Run a full AI visibility audit quarterly and a lightweight share-of-voice check monthly. AI answers change with every model update and retrieval refresh, far faster than search rankings, so quarterly catches structural shifts while monthly tracking catches movement early enough to act on. Increase frequency around a rebrand, a major content push, or a known model release.
This cadence keeps you informed without drowning in busywork. It fits the measurement layer of the AEO + GEO playbook, and it deliberately runs faster than a traditional SEO audit schedule.
The Short Answer
Two cadences cover most brands: a fast monthly pulse and a slower quarterly deep-dive, plus off-cycle checks when something material changes. The monthly run is your smoke alarm; the quarterly one is your inspection.
Why AI Needs a Faster Cadence Than SEO
Traditional rankings are relatively stable, so an annual technical audit with light monitoring works. AI answers are not stable: retrieval indexes refresh, models update, and the sources a system trusts shift underneath you.
An AI Overview or chat answer can change in weeks, not quarters. Auditing on an SEO timetable means you find out about a problem long after it has cost you visibility, and long after a competitor has moved into the answer.
The gap between “a model started recommending your competitor” and “you noticed” is measured in months if you audit like it’s SEO. Close that gap.
Monthly: Track Share of Voice
Every month, run your fixed query set across ChatGPT, Claude, Perplexity, and Gemini and log the results. This is fast, cheap, and catches movement early. The full method is in how to track your AI share of voice.
Monthly tracking is not a full audit. It is a smoke alarm, cheap, frequent, and designed to catch a problem while it is still small.
Quarterly: Run a Full Audit
Once a quarter, go deeper than the monthly check:
- Expand the query set temporarily to probe new angles and competitors.
- Analyse sources — where each assistant is pulling its information from.
- Check entity health — are your profiles, schema, and knowledge sources still consistent?
- Review accuracy and sentiment trends, not just mention rate.
- Compare against competitors so you know your position, not just your movement.
Event-Based Checks
Some moments justify an off-cycle audit regardless of the calendar:
- A rebrand or name change — the highest-risk moment for your entity, and the most likely to make AI get you wrong.
- A major content launch — to confirm new pages are being retrieved and cited.
- A known model release — behaviour can shift overnight when a new version ships.
- A competitor move — a rival’s campaign can change the recommendation set out from under you.
What to Check Every Time
Whatever the cadence, the core questions are constant. A repeatable audit answers all five:
- 1Presence — does each assistant mention you for your target questions?
- 2Accuracy — when it does, is the description correct?
- 3Competitors — who appears alongside or instead of you?
- 4Sources — where is the information coming from?
- 5Movement — how has all of that changed since last time?
A Repeatable Audit Template
The reason most brands audit inconsistently is that they treat each audit as a fresh project. The fix is a template you reuse unchanged, so the monthly run takes an hour and the quarterly run takes an afternoon. Structure it as a simple grid.
Down the side, list your frozen query set. Across the top, list the four assistants: ChatGPT, Claude, Perplexity, and Gemini. In each cell, record four things: whether you were mentioned, whether the description was accurate, which competitors appeared, and, where the assistant shows them, which sources it cited. A colour or a simple score in each cell turns the whole grid into a scannable heat map of where you stand.
Add three summary rows beneath it: your overall mention rate as a percentage, the count of accuracy errors, and the competitors that appeared most often. Those three numbers are the ones you actually watch month to month, and they map directly onto the three things worth scoring in your AI share of voice.
The value of a fixed template is that it removes judgement from the process. You are not deciding what to look at each time; you are filling in the same grid, which is exactly what makes the results comparable across months. Improvisation is the enemy of a trend line.
By Hand vs Paid Tooling
You do not need a tool to start, and starting by hand is genuinely better for the first few cycles because it teaches you what the answers actually look like. A spreadsheet and a disciplined hour a month will surface every pattern that matters.
Tools earn their place once the manual run becomes the bottleneck, when your query set grows past what you can run by hand, or when you want to track more competitors and surfaces than a person can log in an afternoon. At that point automation buys you scale and frequency, not insight you didn’t already have.
Start by hand, move to tooling when volume, not curiosity, forces it. The brands that learn the manual process first get far more out of the tools when they adopt them.
Who Should Audit More Often, and Who Can Do Less
The quarterly-and-monthly rhythm suits most brands, but the right frequency genuinely varies with your situation, and matching it to your circumstances saves effort without sacrificing safety. Brands in fast-moving, competitive categories, where new entrants and shifting reviews constantly reshape the recommendation set, benefit from tighter monthly tracking and a heavier quarterly review, because the ground moves under them faster. So do brands whose revenue depends heavily on being recommended, since for them a drop in AI visibility is a direct commercial risk rather than a soft signal. And any brand in a high-stakes category, where an inaccurate AI description could mislead a customer about health, money, or safety, should monitor accuracy especially closely, because the cost of a wrong answer is higher than a missed mention.
On the other end, some brands can comfortably do less. If you operate in a stable niche with few competitors and little AI answer activity, a lighter cadence, a quick monthly glance and a genuine deep-dive twice a year, may be all you need, provided you still keep the query set frozen so the occasional check remains comparable. The point is not to audit for its own sake but to match the frequency to how fast your particular corner of the answer landscape actually changes. Auditing a stable niche weekly wastes effort; auditing a volatile one annually courts nasty surprises.
Whatever your category, the honest test is whether you would notice a meaningful change in how AI represents you before it affected your pipeline. If your current cadence would catch a competitor displacing you, or a model starting to describe you wrongly, within a few weeks, it is fast enough. If a shift could quietly persist for a quarter before you saw it, you are auditing too slowly, and the fix is not more elaborate audits but more frequent, lighter ones on a fixed query set.
The Cost of Not Auditing
The risk is not just missing an opportunity; it is missing a regression. A model can start describing you inaccurately, drop you from a recommendation, or begin citing a competitor, and without a cadence you simply won’t know until it shows up in pipeline.
Start with monthly tracking this week and schedule the first quarterly audit ninety days out. That single habit puts you ahead of almost every competitor, who are still measuring rankings and wondering why the number no longer predicts their pipeline.
The discipline compounds in a quiet but valuable way: the longer you run a frozen query set, the more meaningful every future reading becomes, because you can compare against a real history rather than a hunch. A brand two years into consistent tracking can see exactly when a model update helped or hurt, which content pushes moved the number, and how its position has trended against named competitors. That historical depth is impossible to backfill later, which is the strongest argument for starting now even if your current cadence is imperfect. Begin measuring, keep the questions fixed, and let the record accumulate.