that materially change AI visibility numbers.

Dimension	Suggested segments	Why it matters for AI visibility
Industry	Finance, healthcare, publishing, B2B SaaS, ecommerce	MaxAEO’s top quartile ranges from ~44% in finance to ~72% in publishing.
AI engine	ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, AI Mode, AI Overviews	Visibility on one platform does not guarantee visibility on another (Semrush).
Geography and language	UK, US, EU country markets, non-English prompts	UK platforms agree less than US platforms on which brands to mention.
Prompt type	Branded vs non-branded, problem vs comparison prompts	Branded prompts inflate mention rates and hide discovery problems.
Stage	Pre-revenue, seed, Series A, profitable	Earlier companies have fewer third-party sources for AI engines to cite.
Funding model	Bootstrapped, grant-funded, VC-backed	Paid tools and PR budgets change how fast you can build citations.
Customer type	SMB, mid-market, enterprise, consumer	Enterprise buyers phrase prompts differently from solo freelancers.
Team structure	Solo founder, small team, marketing department	Capacity to run prompt panels and publish source content varies.

Best-fit rule: Compare your startup first by industry, then by the AI engine your buyers use most, and only then use the blended 31% median when no closer peer data exists.

Do AI visibility benchmarks change by startup stage?

Straight answer: none of the 2026 sources publish stage-level AI visibility benchmarks. Every number below comes from blended or category data, and the “reasonable reference” columns are my working guidelines as a founder, clearly labeled as such. I would rather give you an honest frame than a fake percentile.

Pre-revenue and pre-seed

Metric	Reasonable reference	Stretch reference	Context
Non-branded mention rate on your 10 to 20 niche buying prompts	Any presence above 0%	Approach the ~31% blended median on niche prompts	Author’s guideline; no published stage data

At this stage the goal is learning speed, not a percentile. You are testing whether AI engines can even describe the problem you solve, and whether they name any competitor at all. If they name nobody, you may have found an empty category or a problem buyers do not phrase the way you do. Both answers are worth money.

Founder moves:

Write prompts in your customers’ words, taken from real discovery calls, and record which brands AI names. That list is your true competitive set.
Run the panel manually in a spreadsheet before paying for any tool. My rule is “default to no-code until you hit a hard wall”, and a Google Sheet is no-code.
Log the exact prompt, engine, date, and answer text so you can compare cleanly next month.

Seed stage

Metric	Below reference	Median reference	Top quartile reference	Context
Non-branded mention rate, main buyer engine	Under ~31%	~31%	~58%+	MaxAEO blended data, not seed-specific

At seed stage, consistency matters more than the absolute number. A brand that appears in 30% of answers every week is in a stronger position than one that spikes to 50% and falls to 10%. Watch the trend across at least three monthly snapshots.

Below the median: find the five prompts where competitors appear and you do not, then publish one answer-first page per prompt.
Near the median: check which engine pulls the average down, and focus there.
Top quartile: protect it by tracking citation sources, since a single deleted listicle can erase a chunk of your visibility.

Series A and growth stage

Metric	Low end	Median of listed leaders	High end	Context
Category shortlist frequency (B2B SaaS)	6% to 28%	75% to 100%	100%	Arobis AI, 18 runs per category, Llama 3.3 70B; author’s median calc

Once you compete with category leaders, the shortlist is the battlefield. In CRM, five vendors hit 100%, so being “frequently recommended” no longer differentiates anyone. The question becomes whether AI places you first, describes you accurately, and recommends you for the segment you actually want to win.

Fix the constraint that hurts most: usually inaccurate descriptions of pricing or ideal customer.
Keep entity information consistent across your site, review platforms, and directories as you scale content.
Validate that wins hold across engines, not just the one your team checks every morning.

Bootstrapped and profitable startups

Bootstrapped companies should judge AI visibility by cash, not by vanity. A 25% mention rate that sends converting buyers beats a 60% rate built on prompts nobody with a credit card types. Indexly cites a claim that AI search traffic converts at 4.4 times the rate of traditional organic search, but the claim is not attributed to a primary study, so test it against your own GA4 data before betting on it.

Track AI referral sessions and conversions in GA4 from day one.
Prioritize prompts with clear buying intent (“best X for Y”, “X alternative for Z”).
Do not copy the content volume of VC-backed peers; a handful of precise pages can beat a hundred generic ones.

How does AI visibility differ by industry and business model?

The 2026 data segments by industry and SaaS category rather than by classic business model. Here is what is consistently available.

