Zahir Hasan didn’t have to tell me his company’s numbers were wrong.
I’d sent Hasan, COO of the Oslo-based research firm Clovion AI, a list of methodology questions about “Surviving the AI Funnel,” Clovion’s new study of how Claude, ChatGPT, and Gemini recommend brands across a conversation. Question ten was routine, the kind of thing you ask every research team. The report says the three AI assistants flatly contradict each other on brand facts 15% of the time, based on 33 verified contradictions. Was 33 really enough to support a claim about which model tends to undersell a brand’s features and which tends to oversell them?
Hasan’s answer wasn’t a defense of the number. It was a correction. “The real number is 330,” he wrote back. “A designer dropped a zero in layout.” The same slipped decimal, he said, had also turned 2,040 brands into “204” on page seven of the PDF that I’d been sent in advance of its publication. A revised version is coming out this week. So, I got the corrected figures first.
That’s a strange way to start a column about an AI research report, admitting before anything else that the draft report had an error in it. But it’s the most honest way in, because the correction says something the study’s headline stats never could. Reading AI answers correctly, whether you’re a marketer trying to figure out if ChatGPT is recommending your product or a researcher building a study about it, comes down to catching the decimal point before you build a strategy on it.
The Funnel, Recapped
Set the typo aside for a moment and the underlying research holds up. Clovion ran 69,120 multi-turn conversations across the three assistants in 36 B2B software and fintech categories, asking an opening question like “best CRM tools?” and then a single realistic follow-up. Re-asking the same question kept 90% of the recommended list intact. Adding one ordinary buyer detail, something as plain as “for a small team,” kept only 28%. Sixty-two percent of the brands that made the first answer were gone by the second one.
I asked Hasan whether “small team” was cherry-picked to produce that drop. It wasn’t. His team also tested “for a large enterprise” and got almost identical churn, around 72% either way, against roughly 10% when the question was simply repeated. The list isn’t unstable. It’s responsive, and mostly to whether the model has decided who a brand is actually for.
That’s the part worth sitting with if you do SEO or brand strategy for a living. Being named in an AI answer is not the same thing as being trusted by it. A model that puts you in its first CRM list can still cut you the moment a buyer gets specific, and Clovion’s data says that happens most of the time, not some of the time.
The Correction Changes the Shape of the Smallest, Most-Cited Number
Here’s where the fixed decimal actually matters for how you should read this study. The old figure, 33 verified…
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