How often to re-evaluate, and why more is not better
The systems change underneath the measurement, which argues for frequency. Real change is slow, which argues against it. The resolution is not a compromise.
The tension
AI systems update without notice, so a reading is a statement about a date. That pulls toward measuring often. But the signals that matter, corroboration especially, move over quarters rather than weeks, so frequent measurement mostly produces movement that is not progress.
The answer is not a middle number. It is that different parts of an AI visibility evaluation have genuinely different natural periods.
What changes on what timescale
Retrieval: immediately, and it can break overnight. A robots.txt edit, a security product update, a framework change. This is the one worth watching continuously, because it fails abruptly and is cheap to fix.
Comprehension: weeks. It changes when the business changes what it says, or when a source drifts out of agreement with the others.
Corroboration: quarters. Measuring it monthly produces eleven readings a year that say nothing and one that might.
Selection: continuously variable, and only meaningful in aggregate. It moves every time it is sampled. What matters is the trend across many samples, not any single reading.
The practical shape: retrieval monitored continuously, the full evaluation quarterly, and selection sampled on a schedule and read as a trend line rather than as an event. Anything more frequent than that is selling activity rather than information.
Why over-measuring causes harm
A number checked weekly gets reacted to weekly. Most of what it shows at that interval is noise, so the reactions are to noise, and the business ends up steering by a signal it cannot distinguish from randomness. Worse, real movement gets attributed to whatever was done most recently, which manufactures false confidence in tactics that did nothing.
The exception
Re-evaluate immediately after a structural change: a site migration, a rebrand, a domain move, a change of business name or address. Those are the moments when something genuinely breaks, and they are also the moments when nobody thinks to check, because everyone is focused on whether the site looks right.