Industry or category	Benchmark	What shapes the number	Watch-out
Publishing	~72% top quartile mention rate	Publishers are themselves the sources AI engines cite	High bar: 31% is mediocre here
Finance	~44% top quartile mention rate	Cautious answers in regulated topics	Do not judge finance brands against the blended 58%
Healthcare	~31% described as excellent	AI engines hedge on medical advice	Branded prompts may hide weak discovery
B2B SaaS, CRM	6% to 100% shortlist frequency	Clear category definitions, many comparison pages	Five-way tie at 100% on one model
B2B SaaS, project management	11% to 100% shortlist frequency	Widest spread of the three categories	Popular tools like ClickUp and Notion scored low
B2B SaaS, marketing automation	28% to 100% shortlist frequency	Long-established vendors dominate	Sub-brands of big suites can underperform

Three differences stand out. Publishing sits far above everyone else because AI engines lean on publishers to build answers, so a publisher competes on a different playing field. Finance and healthcare sit lower because engines hedge in regulated topics, which means a 31% healthcare brand may be outperforming a 50% SaaS brand relative to its peers. And within SaaS, project management has the widest spread, which tells me the category is less settled in the models’ minds and more open to newcomers.

Are US AI visibility benchmarks valid for European startups?

Only partly, and the 2026 Semrush UK benchmark is the clearest proof. Semrush, now an Adobe company, found “there is no single, global approach by which AI visibility is achieved.” The brands mentioned, the sources cited, and the consistency between platforms all differ by market.

Region	Benchmark	Sample period	What may explain the difference
United Kingdom	63 to 70 of top 100 brands shared by any two platforms	January to July 2026	Smaller prompt base (2.5M monthly), more fragmented source pools
United States	70 to 78 of top 100 brands shared by any two platforms	January to July 2026	Larger prompt base (25M monthly), more uniform English-language sources
European Union	No published EU benchmark in the 2026 sources reviewed	n/a	Language diversity, smaller national markets
Global (14 countries)	Query language reshapes social citation rates across 7 models	2026, 3.25 billion citations analyzed	Engines respond to non-English prompts in very different ways

The global row comes from vendor research summarized in this ranking of AI visibility platforms by AEO score, which reports that Google AI Overviews and ChatGPT treat non-English prompts in fundamentally different ways. For EU founders, that is the headline. My linguistics background makes me stubborn here: a Dutch buyer asking about “boekhoudsoftware voor zzp’ers” is not asking the same question as an American asking about “bookkeeping software for freelancers”, even when the meaning overlaps. Different words pull different sources, and different sources produce different shortlists.

Regional factors EU founders should account for:

Language: run separate prompt panels per language, not translated copies of your English set.
Local sources: national review sites, chambers of commerce, and local media may carry more weight for local prompts.
Privacy rules: GDPR affects how you can track AI referral visitors in analytics, so check consent settings before trusting GA4 numbers.
Market size: fewer prompts and fewer sources per country means more volatility month to month.
Cross-border sales: a brand visible in Germany can be invisible in France on the very same engine.

Which AI platform matters most for your brand?

The one your buyers use. That sounds obvious, yet most founders check ChatGPT and stop. Semrush found that Gemini mentions more distinct brands per answer than ChatGPT, while ChatGPT cites the most sources per answer of the four platforms studied. Those are two different games.

On Gemini, more brands per answer means more seats on the shortlist, so a newcomer has a better chance of getting named.
On ChatGPT, more sources per answer means citations matter: getting your page cited may be the easier path than getting your brand named.
On Google AI Overviews and AI Mode, Google Search Console now reports impressions, broken down by page, country, device, and date, but with NO clicks, click-through rate, or query data.
On Microsoft Copilot, Bing Webmaster Tools added an AI Performance report in public preview in February 2026 and Citation Share on June 16, 2026.

MaxAEO’s advice fits here neatly: a good score is one that clears your industry’s top quartile on your buyers’ MAIN engine for non-branded prompts. Benchmark against your vertical, never the global average.

What does a good AI visibility score look like in 2026?

Here are performance bands based on MaxAEO’s blended distribution. Shift them down for finance or healthcare and up for publishing.

