You measure AI-written content the same way you measure any other content: you tag it, track it in your analytics, and compare its performance against a control group of pages you wrote yourself.
There is no special AI metric. The work is in the setup, not the reporting. If you skip the tagging step, every number you look at afterward will be meaningless because you will not know which pages were AI-assisted and which were not.
Step 1: Tag your pages
Before you publish anything, decide on one consistent label and apply it at the page level. In Google Analytics 4, the cleanest way is a custom dimension or a simple naming convention in your page URLs, such as a `/ai-assisted/` path segment. In a CMS like WordPress, a taxonomy term or a custom field works just as well. The point is that the label travels with the page forever, so six months from now you can still split your traffic reports by it. Pick the label once. Changing it halfway through destroys your comparison.
Step 2: Choose metrics that survive a small sample
Engagement rate and average engagement time are more stable than raw pageviews when you are comparing small groups of pages. Pageviews mostly reflect how much traffic you sent to a page, which is a distribution problem, not a content quality problem. Scroll depth and return-visitor rate are also useful.
According to our AI tool database, the site maintains an internal database of 360 AI tools, each with a pricing and capability snapshot recorded at verification time, most recently on 2026-09-18. That database is a directory of tools and their verified pricing and features. It does not contain performance benchmarks for AI-written content, and it cannot tell you whether your AI-assisted pages are doing well. Use it to check what a tool actually costs and does, not to benchmark your own results.
Step 3: Build the comparison honestly
Split your pages into two groups: AI-assisted and human-written. Compare them on the same metrics over the same period. The honest way to do this is to match pages by topic and by publish date, because a page about a trending news topic will outperform a page about a niche technical question regardless of who wrote it.
If you cannot match them, at least note the mismatch so you do not fool yourself later. A reasonable heuristic, and I want to be clear this is a working rule of thumb rather than a sourced standard, is to give any comparison enough pages per group and enough time that a single viral post does not swing the whole result.
Ten pages per group over a full quarter is a sensible starting point for a small site, but treat it as your own judgment call, not a benchmark anyone published.
What each metric tells you
Average engagement time tells you whether people are reading past the first paragraph. A high bounce rate with a long engagement time usually means the page answered the question and the reader left satisfied. A low bounce rate with a short engagement time usually means the page confused the reader and they clicked around looking for a better answer. Conversion rate, if you have one, tells you whether the content moved someone to act. None of these metrics tell you whether AI wrote the page. They tell you whether the page worked.
When this method fails
This approach breaks down when your AI-assisted pages are concentrated in one topic area and your human-written pages are concentrated in another. It also fails on very small sites, where a single page can dominate the average and make the comparison useless. And it cannot separate the effect of the writing from the effect of the publishing cadence.
If you published five AI-assisted pages a week and one human-written page a month, you are measuring frequency as much as quality. The honest fix is to slow down and match the cadence, or to accept that you are measuring a bundle of changes rather than one.