Most teams check their AI visibility once and call it a measurement. A single spot-check tells you where you stand today and nothing about whether anything you did worked. Measurement means the same prompts, the same engines, on a schedule, with competitors tracked alongside you. Across 63 sites we scored the average readiness score was 59 out of 100.
Updated October 2026 · benchmark n=63 sites · check your site ·
Measuring AI visibility means tracking how often AI assistants mention and recommend your brand versus competitors — your “share of model.” You cannot improve what you do not measure. This is the complete guide to measuring it and moving it.
What is AI visibility?
AI visibility, or share of model, is the share of AI answers in your category that name your brand. It is the AI-era equivalent of share of voice — a concrete metric you can baseline, track over time, and improve, instead of guessing whether AI recommends you.
How measurement works
You define the buyer questions worth tracking and your competitors, ask AI assistants those questions on a schedule, and record who gets named. From that you get two numbers: your mention rate (how often you appear) and your share of voice (your mentions versus all competitors).
The measurement playbook
This pillar is organized into two sub-topics: set up tracking, then improve and report on it.
- Track Your AI Visibility — How to set up and run measurement of your AI visibility: share of model, the right buyer prompts, and competitor tracking.
- Improve & Report Your AI Visibility — How to act on your AI-visibility data: the levers that increase AI mentions, and how to build a recurring report.
Why measurement comes first
Most teams have never checked whether AI assistants recommend them. The first measurement is usually a wake-up call, and it gives you the baseline every improvement is measured against — exactly as rank tracking did for classic SEO.
Do it with DigiJaws
The AI Visibility Tracker runs this end to end: enter your brand, buyer questions, and competitors, and track your mention rate and share of voice over time. Background: what AI search readiness is.
What is share of model?
The share of AI answers in your category that mention your brand versus competitors. It is the core metric of AI visibility.
How often should I measure?
Monthly is a good start; weekly if your category moves fast. AI answers shift as models and sites change, so the trend matters more than a single snapshot.
1. Improve & Report Your AI Visibility
Measurement only matters if you act on it. These guides cover the levers that increase how often AI recommends you, and how to turn it all into a recurring report your team tracks.
Start by measuring
New here? First set up tracking. Run it with the AI Visibility Tracker.
How to Build an AI Visibility Report
What gets reported gets managed. A short, recurring AI-visibility report turns one-off checks into a metric your team tracks like any other channel.
Steps
- Pick your core metrics: mention rate, share of voice, and competitor movement.
- Set a cadence — monthly is a good start, weekly if the category moves fast.
- Capture screenshots of the actual AI answers as evidence and context.
- Plot the trend so changes are obvious at a glance.
- Tie each change to an action you took, so you learn what moves the number.
- Share it with stakeholders alongside your other marketing metrics.
Why it matters
A single AI-visibility check is a snapshot; a recurring report is a system. The trend, not the snapshot, is what tells you whether you are winning.
Do it with DigiJaws
The AI Visibility Tracker stores your runs over time so the report builds itself. Back to the Measure AI Visibility playbook.
How to Improve How Often AI Recommends You
Once you can measure your share of model, you can move it. AI recommendations are driven by what credible sources say about you and how machine-readable your own claims are. Here are the levers, in order of impact.
Steps
- Earn third-party mentions. Generative engines weigh what other credible sites say about you. Digital PR, guest content, and being included in roundups move the needle most.
- Get into comparison and “best of” content. AI assistants frequently pull from listicles and comparisons; being present and well-described there directly affects who gets named.
- Make your claims machine-readable. Clear positioning, structured data, and an llms.txt help models understand and quote you accurately.
- Strengthen reviews and reputation — a core trust input for recommendations.
- Re-measure your share of model after each change to see what worked.
Why this order
AI answers are built from sources the model trusts. The fastest gains come from increasing how often credible third parties mention you, then making sure your own site confirms and structures those claims.
Do it with DigiJaws
Track your progress with the AI Visibility Tracker and fix your on-site signals with the SEO Agent. Back to the Measure AI Visibility playbook.
2. Track Your AI Visibility
You cannot improve what you do not measure. These guides set up repeatable measurement of your AI visibility: your share of model, the prompts to track, and how to watch competitors.
Then improve & report
Once you are measuring, learn to improve and report on it. Automate it with the AI Visibility Tracker.