Performance band	Non-branded mention rate	Interpretation	Appropriate response
Needs investigation	Below ~31%	Below the median brand; AI may not link you to the category	Audit which prompts name competitors but not you
Within benchmark	~31% to ~58%	Typical peer performance	Close engine-specific gaps
Strong	~58% and above	Top quartile in blended terms	Protect citation sources, extend to new languages
Exceptional or anomalous	Near 100% across all prompts	Unusually high; verify prompt quality	Check for branded or overly narrow prompts

Above benchmark is not automatically healthy. A 90% mention rate on prompts like “tools similar to [your brand]” proves nothing about discovery. And a high mention rate paired with inaccurate descriptions (wrong pricing, wrong audience) can send AI-referred buyers to your site with the wrong expectations, which shows up later as low conversion and churn.

How do you diagnose your AI visibility result?

If you are below the benchmark

Verify the calculation, the period, and that your prompts are truly non-branded.
Confirm your peer group: industry first, then engine, then market.
Split the result by engine, prompt type, and language.
Find the one gap you can control most: missing comparison pages, weak third-party mentions, or inconsistent product descriptions.
Run one bounded experiment for 30 to 60 days and re-measure on the same prompt set.

If you are near the benchmark

Check the three-month trend, not just this month’s snapshot.
Compare by engine, product line, customer segment, and country.
Lift the weakest engine without losing ground on the strongest.
Set targets based on pipeline economics, not a percentile alone.

If you are above the benchmark

Confirm your definition matches the source definition (non-branded, relevant, same engines).
Check that the result survives across months and engines.
Identify which sources AI cites when it names you, and document why they exist.
Expand to new languages or segments while watching accuracy and referral conversion.

Which metrics should you read alongside AI visibility?

Main benchmark	Pair with	Why
Mention rate	Answer accuracy, position in answer	Being named last with wrong pricing is weaker than being named first correctly.
Recommendation frequency	Cross-engine consistency	A 100% score on one model may not hold on others.
Citation share	AI referral sessions (GA4)	Citations without visits do not pay salaries.
AI impressions (Search Console)	Organic clicks and conversions	Answers are replacing clicks, so impressions may rise while traffic falls.
AI referral sessions	Conversion rate and revenue per visit	The only metric of the four tied directly to revenue.
Overall visibility score	Per-language and per-market breakdown	UK and US results differ, and EU languages differ even more.

Do not chase mention rate in isolation. A change is healthy only if answer accuracy and AI referral conversion stay within acceptable ranges.

This is the game-design lesson I keep repeating at Fe/male Switch: “Gamification without skin in the game is useless.” A visibility score you can raise without changing a single buyer’s decision is a badge, not an asset. Tie every visibility goal to an outcome you can bank.

What mistakes do founders make with AI visibility benchmarks?

Testing branded prompts and celebrating. “What is [my brand]?” will almost always mention you. That tells you nothing about discovery.
Checking one engine. Semrush shows visibility on one AI platform does not guarantee visibility on another.
Using the global average. 31% is excellent for healthcare and mediocre for a publisher.
Trusting vendor case studies at face value. Goodie reports Rathbones saw 2.3x AI visibility growth and Dermalogica an 85% uptick in AI searches. These are named and checkable, which is good, but they are still the vendor’s own wins.
Treating Search Console AI impressions as traffic. They carry no click data. Use GA4 referrals for the click side.
Translating English prompts for EU markets. Native phrasing pulls different sources.
Buying an enterprise tool before running a manual panel. You will not know what to configure until you have looked at 50 answers yourself.

What does a 90-day AI visibility improvement plan look like?

Days 1 to 14: measure

Lock your definition: non-branded mention rate on a fixed panel of 20 to 30 buying prompts.
Run the panel on at least three engines your buyers use and record every answer.
Pick your peer benchmark: industry top quartile on your main engine.
List which competitors appear, which sources get cited, and how AI describes you.

Days 15 to 45: test

Choose ONE gap and one hypothesis, such as “a comparison page answering prompt X will get us cited on ChatGPT”.
Apply the GEO moves Supermetrics lists: answer-first structure, inline sources on statistics, consistent entity naming, and schema markup.
Set a stop condition and a guardrail (accuracy of AI descriptions).
Pitch one or two third-party sources that already get cited for your category.

Days 46 to 75: improve

Re-run the identical prompt panel and compare engine by engine.
Keep, revise, or kill the test based on evidence.
Repeat the winning move for another prompt cluster or language.

Days 76 to 90: standardize

Document what worked as a repeatable checklist for every new page.
Hand the weekly prompt run to an AI agent or a no-code automation, with a human reviewing answers monthly.
Set next quarter’s target using your own trend plus the external benchmark.
Book a quarterly benchmark review in the calendar now.

This is how I think about AI in my own companies: a force multiplier for small teams, with a human in the loop. The agent runs the prompts and collects the answers. You decide what the answers mean and what to build next.

