None of these counters are broken; each of them is doing precisely what it was built to do. But what happened is that the population- AI bots versus people- underneath them changed.
I can prove that in one channel, and reason about the rest. Here is the worked example, and what it means for the numbers you own.
THE MOMENT THAT CONTACT WAS A SCRIPT, THE METRIC IS DESCRIBING SOMETHING THAT DID NOT HAPPEN.
MY EXPERIENCE WITH AI SKEWING OUTCOMES
I am co-founder and CTO of a nearshore call center company and I build our AI quality systems. This started with an afternoon I had not planned to spend on it. I was checking that a page of our own research was being found, so I opened our web search report and sorted it by volume. It did not look like people. The largest single query on the property was a seven-word sentence: " belize healthcare outsourcing hipaa phipa alignment 2026." Below it sat near-identical sentences with the same clinical phrasing and year stamp, differing by a word or two.
The top four queries had between roughly 2,200 and 5,500 impressions each. The click column read zero all the way down.
Here’ s what really tipped me off that something was wrong. Nobody types the same seven-word sentence 5,000 times in a month.
Machine and bot traffic alone would not have been worth an afternoon; every website collects some. What bothered me was the shape. Real human demand has one: a head of short queries, a long thin tail, and clicks that roughly track position. This had none of it.
FIGURE 1
I used web search for the proof because of what it hands back to you. Most channels give you an event and nothing else: a session opened but a ticket did not appear. Web search gives you the exact string the visitor typed, which means you can sort the two populations apart instead of guessing at them.
So, I did what an engineer does with a dataset that offends him. I exported it and wrote code to take it apart.
The Split, Run Twice
For the 30 days to July 11, 2026, strings that no person types accounted for 39 % of the impressions that carried a visible query string, which is the only portion anyone can classify.
Then I reran the same code against the 30 days to August 1. It came back at 19.45 %.
Same property, nothing changed on my side, and the share halved.
FIGURE 2
METRICS
FIGURE 1 shows the two windows side by side, and the two populations sit apart on every measure that matters. Machine and bot queries averaged position 4.0 in the current window against 22.6 for the humans, and clicked at 0.04 % against 0.49 %.
Blend them, as every standard report does, and the property reports a 0.41 % click-through rate. In the earlier window the blended figure was 0.27 % against 0.44 % for humans alone.
Every input is real. But the output is fiction.
That volatility is why I will not hand you a correction factor, meaning a fixed multiplier you apply to a contaminated number to estimate the clean one.
Divide human-only by blended and mine was roughly 1.6 in July and roughly 1.2 six weeks later. Anyone selling you a fixed adjustment for bot traffic is selling a number with an expiry date.
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