How to Track Competitors in AI Answers
Knowing who AI recommends instead of you is half the battle. Tracking competitors in AI answers turns a vague worry into a number you can move.
Steps
- List your 3–5 direct competitors — the brands buyers actually weigh against you.
- Run your buyer prompts and record which competitors get named, and in what order.
- Calculate share of voice: your mentions divided by all brand mentions.
- Watch the trend over time, not just a single snapshot.
- When a competitor consistently wins a prompt, study their content and sources to learn why.
- Feed those lessons back into your own pages and outreach.
Why it matters
AI answers are a zero-sum shelf — a few names, then silence. Tracking competitors shows you exactly which prompts to fight for and who to learn from.
Do it with DigiJaws
The AI Visibility Tracker measures your share of voice against the competitors you choose. Back to the Measure AI Visibility playbook.
How to Choose the Right Buyer Prompts to Track
Your AI-visibility data is only as good as the questions you track. The right prompts mirror how real buyers ask AI in your category; the wrong ones flatter you but tell you nothing.
Steps
- Map your buyer journey: awareness, comparison, and decision questions.
- Phrase prompts exactly as a buyer would type them, not as marketing copy.
- Cover three types: category (“best X for Y”), use-case, and comparison (“X vs competitor”) prompts.
- Include the comparison prompts where competitors are most likely to be named.
- Lock a stable set of 5–10 prompts so you can track trends over time.
- Revisit the set quarterly as your market and language evolve.
Why it matters
If you only track prompts you already win, you learn nothing. Tracking the real, contested buyer questions shows you where you actually stand.
Do it with DigiJaws
The AI Visibility Tracker runs your chosen prompts and tracks them over time. Back to the Measure AI Visibility playbook.
How to Measure Your Share of Model (Step by Step)
Share of model is how often AI assistants mention and recommend your brand versus competitors. You can measure it by hand in 20 minutes, or automate it. Here is the manual method so you understand what the metric really is.
Steps
- Write 5–10 buyer questions a real customer would ask an AI — for example, “what is the best [your category] for [use case]?”
- List your 3–5 main competitors.
- Ask each question in ChatGPT and Perplexity, fresh, and record which brands get named.
- Calculate your mention rate: the share of questions where you appear.
- Calculate your share of voice: your mentions divided by all brand mentions (you + competitors).
- Repeat on a schedule (weekly or monthly) — answers shift as models and sites change.
What good looks like
There is no universal benchmark — what matters is your share versus your direct competitors, and the trend over time. The first measurement is your baseline.
Automate it
The AI Visibility Tracker runs all of this for you and tracks it over time. Concept background: what AI search readiness is. Back to the Measure AI Visibility playbook.
What sites actually get right
If you are setting a baseline, these are the marks to measure against. Pass rates across the 63 sites we scored. Average overall readiness score was 59 out of 100.
| Signal | Sites passing | Sites failing |
|---|---|---|
| Structured data | 59% | 41% |
| FAQ schema | 6% | 94% |
| Organization schema | 46% | 54% |
Sources & method
Pass rates come from DigiJaws scans of 63 sites. It is a modest sample drawn from sites submitted to our own checker rather than a random sample of the web, so treat it as indicative of sites that run an AI-visibility audit, not as a census. Figures are a snapshot of that scan set taken on 2 October 2026 (n=63), not a live read — the current values are always at bench-stats.
This guide consolidates material previously published across 7 separate pages on this site. Nothing was cut — the sections above are the same guidance in one document, which reads better and gives an answer engine a single place to cite. Live data: bench-stats. Related: all guides, the library, AI answers.
Frequently asked questions
How do you measure AI visibility?
Pick the buying-intent prompts your customers actually use, run them against the engines on a fixed schedule, and record which brands get named. Share of model, tracked over time, is the headline metric.
What is share of model?
The proportion of tracked prompts where an engine names your brand, measured against the competitors named instead of you. It is a share metric, so it moves when rivals gain ground even if you stand still.
How often should I re-check?
Weekly is enough for most teams. Model updates and content changes move results on that order, and anything more frequent produces noise you cannot act on.
Can I see this in Google Analytics?
Not reliably. AI referrals are under-attributed and often arrive without a referrer, which is why prompt-level tracking exists rather than waiting for analytics to show it.
Related: What is AI search visibility?
Measuring AI Visibility (Share of Model): The Complete Guide. DigiJaws, 2026. https://digijaws.com/guides/measure-ai-visibility/