How should different founders use these benchmarks?

Bootstrapped startups

Measure AI referral conversions before mention rate; cash pays for everything else.
Compare against capital-efficient peers in your niche, not category giants at 100%.
Start with free checks such as the AI visibility checkers offered by Arobis AI and Frase before paying for subscriptions.

VC-backed startups

Pair visibility growth with spend on content and PR so investors see the cost of each percentage point.
Consider paid tracking at scale: Ahrefs Brand Radar AI starts at $199 per month as an add-on and covers ChatGPT, Perplexity, Gemini, Copilot, Grok, AI Overviews, and AI Mode, built on 405M+ search-backed prompts.
Separate temporary visibility pushes from durable source-building.

Women-led and underrepresented founders

None of the 2026 datasets segment by founder demographics, so nobody knows yet whether AI engines treat underrepresented founders’ brands differently. Treat that as an open question, not a settled fact.
Capital gaps limit PR budgets, and that is structural, not personal. As I say often, “Women do not need more inspiration; they need infrastructure.”
Focus on moves that cost time rather than money: precise comparison pages, consistent entity data, and contributions to sources AI already cites.

Solo founders and freelancers

Track three numbers only: mention rate on your main engine, AI referral sessions, and AI referral conversions.
Use a 15-prompt panel you can run in under an hour each month.
Pick improvements that reduce recurring work, such as a single strong service page that answers your top buying prompt.

EU startups

Build one prompt panel per language and per country you sell in.
Label US-based benchmarks clearly in board decks; the Semrush data shows UK platforms agree less than US ones, and continental markets add language on top.
Check GDPR consent setup before trusting AI referral numbers in analytics.

What are the data gaps and caveats in 2026 AI visibility research?

Small samples: Arobis AI tested 54 prompt-runs on a single model. Each run moves a vendor’s score by about 5.6 points.
Survivorship bias: category shortlists show vendors that got named; brands never mentioned are invisible in the tables.
Selection bias: MaxAEO aggregates brands that pay for or use its monitoring, which may skew toward brands already investing in AI visibility.
Definition mismatch: mention rate, recommendation frequency, share of voice, and citation share are four different metrics. Never mix them in one comparison.
Stage mixing: no source separates pre-seed startups from public companies.
Geographic bias: the 126 million prompt Semrush flagship study is US-only, and the only market comparison published is UK vs US.
Funding bias: no dataset segments by bootstrapped vs VC-backed.
Industry bias: B2B SaaS dominates the category-level studies.
Time sensitivity: models update constantly, so a mid-2026 snapshot may age quickly.
Vendor incentives: most publishers of these benchmarks sell AI visibility tools or audits.

Arobis AI reports CRM leaders at 100% shortlist frequency, while MaxAEO reports a 31% median mention rate. The gap reflects sample design: a winners’ shortlist on one model versus hundreds of brands across eight engines. For most founders, MaxAEO’s distribution is the more relevant reference because it includes companies like theirs.

Quotable benchmark insights

“Moving from the 31% median to the 58% top quartile means AI names your brand in nearly twice as many non-branded buying answers.”
“In UK AI answers, roughly a third of the top 100 brands one platform mentions are missing from another platform’s top 100.”
“A 31% AI mention rate is excellent in healthcare and mediocre in publishing, so compare by industry before you panic or celebrate.”
“In the 2026 Arobis AI study, Copper matched HubSpot at 100% CRM shortlist frequency while Salesforce reached 94%: AI shortlists do not simply mirror fame.”
“Google’s AI reports count impressions without clicks, so GA4 referral data is still the only AI visibility number tied to revenue.”

Founder AI visibility benchmark worksheet

Copy this into a spreadsheet and fill it in this week.

Field	Your startup	Best-fit benchmark	Gap	Next action
Metric definition	Non-branded mention rate, fixed panel	MaxAEO definition	Match or mismatch	Standardize prompts
Current result	[Your %]	~31% median / industry top quartile	[Difference]	[Action]
Three-month trend	[Up / down / flat]	Not published	n/a	[Action]
Weakest engine	[Engine]	Cross-platform overlap 63 to 78 of 100	[Gap]	[Experiment]
Guardrail: answer accuracy	[% of answers describing you correctly]	Internal target	[Difference]	[Monitor]
Guardrail: AI referral conversion	[GA4 %]	Your organic conversion rate	[Difference]	[Monitor]
Review date	[Date]	n/a	n/a	[Owner]

Practical AI visibility checklist

Define your AI visibility metric in one sentence.
Use the same numerator, denominator, and period as your chosen source.
Record whether each benchmark is a median, quartile, frequency, or range.
Segment by industry, engine, language, and market.
Exclude branded prompts from your discovery score.
Keep the original source link for every benchmark you quote internally.
Note sample size and collection period next to each number.
Explain why two sources disagree before choosing one.
Pair visibility with answer accuracy and AI referral conversion.
Write actions for below-, near-, and above-benchmark results.
Label your own estimates as estimates.
Add a “last updated” date and schedule the next review.

Frequently asked questions about AI Search Visibility Benchmarks 2026

What is a good AI visibility score for a startup in 2026?

A good score clears your industry’s top quartile on your buyers’ main AI engine for non-branded prompts. In blended terms that is roughly 58% or more, against a median of about 31%. Industry changes the target: about 44% is top quartile in finance, while publishing demands about 72%.

Should founders use the average or the median?

Use the median. A handful of giants appearing in nearly every answer would pull an average upward and make typical brands look like failures. The median shows where the middle brand actually sits, and quartiles show what “strong” looks like.

How often should founders review AI visibility?

Run your prompt panel weekly or monthly, and review against external benchmarks quarterly. Models and their sources change often, so a single snapshot can mislead. Semrush publishes monthly benchmarks and MaxAEO uses a rolling 90-day window, which is a sensible rhythm to copy.

Why does our result differ from the benchmark?

The usual suspects are prompt design (branded vs non-branded), engine mix, language, industry, and sample size. A panel of 20 prompts on one engine will swing far more than a panel of thousands across eight engines. Check definitions before you change strategy.

Are US AI visibility benchmarks relevant in Europe?

The measurement methods transfer; the numbers do not. Semrush found UK platforms agree less than US platforms on which brands to mention, and research across 14 countries shows query language reshapes citations. EU founders should build per-language panels and treat US numbers as context only.

Can an above-benchmark AI visibility result still be unhealthy?

Yes. A brand can appear in 70% of answers while AI describes it with outdated pricing or the wrong target customer. That brings in mismatched visitors who do not convert. High visibility on ONE engine can also hide near-zero visibility on the engine your buyers prefer.

What is the difference between AI search visibility and GEO?

AI search visibility is the measurement: how often your brand and URLs appear in AI-generated answers. GEO (generative engine work, often written as generative engine optimisation) is the practice of improving that number through answer-first structure, inline sources on statistics, consistent entity naming, and schema markup.

Which AI visibility tool should a small team start with?

Start with a manual spreadsheet panel and free checkers, then upgrade when manual work becomes the bottleneck. The comparison of the 10 best AI visibility tools in 2026 and the review of AI search visibility tools for agencies are useful starting points, though both come from companies with their own products in the race.

Sources and update log

Arobis AI, “State of AI Search Visibility 2026,” 2026 edition. Sample: 3 B2B SaaS categories, 6 prompts each, 18 prompt-runs per category, Llama 3.3 70B. Metric: recommendation frequency per standardized prompt set. Arobis AI search visibility rankings and benchmarks
MaxAEO, Chris Han, “AI Visibility Benchmarks 2026: What a Good Mention Rate Looks Like by Industry,” mid-2026. Sample: hundreds of brands, 8 engines, daily panel, rolling 90 days. Metric: non-branded mention rate. MaxAEO AI visibility benchmarks by industry
Semrush, “AI Visibility Index 2026 Study,” June 2026. Sample: 126 million+ US prompts, 22 industries, 4 AI platforms. Semrush AI Visibility Index 2026
Semrush via Business Wire, “Semrush Releases 2026 AI Visibility Index, Revealing Its First UK Benchmark,” October 13, 2026. Sample: 2.5M UK and 25M US monthly prompts, January to July 2026. Semrush UK and US AI visibility comparison
Supermetrics, “How to measure and report AI search visibility in 2026.” Covers Google Search Console and Bing Webmaster Tools AI reports. Supermetrics guide to AI visibility reporting
MADX Digital, “AI Search 2026 Benchmark Report.” B2B SaaS, share of voice, citations, brand mentions. MADX B2B SaaS AI search benchmark
Nick Lafferty, “9 AI Visibility Optimization Platforms Ranked by AEO Score (2026).” Tool pricing, vendor case studies, multilingual citation research. AI visibility platforms ranked by AEO score
Frase, “The 10 Best AI Visibility Tools in 2026,” updated August 2026. Frase AI visibility tools comparison
Indexly, “Best AI Search Visibility Tools for Agencies (UPDATED 2026),” July 10, 2026. Indexly agency AI visibility tools review

Update log

Date	Change	Reason
October 2026	Initial publication	n/a
Next review: January 2027	Refresh mention rate and platform overlap data	New monthly and quarterly datasets expected

Next steps: what should you do this week?

Here is the provocative part. Most founders still measure success by Google rank, while their buyers are already reading shortlists written by ChatGPT and Gemini. If you rank on page one and never appear in AI answers, you are optimizing for a door fewer people walk through every month. The good news from the 2026 data is that AI shortlists do not simply reward the biggest budget. Copper sitting next to HubSpot at 100% is proof that clarity can compete with fame.

So treat this like a game with real stakes, the way I design every quest at Fe/male Switch. Write 20 non-branded buying prompts in your customers’ own words. Run them on the three engines your buyers use. Calculate your mention rate with the formula above, place it against the 31% median and your industry’s top quartile, and pick ONE 90-day experiment. Then come back in a quarter and compare. That is the whole loop, and it costs you an afternoon to start.

FAQ on AI Search Visibility Benchmarks 2026

How can small startups compete with category giants inside AI answers?

AI engines often cite Reddit threads, niche blogs, and forums, so transparent community participation can earn mentions without enterprise budgets. Answer real buyer questions in those spaces, disclose your affiliation, and target narrow or localized use cases that giants ignore. Clear positioning matters more than ad spend. See how small teams compete with giants in AI search

Which qualitative signals should founders track besides mention rate?

Track sentiment (whether AI describes you positively), prominence (named first or buried), and the authority of each citing source. A brand that is named often but framed as a "cheaper, limited alternative" still loses deals. Score every logged answer on a simple 1-to-3 sentiment scale. Avoid hidden mistakes that hurt brand AI visibility

How do I track AI referral traffic in Google Analytics 4?

Create a custom channel group in GA4 with a regex matching referrers such as chatgpt.com, perplexity.ai, gemini.google.com, copilot.microsoft.com, and claude.ai. Place it above the default Referral channel, then attach conversion events. Expect some undercounting, because some AI clicks arrive as direct traffic. Set up Google Analytics for startups correctly

Does ranking on Google's first page guarantee AI mentions?

No. Language models favor content that clearly explains entities, relationships, and category fit, not just ranking position. Treat LLM optimization as a separate layer. State who you serve, what you replace, and how you price in plain sentences that models can quote word for word. Follow the LLM optimization guide for AI visibility

How should startups structure pages so AI engines can extract and cite them?

Answer the query in the first two sentences, use question-based H2 and H3 headings, define ambiguous terms, and add tables and lists that are easy to parse. Group related pages into topic clusters so engines see depth. Add substantive FAQs with schema markup. Use the September 2026 GEO checklist for startups

Do directory listings and backlinks improve AI search visibility?

They can. AI engines build answers from third-party sources, and consistent listings reinforce your entity data. Prioritize directories where your competitors already appear, use identical company descriptions everywhere, and avoid low-quality link farms that only add noise. Explore a 1,500+ website directory for startup listings

Bing Webmaster Tools shows Citation Share for each grounding query, which is the search term Copilot uses to fetch results. Find queries where your pages earn impressions but a low share, and improve those pages first with tighter answers, fresher data, and clearer sourcing. Re-check monthly. Read Supermetrics' guide to AI visibility reporting

What can startups learn from brands that stay visible in AI answers every month?

Semrush identified a "Universal 36" group of brands that won AI visibility every month across 126 million US prompts. Leaders are defined by consistency, not spikes. Identify the steady performers in your category, work out which sources cite them, and build a similar source footprint. Download the Semrush AI Visibility Index 2026 study

How do I build a prompt panel that reflects real buyer questions?

Combine discovery-call language with prompt-volume data. Profound's dataset draws on 400M+ anonymized AI conversations, and Ahrefs Brand Radar uses 405M+ search-backed prompts. Mix problem, comparison, and "alternative to" prompts. Refresh about 20% of the panel each quarter while keeping a stable core for trends. Compare AI visibility platforms ranked by AEO score

How do I find which sources AI engines cite for my competitors?

Run comparison prompts that name competitors and log every cited URL. Domains that keep recurring are your citation targets. Free tools like Arobis AI's checker show AI citation sources and share-of-model analysis. Then pitch those publications, ask to be added to listicles, or earn reviews on cited platforms. Check your brand with the Arobis AI visibility audit